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    • Q1:MATH0003 A2 Investigation (30%) Assessment 2: Investigation – Disease Modelling Academic Integrity in Mathematics: all your work should be your own and it should be underpinned by integrity, which means to act ethically, honestly and with fairness. Although no referencing is required in your investigation and correct answers between students will be similar, there is NO exception to this rule in mathematics. This is an individual assessment, and at no point should you discuss your answers or working out with other students. This assessment is submitted via Turnitin and it is very obvious to Curtin university tutors who mark assessments when students have colluded (worked together), especially when answers are incorrect. Students who are found to have colluded in this assessment risk receiving a Curtin Academic Integrity notice. The below document will guide you through your investigation. All answers and working to the questions within should be entered on this document. You will be required to copy and paste graphs and tables from your Excel workings into this document. Taking a screenshot or snip may be easier than copy and pasting. For information on how to take a screenshot or snip of your screen on a Mac, click here. For information on how to take a screenshot or snip of your screen on a Windows device, click, here. Once complete, you must submit your saved Word file to the Turnitin point in the Assessments folder. Make sure you read the submission instructions and upload this document to the correct point. You will be required to work with Excel to complete this assessment, as instructed in this document. You must save all of your work on Excel and submit that file to the Blackboard submission point, below the Turnitin submission point in the assessments folder. You will receive grades and feedback directly on the Word document that you submit in Turnitin, tutors will refer to the Excel file, but will not leave grades or feedback there. Note: your assessment will not be considered submitted until BOTH the Word and Excel files have been received in the system. Non-submission of the Excel file will result in the entire assessment being treated as not submitted. Some points to note: Show as much working as possible for every question, marks are attached to your working, not just your final answer. Refer to the supporting resources in the Assessments folder for assistance with the Excel components of this assessment. Some questions have multiple parts. Make sure that you thoroughly read and answer all parts of each question. Notation: * represents multiplication ^ represents an exponent (eg x^4 represents x4 or x to the power of 4) “d.p” stands for decimal places For the following questions you will be required to analyse disease trends using the data provided in the Excel file titled “Assessment 2 – disease_modelling_statistics”. Introduction: Mathematical models can project how communicable (infectious) diseases progress to show the likely outcome of an epidemic (like COVID-19) and help inform public health interventions. The modelling can help decide which intervention(s) to avoid, which to trial or it can help predict future growth patterns. Scientists also study the incidence of non-communicable (non-transferrable) diseases such as cancer and heart disease over time and over different populations to assist with the analysis of risk factors which increase the chances of developing these diseases. In this assessment you will investigate the trends in two diseases over time, using mathematical principles learned to date. Complete the following questions using the data in the Excel spreadsheet titled “Assessment 2 – disease_modelling_statistics”. It is recommended that you attempt the Excel based questions after the completion of Topic 7 – Excel Basics. Question 1 23 MARKS AVAILABLE Coronary heart disease (CHD) is Australia’s leading cause of death. It is the term given to heart problems caused by narrowed heart arteries that supply blood to the heart muscle. When the blood flow to the heart muscle is completely blocked, the heart muscle cells die, which is termed a heart attack. Australian death rates have fallen substantially over recent decades. Advances in treatment, improving the level of care, and controlling the risk factors of death in CHD patients (such as smoking, hypertension, and overweight) are the most important reasons for the reduction in the mortality rate of this disease. For this question you will use the data in the “Australian CHD Data by Sex” tab of the supplied Excel Spreadsheet. It outlines the number of deaths (by sex*) in Australia due to CHD between 2000 and 2019 as recorded by the World Health Organisation (WHO). * WHO defines “sex” as the biological characteristics that define humans as female or male. Use the Pivot Table function in Excel to produce a cross tabulation which organises the data with the year variable down the rows and the sex variable across the columns. The cells in the table should contain the number of deaths of a given sex each year. Include column and row grand totals in your final table. Ensure your table contains an appropriate title and column/row headings. Take a snip or copy and paste this table below. [1.5 marks] For the following question you will first need to copy and paste the values from the pivot table created in 1a) into a new tab of your Excel spreadsheet. Be sure to paste using the ‘values only” option, not the formatting as well. Name this tab “CHD by sex & year” In this new tab, add an additional column in between the “year” and “sex” columns and title it “years since 2000 (t)”. Fill this column with appropriate values e.g. 2000 t = 0, 2001 t =1 etc. Using Excel and the table, create a line graph which represents the relationship between the variables time since 2000 and the number of deaths from CHD between 2000 and 2019 for both sexes. Your final graph should have two lines on one set of axes. Take a snip or copy and paste your graph below. Hints: Determine the dependent and independent variables and which axis they should be situated on. Ensure your graph contains all relevant information and features. [2.5 marks] What type of function does the number of CHD deaths data appear to be? [ 0.5 mark] Explain what features of your graph support your answer in part b)ii. [1 mark] Describe the trend you can see in CHD death rates over time. Compare the similarities and differences between the sexes. [1.5 marks] Use Excel to add a linear trendline & trendline equations to both the male and female lines. Take a snip of your graph with trendlines and paste below. In addition to pasting your graph into this document, type the trendline equations here: Male: Female: [1.5 mark] Given y = mx + c is the standard slope-intercept format of a linear equation: what is the m value in your trendline equation identified above: for the male trend line? [0.5 mark] for the female trendline? [0.5 mark] Explain what the m values represent in the context of this scenario? In your explanation interpret the differences between the male and female values. [1 mark] what is the c value in your trendline equation identified above: for the male trend line? [0.5 mark] for the female trendline? [0.5 mark] Explain what the c values represent in the context of this scenario? In your explanation interpret the differences between the male and female values. [1 mark] In Excel, create two pie charts which display the proportion of male vs female Australian CHD deaths in the year 2000 (pie chart 1) and in the year 2019 (pie chart 2). You should have one pie chart for each year. Take a snip of the two pie charts and paste below. [2 marks] Discuss the differences between the two pie charts in the context of deaths due to CHD by sex in 2000 and 2019. [1 mark] Was the difference observed in your pie charts in part f expected based on the differences in the trendlines for both male & females – yes or no? Justify your response by linking the differences in the pie charts to the male and female trendlines. [2 marks] Write one linear equation which can be used to predict the total number of deaths from CHD in Australia in any given year since 2000. Hint: total number of deaths = # male deaths + # female deaths [1.5 marks] Use your trendline from part g)i to predict the total number of people (male and female sex) who will die from CHD in the year 2030. [1.5 marks] Use your trendline from part g)i to predict the year the number of annual deaths due to CHD will have been reduced to 10,000 people. [2.5 marks] Question 2. 19 MARKS AVAILABLE When comparing mortality trends over time between nations, it is important to also take into consideration the population size at any given time. The mortality rate is often expressed as the number of deaths per 100,000 people. For the following questions you will refer to the data in the “CHD mortality nation comparison” tab in the Excel spreadsheet. Using skills covered in Topic 7 – Excel Basics, format the data in the table to achieve the following: Alter the column width appropriately Bold the column headings Add the filters to the column headings, so that data can be filtered if required Freeze the top row so you can scroll down and always have the column headings in view Take a snip or copy and paste the top section of your table below. [1.5 marks] Insert a column to the right of the number of deaths column and title “deaths/100,000”. Using the values in the “number of deaths” column and the “nation population” column, determine the equivalent death rate per 100,000 for each year for each nation. To do this, use an Excel formula to calculate the appropriate “deaths/100,000” value for one cell in this new column (to 2 decimal places). Apply this formula to all cells in this column. Once done, click on any value in this new column, take a snip of the first 10 rows with the highlighted cell and the formula bar with the underlying calculation. Paste the image below. [ 2 marks] Use the Pivot Table function in Excel to organise the raw data in a table which displays the number of deaths per year for each nation. Place the year variable in rows and the nation variable in columns. Ensure your table contains an appropriate title and column/row headings. You do not need to include grand totals. Take a snip or copy and paste this table below. [ 1.5 marks] Using the pivot table created in part c)i, create line graphs which represents the relationship between the year and the number of deaths from CHD between 2000 and 2019. Your final product should be one graph with 4 lines - one representing each nation. Take a snip or copy and paste this graph below. [2.5 marks] In which nations, has the number of deaths from CHD each year increased over time? [1 mark] In which nations, has the number of deaths from CHD each year decreased over time? [1 mark] Use the Pivot Table function in Excel to organise the raw data in a table which displays the deaths per 100,000 people, per year for each nation. Place the year variable in rows and the nation variable in columns. Ensure your table contains an appropriate title and column/row headings. You do not need to include grand totals. Take a snip or copy and paste this table below. [ 1.5 marks] Using the pivot table created in part d)i, create line graphs which represents the relationship between the year and the number of deaths/100,000 from CHD between 2000 and 2019. Your final product should be one graph with 4 lines - one representing each nation. Take a snip or copy and paste this graph below. [ 2.5 marks] Which nation has had the most stable CHD deaths/100,000 rate between 2000 & 2019? What feature of the graph explains your response. [1 mark] Use your pivot tables and/or graphs in part c) & d) to complete the empty cells in the table below. You only need to enter the names of the nations. You do not need to include values. For example, in the red cell below you should type the name of the nation which had the greatest number of deaths due to CHD in 2020. [2 marks] Country Comparison of Mortality due to CHD in 2000 & 2019 Rank (1=highest; 4=lowest) Total deaths in 2000 Total deaths in 2019 Deaths/100,000 in 2000 Deaths/100,000 in 2019 1 2 3 4 Compare and contrast both the number of deaths due to CHD and the deaths/100,000 rates between Brazil and Australia. In your response discuss the changes over time, the trends (growth/decline) and compare the values. [2.5 marks] Question 3. 18 MARKS AVAILABLE The global emergence of the coronavirus (COVID-19) marks a significant worldwide outbreak of an infectious disease caused by the severe acute respiratory syndrome SARS-CoV-2. Cases were first detected in China in December 2019, with the virus spreading rapidly to other countries across the world. This rapid transmission prompted the World Health Organization (WHO) to declare it a pandemic (worldwide spread of a new disease) in March 2020. For the following questions you will refer to the data in the “US COVID Cases 20_21” tab in the Excel spreadsheet. a) Using Excel, create a line graph which represents the relationship between the variables time (the number of weeks) and the number of COVID cases in the US between 19/1/2020 (case #1) and 24/1/2021. For the time variable, t, use values which represent the number of weeks since case #1. i.e. on 19/1/2020 t=0; on 26/1/2020 t = 1; on 2/2/2020 t = 2 etc. For the number of COVID cases variable, format the axis to display the units in “millions”. Take a snip or copy and paste your graph below. Hints: Add an additional column to your spreadsheet called “weeks since case #1” and fill with appropriate values e.g. 20/1/2020 t=0; on 26/1/2020 t = 1 etc. Determine the dependent and independent variables and which axis they should be situated on. Ensure your graph contains all relevant information and features. [2.5 marks] b) i. What type of function does the number of COVID-19 cases in the US over this period of time appear to be? [0.5 mark] Explain what features of your graph support your answer in part 3b)i. [1 marks] c) Add a fourth column to your table in Excel which indicates the “growth factor” from the week before. To do this, divide each week’s number of cases by the number of cases of the week before. This first three values have been completed for you. You must perform the calculations in Excel (not manually, using a calculator). Extend this formula for the entire table to calculate the growth factor to 5 d.p. Click on one of the cells in your growth factor column, take a screen shot showing your table (minimum of the first 10 lines) with the highlighted cell and the formula bar with the underlying calculation. Take a snip or copy and paste that screen shot below. Weeks since case #1 Number of Cases Growth Factor 0 1 1 4 4.00000 2 9 2.25 3 20 2.22222 4 24 1.2 [2 marks] Use Excel to calculate the average of the growth factor values without the outlier values included in the calculation. The outlier values you are to exclude in the calculation of the average, are the two largest growth factor values. What is the average factor (5 d.p.)? Click on the average value in Excel, take a snip of the average cell and the formula bar to show how you used an Excel formula to find this average. Paste the image below. [1 mark] The general form of an exponential equation is y=ka^t. Consider the table above of the number of COVID-19 cases in the US and the average growth factor in relation to the general form of an exponential equation as you answer these next questions: What does the “y” in the general form “y=ka^t” represent in the context of the relationship between the number of COVID-19 cases and weeks since case #1? [1 mark] What does the “k” in the general form “y=ka^t” represent in the context of the relationship between the number of COVID-19 cases and weeks since case #1? Give its value and explain. [1.5 marks] What does the “a” in the general form “y=ka^t” represent in the context of the relationship between the number of COVID-19 cases and weeks since case #1? Give its value and explain. [1.5 marks] What does the “t” in the general form “y=ka^t” represent in the context of the relationship between the number of COVID-19 cases and weeks since case #1? Explain. [1 marks] Use your answers from parts d) i-iv to construct an exponential equation describing the relationship the number of COVID-19 cases and weeks since case #1. [1 mark] e) Use your formula from part Q3d)v to predict the number of COVID-19 cases in the US on 31/1/2021. Show all working. [2 marks] The exponential nature of communicable diseases like COVID-19, not only applies to the nature in which the disease can spread, but also in how the number of cases can reduce. Every case of COVID-19 that is prevented cuts off transmission chains, which prevents many more cases down the line. That means the same precautions that reduce transmission enough to cause a big drop in case numbers when cases are high, translate into a smaller decline when cases are low. Those changes add up over time. For example, reducing 1,000 cases by half each day would mean a reduction of 500 cases on Day 1, a reduction of 250 cases on Day 2, 125 cases on Day 3 but only 31 cases on Day 5. Below is a graph which demonstrates this. It displays the number of COVID-19 cases in the US between December 2020 and February 2022 and the following questions will refer to it. Dec 20 Jun 21 Jul 21 Aug21 Sept21 Oct 21 Nov 21 Dec 21 Feb 21 Feb 22 Jan 22 May 21 Jan 21 Mar 21 Apr 21 TOTAL COVID-19 CASES - USA f) i. Approximately what month/year recorded the greatest number of COVID-19 cases? [0.5 mark] ii. Approximately what month/year recorded the least number of COVID-19 cases? [0.5 mark] iii. Identify one period in which exponential growth of COVID-19 cases occurred & identify one period in which exponential decay of COVID-19 cases occurred. Don’t just give a month, provide the range of time in which growth/decay has been observed. Make it clear in your response which period of time represents growth and which represents decay. [1 mark] iv. What period experienced the greatest rate of growth? Don’t just give a month, provide the range of time in which the greatest rate of growth has been observed. [0.5 mark] v. What period were cases the most stable? Don’t just give a month, provide the range of time in which cases were stable. [0.5 mark] End of Assignment World Health Organization (2023) WHO COVID-19 Dashboard – United States of America. https://covid19.who.int/region/amro/country/us World Health Organization (2023) WHO Mortality Database - Ischaemic Heart Disease https://platform.who.int/mortality Total /60 Total /30 Late Penalty __ days late = __ % deduction = ____ mark deduction Final score /30See Answer
    • Q2:Module 6: Homework Assignment 30 pts. Respond to the prompt(s) for each item below using complete sentences and clear writing. You may single-space your work. A cover page or page numbers are not required. Cite and reference only where indicated unless you utilize outside sources in a response. Please do item 1,2,3 only Return to the assignment page when you are ready to submit your assignment. Collaboration on this assignment is NOT allowed. Your work must be your own. Item 1 (2 pts.) What do p-values and effect sizes tell us about the significance of our results? Be sure to comment on any similarities or differences between the two. Item 2 (5 pts.) Explain the relationships between ... A. Sample size and Confidence intervals (1 pt.) B. Power and Effect size (1 pt.) C. Sample size and Power (1 pt.) D. Power and Type II Error (1 pt.) E. Alpha and Power (1 pt.) Item 3 (7 pts.) Use the following scenario to respond to parts A - D. Nancy compares the effectiveness of two forms of psychotherapy against a control group (no therapy) for obsessive compulsive disorder. The IV and DV are listed below: IV = Therapy Condition (Therapy A, Therapy B, No Therapy) DV = Effectiveness Score (pre-to-post reduction in symptoms) Nancy sets the hypotheses as follows: H0 = There are no differences among the mean effectiveness scores of groups. Ha = There are statistically significant differences among the mean effectiveness scores of groups. A. Explain what it would mean in this scenario if Nancy were to commit a Type I error. (2 pts.) B. Explain what it would mean in this scenario if Nancy were to commit a Type II error. (2 pts.) C. If the results of the data analysis support the alternative hypothesis, what was Nancy's decision about the null hypothesis? (1 pt.) D. If the results of the data analysis support the alternative hypothesis, is the following statement appropriate? Explain. (2 pts.) Therapy A is more effective than Therapy B which is more effective than having no treatment. USE THE FOLLOWING SCENARIO TO ANSWER ITEMS 4 AND 5: Jack wants to investigate the mean differences between middle-aged adults and older adults on a quantitative measure of life satisfaction (0 - 7 scale). Jack expects that life satisfaction will differ between the age groups. Item 4 (6 pts.) A. What is the more appropriate statistical test for him to use and why? (2 pts) B. Explain power within the context of this study. Be sure to reference null and alternative hypotheses in your explanation. (4 pts.) Item 5 (10 pts.) After running the appropriate statistical tests, Jack finds that life satisfaction was in the older adults (M = 4.9, SD = 1.2) than in middle-aged adults (M = 4.1, SD = 1.8), t(58) = 2.03, p = . 047, 95% CI [0.01, 1.59], d = . 52. A. From these statistical results, what conclusions can Jack make? Comment on whether there are statistically significant differences between the groups and how you know. (2 pts) B. What should Jack decide about the hypotheses? State the decision and explain what the decision means in the context of this study. (2 pts.) C. What does the "t(58) = 2.03" tell us? (2 pts.) D. What can we conclude from the confidence interval within the context of this study? (2 pts) E. What does the effect size tell us within the context of this study? (2 pts)/nModule 6: Homework Assignment 30 pts. Respond to the prompt(s) for each item below using complete sentences and clear writing. You may single-space your work. A cover page or page numbers are not required. Cite and reference only where indicated unless you utilize outside sources in a response. The weekly homework assignment requires very thorough and detailed answers. These are not 1-2 sentence responses. Please take your time and be sure to elaborate on all your answers. Min word count:500 APA format mandatory Return to the assignment page when you are ready to submit your assignment. Collaboration on this assignment is NOT allowed. Your work must be your own. Item 1 Item 2 Item 3See Answer
    • Q3:Students are required to select a research article or an academic project paper of research interests. Read the selected article/paper and provide the summary of the reading. A good paper summary should include the following components: 1. An overview of the article at the beginning, including the title and author names. 2. Describe the main topic, background/introduction of the paper, condense the main points in your own words. 3. State the research aims and/or hypothesis. Usually, the research aims are described at the last part of the background/introduction or at the beginning of the methods section. Research aims will run through the entire research article. 4. Summarize the study design and research methods. For example: describe the study method (randomized clinical trial, prospective cohort study or case-control study, etc); participants recruited in the study; inclusion and exclusion criteria; sample size; explain how measurements were made and what calculations were performed; what statistical tests were done. 5. State the most important results in details. Are the findings supported by previous research? What are the limitations of the study? 6. One or two sentences' summary of the conclusion. The summary should be about within 2 pages (double space with a 12-font size). Instructions: Need to answer all the questions above Plagiarism free Solutions generated from any AI platform is strictly Prohibited Solution to be formatted in APA and use appropriate references with in-text citations/nPatient Education and Counseling 86 (2012) 233-238 Contents lists available at ScienceDirect Patient Education and Counseling Patient Education and Counseling journal homepage: www.elsevier.com/locate/pateducou Intervention Relationship between improvements in heart failure patient disease specific knowledge and clinical events as part of a randomized controlled trial Naga V.A. Kommuria, Monica L. Johnson b, Todd M. Koelling b,* ª Department of Internal Medicine, Wayne State University, Detroit, USA b Department of Internal Medicine, University of Michigan, Ann Arbor, USA ARTICLE INFO Article history: Received 1 December 2010 Received in revised form 2 May 2011 Accepted 9 May 2011 Keywords: Heart failure Patient education Hospitalization Knowledge Outcomes ABSTRACT Objective: To examine the changes in performance on heart failure knowledge assessments administered before and after discharge education. Methods: We conducted a randomized controlled trial comparing the effects of a 1-h, one-on-one teaching session with a nurse educator to the standard discharge process in patients with systolic heart failure. Patients completed a 30 point heart failure knowledge questionnaire (HFKQ) prior to and 3 months after the education intervention. Results: Patients randomized to the nurse education intervention (n = 113) demonstrated significantly higher total HFKQ score increases compared to patients receiving the standard discharge process (n = 114) (median, IQR 1, 0 to 4 vs 0, -2 to 2, p = 0.007). Patients experiencing death or rehospitalization in the subsequent 6 months were found to have significantly lower HFKQ scores (10, 7 to 12 vs 11, 8 to 13, p = 0.002) compared to patients without a clinical event. Conclusion: Heart failure nurse education at the time of hospital discharge results in improved patient knowledge and reduced risk of readmission. Practice implications: Health care personnel should encourage education sessions for heart failure patients. Resources possibly need to be allocated for nurse led education sessions in heart failure patients as it improves outcomes and knowledge. @ 2011 Elsevier Ireland Ltd. All rights reserved. 1. Introduction Patient education has been recognized to play a critical role in the care of patients with heart failure [1,2]. Multiple studies have evaluated the effects of heart failure patient discharge education combined with various post-discharge support programs [3-16]. These studies have demonstrated improved clinical outcomes, reductions in hospitalizations, and reduced costs compared to control groups. Whether these benefits are due to the additional disease specific education, the post- discharge support programs, or a combination of the two is difficult to understand. Furthermore, because these prior studies have not assessed heart failure patient knowledge concurrently with the educational interventions, establishing a relationship between improvements in knowledge and improvements in clinical outcomes has not been possible. Recently, we demonstrated that heart failure patient educa- tion delivered face-to-face by a nurse educator at the time of hospital discharge leads to lower rates of hospital readmission compared to patients receiving written materials as part of the standard discharge process [7]. Additionally, patients random- ized to receive the nurse educator delivered intervention were more likely to be following appropriate self-care behaviors [7]. Whether heart failure patient education leads to measurable changes in disease specific knowledge is not known. Further- more, it is not known whether potential improvements in heart failure patient knowledge are associated with clinical outcomes and/or self-care behaviors. If a significant relationship exists between improvements in heart failure knowledge and clinical outcomes, then it may be possible to improve the efficiency of educational efforts by identifying patients most likely to benefit from the intervention. To improve this understanding, we have examined changes in performance on heart failure knowledge assessments administered before and after discharge education administered as part of a randomized controlled trial. * Corresponding author at: Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 E. Medical Center Drive, CVC Room 2167, SPC 5853, Ann Arbor, MI 48109, USA. Tel .: +1 734 764 7440; fax: +1 734 615 3326. E-mail address: tkoellin@umich.edu (T.M. Koelling). 0738-3991/$ - see front matter @ 2011 Elsevier Ireland Ltd. All rights reserved. doi: 10.1016/j.pec.2011.05.019 Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved. ELSEVIER 234 N.V.A. Kommuri et al./ Patient Education and Counseling 86 (2012) 233-238 2. Methods 2.1. Subjects This study was performed at the University of Michigan Hospital and was approved by the Medical School Institutional Review Board. Study subjects were recruited from eligible candidates admitted to the hospital with a diagnosis of heart failure (International Classification of Diseases, 9th ed. [ICD9-CM] 402.1, 402.11, 402.91, 404.01, 404.91, or 428.x) and documented left ventricular systolic dysfunction (ejection fraction ≤ 0.40). For the current study, a total of 667 subjects were screened for enrollment into the study during the recruitment period. Of those screened 402 were excluded from enrollment and the remaining 265 patients were included in the study (Fig. 1). At the time of study enrollment, information regarding compliance with diet and medication was extracted from the medical records. The study coordinator reviewed the admission note, medical records and problem summary list for evidence of non compliance. 2.2. Study design Details about the patient recruitment, randomization process, education intervention and patient follow up are described in our previous papers [7,17]. Briefly, after obtaining informed consent, patients were randomized to receive usual care (standard discharge information) or usual care plus a 1-h long nurse educator delivered heart failure education program. The education session covered details about the basic principles of heart failure, role of dietary sodium, importance of limitation of fluid intake as well as the mechanisms of diuretics and the rationale for other pharmacotherapy. Specific instructions were given to limit the dietary sodium intake to 2000 mg or less and the daily fluid intake to 2000 ml or less. Furthermore, during the session patients were given information about the importance of daily weight monitor- ing, self care behaviors, compliance of medications, smoking cessation, avoiding non steroidal anti inflammatory drugs, limitation of alcohol intake and the measures to take when the symptoms are worse. The intervention group also received heart failure management guidelines written in layman's terms. 2.3. Heart failure knowledge questionnaire The HFKQ was developed by the study team and has not been previously described. The HFKQ was administered to the study population at baseline and 3 months after hospital discharge by the study coordinator. The specific questions included in the HFKQ may be reviewed in the Appendix. The HFKQ contains 30 questions No. Assessed for eligibility: 667 No. Excluded: 402 Refused to participate: 29 Other reasons: 373 Cardiac surgery workup: 80 Noncardiac life threatening illness: 62 Transplant evaluation: 48 Dementia/Psychiatric illness: 46 Unable to follow up by phone: 27 1 Creatinine > 3.5 mg/dl: 24 Long term care facility: 23 Aortic stenosis: 22 Discharged before enrollment: 18 Other research study: 8 Active infection: 8 Acute MI: 7 > No. Consented for study: 265 > No. Enrolled Control Group: 137 No. Enrolled Education Group: 128 No. Not tested in Control Group: 23 No. Not tested in Education Group: 15 Deceased prior to 3 mo FU: 11 Hospitalized at 3 mo FU: 7 Deceased prior to 3 mo FU: 6 Hospitalized at 3 mo FU: 5 Declined questionnaire: 4 < Declined questionnaire: 5 > No. Included at 3 month HFKQ No. Included at 3 month HFKQ testing in Control Group: 114 testing in Education Group: 113 Fig. 1. Patient screening and randomization assignment profile. Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved. 235 N.V.A. Kommuri et al./ Patient Education and Counseling 86 (2012) 233-238 in total, and requires approximately 10 min to complete. The first 15 questions of the HFKQ assess general HF disease knowledge, self-care management behaviors, and medications used in the treatment of heart failure with left ventricular systolic dysfunction. For the purposes of this paper, these questions have been designated as the disease management knowledge (DMK) ques- tions. The remaining 15 questions of the HFKQ assess patient knowledge of dietary recommendations in heart failure and specific sodium content of common foods. These questions have been designated as the dietary and sodium knowledge (DSK) questions. Scoring of the questionnaire assigned a value of 1 point for each correct answer. In addition to the HFKQ testing, enrolled patients completed a questionnaire regarding self-care practices at the time of enroll- ment, 30, 90 and 180 days after hospital discharge. These self-care practices entailed self reported adherence to daily weight monitoring, dietary sodium restriction, fluid restriction, avoidance of smoking tobacco products, having a plan for what to do for worsened symptoms and performance of exercise ≥3 times per week. In this study, we performed a comparison of the 3 month follow-up HFKQ scores with respect to compliance to each of the self-care measures. 2.4. Statistical analysis The primary endpoint of the study was the change in HFKQ score from baseline to the 3 month time point after hospital discharge. Changes in HFKQ scores for the patients randomized to receive nurse education (NE) and controls were compared using the non-parametric Mann-Whitney U test. Changes in HFKQ scores were also compared for patients with or without a clinical event during the 180 day follow-up period. Clinical events were defined as death or hospitalization within the 180 day follow up period. We performed a stepwise multivariable logistic regression analysis to understand the contributors for improvement in heart failure knowledge. Variables entered into the model included age greater than 70 years, gender, white race, duration of disease less than 3 months, no insurance, Medicaid insurance, Medicare insurance, adjusted gross income less than $45,000/year, educa- tion level achieved less than high school diploma and study group assignment. Forward stepwise regression was used to identify independent predictors of improved total HFKQ score. An additional multivariable regression was performed in the interest of understanding the mechanism of improved clinical outcomes in our study, using the technique of mediator analysis suggested by Mackinnon et al. [18] In this analysis, the clinical event, death or rehospitalization within 6 months was the dependent variable while the patient treatment assignment and total HFKQ score at 3 month follow up were entered as covariates. All analyses were performed with SPSS version 17.0 statistical software. A p value of <0.05 was considered statistically significant. Unless otherwise specified, data are expressed as median and interquartile range. 3. Results The comparisons of baseline characteristics for the control and education groups are shown in Table 1 and demonstrate that the two groups were similar with respect to age, sex, socioeconomic status, left ventricular ejection fraction, NYHA classification, clinical comorbidities and laboratory values. Patients in the education group were found to have higher blood pressure than controls. Table 2 shows that baseline knowledge was similar for the two groups. Patients in control group did not demonstrate any improvement in their knowledge (DSK, DMK, total score) at the 3 month follow-up. However, patients in the education group demonstrated a significant improvement in their DMK (<0.001) as well as in the total score (<0.001) during follow up. Neither the control group nor the education group demonstrated improve- ments in their DSK during the study (Table 2). Table 1 Baseline characteristics of study population. Control Education n=128 p value n=137 Age (years) 67 (57, 74) 67 (55, 76) 0.82 Female % 39 39 0.82 White % Education (% not finishing high school) Income ($ in thousands) Uninsured % 76 80 0.46 30 23 0.24 47.4 (37.9, 59.6) 44.8 (36.7, 59.1) 0.26 2 4 0.42 Medicare % Medicaid % Private insurance % 38 30 0.16 0.65 0.16 16 14 44 52 Lives alone % Daughters Left ventricular ejection fraction 24 1 (0, 2) 27 1 (1, 2) 0.64 0.31 0.45 0.93 26.5 (20, 35) 25 (20, 35) NYHA (New York Heart Association) 3 (3, 4) 3 (3, 4) Number of hospitalizations in prior 12 mo 2 (1, 4) 2 (1, 3) Coronary artery disease % Atrial fibrillation % Implantable cardiac defibrillator % Diabetes mellitus % Chronic obstructive pulmonary disease % Non compliance with diet % Current smoking % Heart rate supine (bpm) Mean arterial blood pressure (mm Hg) 66 32 65 43 0.22 0.88 0.13 0.56 20 23 43 26 34 0.11 0.49 23 18 12 15 81 (69, 96) 0.15 0.77 0.42 14 84 (71, 96) 90 (76, 100) 96 (83, 107) 0.02 Admission weight (pounds) 185 (155, 226) 194 (162, 220) 0.78 QRS interval (ms) Hemoglobin (g/dl) Serum sodium Blood urea nitrogen Serum creatinine 117 (92, 147) 12.3 (10.8, 13.7) 127 (98, 161) 0.16 12.4 (11.1, 13.7) 0.54 138 (135, 141) 140 (137, 141) 0.11 28 (19, 43) 25 (18, 37) 1.3 (1.0, 1.7) 0.14 1.4 (1.0, 1.9) 0.27 Data are expressed as median and interquartile range, or percentages, as appropriate. Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved. 236 N.V.A. Kommuri et al./Patient Education and Counseling 86 (2012) 233-238 Table 2 Paired comparisons of knowledge in study population. Knowledge Control Education p value Baseline n=137 3 month follow up (p') n=114 Baseline n=128 3 month follow up (p ) n=113 DSK score 5 (4, 6) 4 (2, 5) 5.5 (4, 6) (0.39) 4 (2, 5) (0.32) 9 (7, 12) (0.23) 6 (4, 7) 4 (3, 5) 6 (4, 7) (0.57) 0.32 5 (4,7) (<0.001) 0.55 DMK score Total score 9 (7, 11) 9 (7, 11) 11 (8.5, 13) (<0.001) 0.33 Data are expressed as median and interquartile range. Abbreviations: DSK - dietary sodium knowledge; DMK - disease management knowledge. * Comparison between baseline and 3 month follow up period. + Comparison between baseline for Control and Education groups. Table 3 Change in knowledge at 3 month follow-up in the study population. Control n=114 Education n=113 p value DSK score 0 (-1, 2) 0 (-1, 2) 0.87 DMK score 0(-1,1) 1 (0, 3) <0.001 Total score 0 (-2, 2) 1 (0, 4) 0.007 Data are expressed as median and interquartile range. Table 4 Association between Heart Failure Knowledge Questionnaire scores and clinical outcomes. No event Event p Baseline HFKQ n=119 n= 146 DSK score 6 (4, 7) 5 (4, 6) 0.352 DMK score 4 (2, 5) 3 (2, 5) 0.136 Total score 9 (7, 11) 9 (7, 11) 0.131 3 month follow up HFKQ DSK score n=110 n=117 6 (4, 7) 5 (4, 6) 0.068 DMK score 5 (3, 7) 11 (8, 13) 4 (3, 5.5) 10 (7, 12) n=117 0.001 Total score 0.002 Changes in HFKQ compared to baseline DSK score n=110 0 (-2, 2) 0(-1, 1) 0.416 DMK score 1 (0, 3) 1 (-1, 2) 0.045 Total score 1 (-2, 4) 1 (-1, 2) 0.072 Data are expressed as median and interquartile range. Patients randomized to education had significantly higher total HFKQ score increases at 3 months compared to patients receiving standard discharge information (1, 0 to 4 vs 0, -2 to 2, p = 0.007) as shown in Table 3. Specific performance on the subset of questions regarding DMK also improved with nurse education (1, 0 to 3 vs 0, -1 to 1, p < 0.001). No significant changes in DSK were seen with nurse education (0, -1 to 2 vs 0, -1 to 2, p = 0.87). Multivariate linear regression showed that patient group assignment to nurse education (beta 0.871, OR 2.39, 95% CI 1.33-4.31, p = 0.004) and duration of heart failure less than 3 months (beta 1.045, OR 2.84, 95% CI 1.41-5.73, p = 0.003) were found to be independent predictors of improved heart failure knowledge scores. Mediator analysis to understand the relationship between the achieved disease specific knowledge and the clinical outcome while controlling for the patient treatment assignment showed that the total HFKQ score at the 3 month time period was strongly associated with the clinical outcome at 180 days. The total HFKQ score at 3 months was inversely associated with clinical events, where beta = - 0.110, OR 0.896 (95% CI 0.827-0.970), p = 0.007. Of the 265 enrolled patients, 146 had clinical events during the 6 month follow-up. 136 patients were readmitted to the hospital, and 25 patients died during the 6 month follow up period. Twenty- three patients in the control group and 15 patients in the education group did not complete the 3 month follow-up HFKQ either due to death or refusal to complete the questionnaire. Patients experienc- ing clinical events were found to have similar baseline HFKQ scores when compared to patients without any clinical events (Table 4). Patients experiencing a clinical event were found to have a significantly lower total HFKQ scores (10, 7 to 12 vs 11, 8 to 13, p = 0.002) and DMK scores (4, 3 to 5.5 vs 5, 3 to 7, p = 0.001) at the 3 month follow-up examination. DSK scores at 3 months showed a nonsignificant trend to being higher for the event free patients (6, 4 to 7 vs 5, 4 to 6, p = 0.068). Furthermore, changes in the DMK score (from baseline to 3 month follow-up), but not the DSK score, were associated with clinical events (1, 0 to 3 vs 1, -1 to 2, p = 0.045). Changes in the total HFKQ score were higher in the patients without clinical events, but this difference did not reach statistical significance (p = 0.072). The associations between total HFKQ scores and changes in HFKQ scores at 3 month follow-up and self reported compliance with heart failure self care measures are shown in Table 5. Patients reporting adherence to recommendations for daily weight monitoring, following a specific dietary sodium restriction and fluid restriction, and reporting a plan for what to do if their Table 5 Association between Heart Failure Knowledge Questionnaire scores and self care measures. Compliant Not compliant p 3 month follow up HFKQ Daily weight monitoring Following sodium restriction Following fluid restriction 11 (8, 13), 133 9 (7, 11), 93 <0.001 13 (11, 14.5), 53 9 (7, 11), 173 <0.001 11 (8, 13.5), 97 9 (7, 12), 129 <0.001 Not smoking 10 (8, 12), 211 10 (7, 12), 15 0.394 Reports plan for worsened symptoms Performing exercise ≥3 times per week 10 (8, 13), 173 9 (7, 11), 53 0.009 10 (8, 13), 87 10 (7, 12), 139 0.082 Changes in HFKQ compared to baseline Daily weight monitoring 1 (-1, 4), 133 0 (-2, 2), 93 0.013 Following sodium restriction Following fluid restriction Not smoking 2 (1, 5), 53 0 (-2, 3), 173 0.015 2 (0, 4), 97 0 (-2, 2.5), 129 0.003 1 (-1, 3), 211 0 (-2, 2), 15 0.460 Reports plan for worsened symptoms 1 (-1, 3.5), 173 0 (-2, 2), 53 0.037 Performing exercise ≥3 times per week 1 (-1, 3), 87 1 (-2, 3), 139 0.34 Data are expressed as median (interquartile range), and n for group. Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved. 237 N.V.A. Kommuri et al./ Patient Education and Counseling 86 (2012) 233-238 symptoms worsened demonstrated significantly higher scores on HFKQ testing. Similarly, patients following these measures were more likely to have increases in their HFKQ scores at 3 months compared to baseline. At 3 month follow-up, HFKQ scores and changes compared to baseline did not differ in patients based on their adherence to smoking and exercise self care measures. 4. Discussion and conclusion 4.1. Discussion Findings from this study show that face to face nurse education significantly improves disease specific knowledge compared to the standard discharge process. Specifically, this patient targeted education program improved heart failure patient overall disease specific knowledge, and disease management knowledge, but did not significantly increase dietary sodium knowledge. This study also demonstrates that heart failure disease specific knowledge is associated with clinical outcomes in patients with heart failure, as patients without clinical events had higher increases in heart failure knowledge scores compared to patients experiencing clinical events in the 6 month follow up period. Patients randomized to receive the education intervention were found to be more than 2- fold more likely to have improved HFKQ scores at three months compared to controls receiving the standard discharge process. Multiple previous studies demonstrated that multidisciplinary discharge education with post discharge follow up improves clinical outcomes [3-12]. Our previous publication first described the independent effects of nurse education on clinical outcomes in patients with systolic heart failure, demonstrating a 35% reduction in rehospitalization in patients randomized to the nurse education intervention [7]. Prior studies have also demonstrated that there is a wide gap between the perceived learning needs of the patients when compared to health care professionals [19-21]. While changes in heart failure knowledge have been reported with disease specific interventions by others [22-24], no studies have assessed the association between knowledge acquisition and subsequent self-care behavior [24]. Furthermore, none of these previous studies have shown that a measurable improvement in the knowledge is associated with clinical outcomes. Findings from our study showed that heart failure knowledge is strongly associated with self reported adherence to heart failure self care measures. Patients reporting adherence with daily weight monitoring, dietary sodium and fluid restriction, and having a plan for what to do if their symptoms worsened had significantly higher HFKQ scores and higher improvement in HFKQ scores compared to patients reporting that they were not adherent to these recom- mendations. This observation supports the connections between the education intervention, the improvement in disease specific knowledge and the desired self care behaviors. It is plausible that these improved behaviors accounted for the better clinical outcomes demonstrated in the education group. Patients who retain educational principles conveyed by the nurse educator are more likely to practice appropriate self-care behaviors and alter their clinical course. Ni et al. previously demonstrated a trend toward an association between poor adherence to self care measures and lack of knowledge about self care. [25] This study did not assess the relationship between self care knowledge and clinical outcomes. Although we were able to demonstrate improvements in overall heart failure knowledge using the education intervention, we were not able to show improvements in dietary knowledge. This possibly points out a significant weakness in our educational program. The HFKQ was written for the purpose of assessing patient disease specific and dietary specific knowledge before and after the education intervention. The questionnaire has not been previously studied and attributes such as reliability and validity cannot be quoted. Previous studies demonstrated that specific education delivered by a registered dietician aimed at teaching skills for reading Nutritional Facts labels and sorting foods into high and low sodium groups led to clear improvements in patient's sodium knowledge [26]. Whether our education intervention would lead to improved ability to sort foods based on sodium content is unknown. Further refinements in our education program may need to include specific attention to Nutritional Facts label interpretation. Never- theless, while patients randomized to receive the education intervention did not improve their performance on the dietary sodium questions of the HFKQ, they reported clear improvement in adherence to dietary self care measures. Previous investigators have described the potential challenges surrounding the education of patients with chronic illness, including heart failure [22]. Medications, blood flow and prior central nervous system insults may directly determine the cognitive abilities of patients presenting with heart failure. These issues, combined with the heterogeneity of educational level, vocabulary, prior knowledge, and reading/oral literacy, strongly influence the success of educational efforts in the heart failure population. Others have shown that despite concerted efforts to provide medication information, patients may not necessarily retain the information on the first try [27]. This had led many to suggest that education for chronic illnesses should be performed over multiple interactions [28], rather than just one, as was the case in this study. 4.2. Conclusion This study demonstrates that a single face to face, 1 h long, educational session with a nurse educator leads to improved heart failure specific knowledge in patients hospitalized with heart failure and left ventricular systolic dysfunction. The knowledge that patients retained from the education session was apparent on testing at the 3 month follow up period, and this was strongly associated with appropriate self care behaviors. Furthermore, we have shown that patients with high heart failure knowledge scores are less likely to be readmitted to the hospital over a 6 month follow up period than patients with low scores. 4.3. Practice implications Nurse led education sessions are effective in improving disease specific knowledge as well as clinical outcomes in heart failure patients with systolic dysfunction. Our study highlights the need for funding of educational sessions to reduce the cost burden of this common chronic illness. Funding sources Quality Care Research Fund from the Academic Medicine and Managed Care Forum. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi: 10.1016/j.pec.2011.05.019. References [1] Adams KF, Lindenfeld J, Arnold JMO, Baker DW, Barnard DH, Baughman KL, et al. Executive summary: HFSA 2006 Comprehensive Heart Failure Practice Guideline. J Card Fail 2006;12:10-38. [2] Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. 2009 focused update incorporated into the ACC/AHA 2005 Guidelines for the Diagnosis and Management of Heart Failure in Adults. Circulation 2009;119: e391-479. Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved. 238 N.V.A. Kommuri et al./Patient Education and Counseling 86 (2012) 233-238 [3] Cline CM, Israelsson BY, Willenheimer RB, Broms K, Erhardt LR. Cost effective management programme for heart failure reduces hospitalisation. Heart 1998;80:442-6. [4] Gwadry-Sridhar FH, Flintoft V, Lee DS, Lee H, Guyatt GH. A systematic review and meta-analysis of studies comparing readmission rates and mortality rates in patients with heart failure. Arch Intern Med 2004;164:2315-20. [5] Hanumanthu S, Butler J, Chomsky D, Davis S, Wilson JR. Effect of a heart failure program on hospitalization frequency and exercise tolerance. Circulation 1997;96:2842-8. [6] Holland R, Battersby J, Harvey I, Lenaghan E, Smith J, Hay L. Systematic review of multidisciplinary interventions in heart failure. Heart 2005;91:899-906. [7] Koelling TM, Johnson ML, Cody RJ, Aaronson KD. Discharge education improves clinical outcomes in patients with chronic heart failure. Circulation 2005;111:179-85. [8] Krumholz HM, Amatruda J, Smith GL, Mattera JA, Roumanis SA, Radford MJ, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol 2002;39:83-9. [9] McAlister FA, Stewart S, Ferrua S, McMurray JJ. Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. J Am Coll Cardiol 2004;44:810-9. [10] McMurray JJ, Stewart S. Nurse led, multidisciplinary intervention in chronic heart failure. Heart 1998;80:430-1. [11] Phillips CO, Singa RM, Rubin HR, Jaarsma T. Complexity of program and clinical outcomes of heart failure disease management incorporating specialist nurse- led heart failure clinics. A meta-regression analysis. Eur J Heart Fail 2005;7:333-41. [12] Rich MW, Beckham V, Wittenberg C, Leven CL, Freedland KE, Carney RM. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. New Engl J Med 1995;333:1190-5. [13] Clark RA, Inglis SC, McAlister FA, Cleland JG, Stewart S. Telemonitoring or structured telephone support programmes for patients with chronic heart failure: systematic review and meta-analysis. Brit Med J 2007;334:942. [14] Harrison MB, Browne GB, Roberts J, Tugwell P, Gafni A, Graham ID. Quality of life of individuals with heart failure: a randomized trial of the effectiveness of two models of hospital-to-home transition. Med Care 2002;40:271-82. [15] Klersy C, De Silvestri A, Gabutti G, Regoli F, Auricchio A. A meta-analysis of remote monitoring of heart failure patients. J Am Coll Cardiol 2009;54:1683-94. [16] Kozak AT, Rucker-Whitaker C, Basu S, Mendes de Leon CF, Calvin JE, Grady KL, et al. Elements of nonpharmacologic interventions that prevent progression of heart failure: a meta-analysis. Congest Heart Fail 2007;13:280-7. [17] Matthews JC, Johnson ML, Koelling TM. The impact of patient-specific quality- of-care report cards on guideline adherence in heart failure. Am Heart J 2007;154:1174-83. [18] Mackinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods 2002;7:83-104. [19] Frattini E, Lindsay P, Kerr E, Park YJ. Learning needs of congestive heart failure patients. Prog Cardiovasc Nurs 1998;13:11-6. [20] Luniewski M, Reigle J, White B. Card sort: an assessment tool for the educa- tional needs of patients with heart failure. Am J Crit Care 1999;8:297-302. [21] Wehby D, Brenner PS. Perceived learning needs of patients with heart failure. Heart Lung 1999;28:31-40. [22] Baker DW, Asch SM, Keesey JW, Brown JA, Chan KS, Joyce G, et al. Differences in education, knowledge, self-management activities, and health outcomes for patients with heart failure cared for under the chronic disease model: the improving chronic illness care evaluation. J Card Fail 2005;11:405-13. [23] DeWalt DA, Malone RM, Bryant ME, Kosnar MC, Corr KE, Rothman RL, et al. A heart failure self-management program for patients of all literacy levels: a randomized, controlled trial. BMC Health Serv Res 2006;6:30. [24] Yehle KS, Sands LP, Rhynders PA, Newton GD. The effect of shared medical visits on knowledge and self-care in patients with heart failure: a pilot study. Heart Lung 2009;38:25-33. [25] Ni H, Nauman D, Burgess D, Wise K, Crispell K, Hershberger RE. Factors influencing knowledge of and adherence to self-care among patients with heart failure. Arch Intern Med 1999;159:1613-9. [26] Neily JB, Toto KH, Gardner EB, Rame JE, Yancy CW, Sheffield MA, et al. Potential contributing factors to noncompliance with dietary sodium restriction in patients with heart failure. Am Heart J 2002;143:29-33. [27] Cline CM, Bjorck-Linne AK, Israelsson BY, Willenheimer RB, Erhardt LR. Non- compliance and knowledge of prescribed medication in elderly patients with heart failure. Eur J Heart Fail 1999;1:145-9. [28] Fredericks S, Beanlands H, Spalding K, Da Silva M. Effects of the characteristics of teaching on the outcomes of heart failure patient education interventions: a systematic review. Eur J Cardiovasc Nurs 2010;9:30-7. Downloaded for Anonymous User (n/a) at Florida State University from ClinicalKey.com by Elsevier on March 02, 2022. For personal use only. No other uses without permission. Copyright @2022. Elsevier Inc. All rights reserved.See Answer
    • Q4:Assignment-3 MSCA31010: Linear & Non-Linear Models (Acknowledgement: Special thanks to Francisco Azeredo & Ming_Long Lam for their content creation support) For this homework, you can use either R or Python, use Word docs, R-markdowns, Jupyter notebooks, or html’s for submission. Train a binary logistic regression model on the claim_history.csv. Your model will predict the likelihood of filing more than one claim in one unit of exposure. You will first calculate the Frequency variable by dividing the CLM_COUNT by EXPOSURE. Next, you will create a binary target variable that determines if the Frequency is strictly greater than one (i.e., the Event). You will use MSTATUS, CAR_TYPE, REVOKED, and URBANICITY as the categorical predictors, and CAR_AGE, MVR_PTS, TIF, and TRAVTIME as the interval predictors. Your goal is to train a model that has just the right set of predictors. The standard libraries for R or Python are allowed. You need to drop all missing values (i.e., NaN) of all the predictors and the target variable before training your model. (15 points) Before you train the model, we want to explore the predictors. For each predictor, generate a line chart that shows the odds of the Event by the predictor’s unique values. The predictor’s unique values are displayed in ascending lexical order. (20 points) Enter the predictors into your model using Forward Selection. The Entry Threshold is 0.05. Please provide a detailed report of the Forward Selection. However, you do not need to show steps such as in the previous question. The report should include (1) the predictor entered, (2) the log-likelihood value, (3) the Deviance Chi-squares statistic, (4) the Deviance Degree of Freedom, and (5) the Chi-square significance. (10 points). Which predictors does your final model contain? (10 points). Please show a table of the complete set of parameters of your final model. Please also include the exponentiated estimates (i.e., apply the exp() function on the parameter estimates). 2. You will visually assess your final model in Question 1. Please color-code the markers according to the Exposure value. Also, please briefly comment on the graphs. (10 points). Please plot the predicted Event probability versus the observed Frequency. (10 points). Please plot the Deviance residuals versus the observed Frequency. 3. (15 Points) You will calculate the Accuracy metric to assess your final model in Question 3. If the predicted Event probability of an observation is greater than or equal to 0.25, then you will classify that observation as the Event (i.e., filing more than one claim per unit exposure). An observation is correctly classified if the predicted target value equals the observed target value. The Accuracy metric is the proportion of observations that are correctly classified. Bonus: (20 Points) For questions 1B, 1C and 1D apply recursive feature elimination (RFE) instead of Forward Selection (see: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html )See Answer
    • Q5: Back to my LMMS Wk 5 - Apply: Assignment [due Day 7] Download the TitanicTrain and TitanicScore data files. The training data file contains attributes of actual passengers aboard the Titanic when it sank in the northern Atlantic Ocean on April 15, 1912. The scoring data set contains 100 fictitious people. You will build a logistic regression model using the actual Titanic data and then use the model to predict the survival status of the fictitious people in the scoring data set. To understand the attributes: ⚫ The TicketNumber and PassengerName attributes identify the passengers. TicketNumber does not exist in the scoring data because the fictitious people were not actually on the ship. You will predict what their fate likely would have been had they boarded the ship. • The Sex attribute is binary: 0 = female; 1 = male. • The Age attribute is the passenger's age in years. In the training data, some fractions of a year are included, and in some cases, the passenger's age was not recorded. • The SiblingsAndSpouses attribute gives the number of brothers and sisters and/or spouses the passenger had on board the Titanic with them. For example, if a married couple boarded the Titanic with their two unmarried children, the husband and wife would each have 1 in this attribute (each other), and the children would likewise each have 1 in this attribute (each other). • The ParentsAndChildren attribute gives the number of parents and/or children the passenger had on board the Titanic with them. Continuing the example above, the mother and father would each have 2 in this attribute (their two children), and the children would likewise each have 2 in this column (their mother and father). ⚫ The PassengerClass attribute indicates the level of luxury the passenger booked: 1 = first-class, most expensive on the upper decks of the ship; 2 = second class, less expensive in the middle decks of the ship; 3 = third class, least expensive in the lower decks of the ship. The Fare attribute is the amount of money the passenger paid for their ticket. • The SurvivalStatus attribute exists only in the training data set and indicates whether each passenger lived or died when the ship sank. Complete the following steps: 1. Import both the training and scoring data sets into RStudio. Name each of them descriptively. For simplicity in building the model, you may wish to attach() the training data set. 2. Using the glm() function in R, build a logistic regression model using the SurvivalStatus attribute as the dependent variable and all of the other attributes except TicketNumber and PassengerName as independent variables. Ensure the model is stored in a descriptively named object in R. Apply the as.factor() function in R to the dependent variable, and ensure that the glm() model's family parameter is: family-binomial() 3. Apply the summary() function in R to the model so that you can evaluate the statistical significance of the independent variables. 4. Using the predict() function in R and type="response", apply the logistic regression model to the scoring data. Create a new data frame that combines the model's predicted outcomes with the individual observations in the scoring data. Name this data frame descriptively and view it in RStudio. Answer the following questions using the model. 1 Examining the summary() of the logistic regression model, how many of the independent variables are not statistically significant at the 95% confidence level (0.05 alpha)? One independent variable is not statistically significant. Two independent variables are not statistically significant. All independent variables are statistically significant. Four independent variables are not statistically significant. 2 Examining the z values in the summary() of the logistic regression model, which independent variable is the single best predictor of SurvivalStatus? 3 4 Fare Age PassengerClass ☐ Sex Examining the z values in the summary() of the logistic regression model, which independent variable describing families traveling together is a better predictor of Survival Status: SiblingsAndSpouses or ParentsAndChildren? Both of these independent variables are equally good predictors of SurvivalStatus. ParentsAndChildren is a better predictor of SurvivalStatus than SiblingsAndSpouses. There is no way to tell in this model. SiblingsAndSpouses is a better predictor of SurvivalStatus than ParentsAndChildren. K K The standard explanation regarding the evacuation of the Titanic is that women, children, and first-class passengers were allowed to board the lifeboats first, thus increasing their chances of survival, while passengers in lower classes (second and third) and older, male passengers were required to wait to evacuate. Does this model support the standard explanation? No. PassengerClass, Sex, and Age are not strong predictors of SurvivalStatus. Partially. Age is a strong predictor of SurvivalStatus, but Sex and PassengerClass are not. Partially. Sex and Age are strong predictors of SurvivalStatus, but PassengerClass is not. Yes. PassengerClass, Sex, and Age are all strong predictors of SurvivalStatus. 5 Examining the data frame containing the predictions for the scoring data set, how many of the fictitious people are predicted to survive the Titanic disaster (Predicted Response scores greater than 50% )? ☐ 66 6 100 ப0 34 What is the age of the person in the scoring data set who is predicted to survive the Titanic disaster with a 52.7% probability? 49 17 2 ☐ 34 7 What is the name of the person in the scoring data set who has the highest predicted survival probability? Warren Guerrero Tracey Mcgee Renee Cortez Kellie May 8 Of the passengers in the scoring data set who are younger than 10 years old, how many are predicted to survive? 9 3 ☐ 0 12 9 There are several male, first-class passengers in the scoring data set who are older than 65. How many are predicted to survive? 10 ☐ 3 0 9 12 Of the women in the scoring data set who would have been traveling with at least one parent or child, how many are predicted to survive? 1 49 26 45See Answer
    • Q6: Scenario You work as a middle manager for one of the top U.S. producers of luxury and mass-market automobiles and trucks. The chief technology officer (CTO) of the company from the course scenario has been watching new technology developments that the company could integrate into its vehicles to enhance the usefulness of and access to the data acquired by the many digital sensors that have been integrated into vehicle subsystems over the past 20-30 years. The technology trend of particular interest is the internet of things (IoT)- the interconnection of embedded devices, such as sensors and computers, over the internet. By taking advantage of this trend, the CTO believes the company can seize an opportunity to provide better service and predictive maintenance to its customers, improving customer satisfaction and adding additional revenue streams. Based on briefings by the CTO, senior management has decided to implement loT in its product line. Your CTO has asked you to lead a cross- functional team to take this initiative forward. Your first task is to make a recommendation for how the company should approach this business problem. Do you recommend using incremental or discontinuous (that is, slow or fast) innovation? Specifically: Should the company... A. Design a completely new product line, based on the new technology (discontinuous innovation) or B. Add new technology features into one model and then incrementally into the broader product line (incremental innovation) The recommendation you and your team make is an important first step in pursuing this new technology. In Milestone One, you and your team defined what that innovation looked like: would it be disruptive (new product line) or incremental (new features in one existing product)? In Milestone Two, you developed a strategy plan and a high-level business model. You've shared these with various stakeholders, and they are in agreement with your assessment of how best to get to market with this loT innovation, which will allow the company to be competitive. Now, the CTO has asked you to come up with a proposed organizational structure that will support the innovation implementation. In this way, you will be assisting in making the innovation sustainable for the company from the course scenario. Companies that are innovative must make sure they have structured the organization in a way that supports innovation, and a key component of innovation is keeping track of what their customers want in terms of products and services. Directions Complete each of the three parts of this project. Use your slides from Milestone One, your document from Milestone Two, and feedback on Milestone One and Milestone Two to complete the first two parts of your presentation. You can use the resources in the Supporting Materials section (below) for any other information you will need to complete your project. Part One: Innovation Approach In this section, share your revised slides from Milestone One and make your recommendation for the innovation approach you think the company should pursue. Ensure this section includes the following information: 1. Explain potential risks and benefits for options A and B. • Overview (1 slide): Present the business problem and options A and B. • Option A (1-2 slides): Explain at least two potential risks and benefits for option A. • Option B (1-2 slides): Explain at least two potential risks and benefits for option B. 2. Compare your competition's products and services. • Competitors: (3-4 slides): Evaluate the competitors' current products and services. • What are your competitors' current products and services? • Are your competitors expanding in the current market? Explain how this impacts their market strength. 3. Analyze your company's capability to pursue the innovation. • Complete a partial gap analysis (2 slides): • Does the company own the technology, or does it need to be purchased? • How is the technology currently being used in today's products and services? • What type of technology is available to purchase? 4. Recommend the innovation approach your company should pursue. • Innovation Approach (2 slides): Explain which innovation approach you are recommending and why. • Consider the different stakeholders (research and development [R&D], marketing, finance) while communicating your recommendations. • Include a description of the incremental or discontinuous product that you are recommending for R&D. • Include the sales forecasts for marketing. • Include a financial snapshot for finance. Part Two: Strategic Plan In this section, share your revised paper from Milestone Two, which provides an overview of your competitors' position in the marketplace, the available and future marketplace, and a plan for how to address a change in business conditions. Ensure this section includes the following information. 1. Analyze the competitors' relative strengths in the marketplace. Use Power BI to create charts on the growth rates. • What is the growth rate of each competitor? • How much of the market does each competitor now own? Do you see a trend of gaining or losing market share? • Determine each competitor's market strength by looking at the financials for each competitor. 2. Explain how your company's market share compares to your competitors'. Use Power BI to create charts on your and your competitors' market share. • Is your company gaining or losing market share? • What is your company's growth potential in the industry? • How do your company's financials look compared to your competitors'? 3. Identify future potential total available market (TAM) and growth for chosen product/service and technology. • What is the TAM for cars and light trucks? • What is the TAM for IoT-connected cars and light trucks? • What is the projected compound annual growth rate (CAGR) for cars and light trucks? • What is the CAGR for loT-connected cars and light trucks? 4. Identify ways to adjust if business conditions change. • What can you do if your customers are slow to respond (buy) the innovation? • What can you do if one competitor is overtaking all the others, including you?See Answer
    • Q7:8. The Peachtree Airport in Atlanta serves light aircraft. It has a single runway and one air traffic controller to land planes. It takes an airplane 12 minutes to land and clear the runway. Planes arrive at the airport at the rate of 4 per hour. a. Determine the average number of planes that will stack up, waiting to land. b. Find the average time a plane must wait in line before it can land. c. Calculate the average time it takes a plane to clear the runway once it has notified the airport that it is in the vicinity and wants to land. d. The FAA has a rule that an air traffic controller can, on average, land planes a maximum of 45 minutes out of every hour. There must be 15 minutes of idle time available to relieve the tension. Will this airport have to hire an extra air traffic controller?See Answer
    • Q8:Case studies should be formatted according to APA guidelines for an executive summary. ul. Case Study Alumni donations are an important source of revenue for colleges and universities. If administrators could determine the factors that could lead to increases in the percentage of alumni who make a donation, they might be able to implement policies that could lead to increased revenues. Research shows that students who are more satisfied with their contact with teachers are more likely to graduate. As a result, one might suspect that smaller class sizes and lower student/faculty ratios might lead to a higher percentage of satisfied graduates, which in turn might lead to increases in the percentage of alumni who make a donation. The following table shows data for 48 national universities. The Graduation Rate column is the percentage of students who initially enrolled at the university and graduated. The % of Classes Under 20 column shows the percentages of classes with fewer than 20 students that are offered. The Student/Faculty Ratio column is the number of students enrolled divided by the total number of faculty. Finally, the Alumni Giving Rate column is the percentage of alumni who made a donation to the university. • AlumniGiving.xlsx Directions Respond in detail to each question. Review the rubric prior to responding. You can use R or JMP, and refer to the chapter readings for additional regression model suggestions. Prepare a managerial report as described below. In the Excel data, add in four additional rows for Add in Park University, Donnelly College, <Instructor Last Name> University, and University of <Your Last Name> 1. Use methods of descriptive statistics to summarize the data. 2. Develop an estimated simple linear regression model that can be used to predict the alumni giving rate, given the graduation rate. Describe and analyze your findings. 3. Develop an estimated multiple linear regression model that could be used to predict the alumni giving rate using Graduation Rate, % of Classes Under 20, and Student/ Faculty Ratio as independent variables. Discuss your findings. 4. Based on the results in parts (2) and (3), do you believe another regression model may be more appropriate? Estimate this model, and discuss your results. 5. What conclusions and recommendations can you derive from your analysis? What universities are achieving a substantially higher alumni giving rate than would be expected, given their Graduation Rate, % of Classes Under 20, and Student/Faculty Ratio? What universities are achieving a substantially lower alumni giving rate than would be expected, given their Graduation Rate, % of Classes Under 20, and Student/ Faculty Ratio? What other independent variables could be included in the model? Remember to include screenshots of the models discussed in your paper. Assignment Instructions ||documents required word and excel in APA format.See Answer
    • Q9:4. The chairperson of the department of management at State University wants to forecast the number of students who will enroll in production and operations management (POM) next semester to determine how many sections to schedule. The chair has accumulated the following enrollment data for the past eight semesters: Students Enrolled in POM Semester 1 2 3 4 5 6 7 8 00 400 450 350 420 500 575 490 650 a. Compute a three-semester moving average forecast for semesters 4 through 9. b. Compute the exponentially smoothed forecast (a = .20) for the enrollment data. c. Compare the two forecasts by using MAD, and indicate the more accurate of the two.See Answer
    • Q10:2. The manager of the Carpet City outlet needs to make an accurate forecast of the demand for Soft Shag carpet (its biggest seller). If the manager does not order enough carpet from the carpet mill, customers will buy their carpet from one of Carpet City's many competitors. The manager has collected the following demand data for the past 8 months: Month Demand for Soft Shag Carpet (1,000 yd.) 1 2 3 4 5 6 (a) and (b), using MAD. Which one should the dealer use for January of the next year? 7 8 8 12 7 9 15 11 10 12 a. Compute a 3-month moving average fore- cast for months 4 through 9. b. Compute a weighted 3-month moving av- erage forecast for months 4 through 9. As- sign weights of .55, .33, and .12 to the months in sequence, starting with the most recent month. c. Compare the two forecasts by using MAD. Which forecast appears to be more accu- rate?See Answer
    • Q11:2. Generate and interpret the Descriptive Statistics of Florida House Prices (data file: house_selling_prices_FL.xlsx). 1. Pick important aspects of the Florida House data set and generate Descriptive Statistics. 2. Is the mean or median a better measure of center for this data set? 3. Why? 4. Plot a histogram, regression line, or some other chart using a column or columns or interest in the data set. 5. Make sure to label all charts and axes.See Answer
    • Q12:1. Create a histogram of number of cones sold (data file: Cones(2).xlsx). 1. What is the min and max of the data set? 2. Create 6 Bins (bin does not have to start at zero) for the data. 3. Use the Data Analysis Plug-in to generate the histogram table and chart. 4. Generate the table - make sure it is labeled properly and the data all falls within the bins. 5. Generate the histogram chart - make sure it is properly labeled and that it has the properties of a histogram.See Answer
    • Q13:5.8 Twitter users and news, Part I. A poll conducted in 2013 found that 52% of U.S. adult Twitter users get at least some news on Twitter. ¹2. The standard error for this estimate was 2.4%, and a normal distribution may be used to model the sample proportion. Construct a 99% confidence interval for the fraction of U.S. adult Twitter users who get some news on Twitter, and interpret the confidence interval in context.See Answer
    • Q14:5.2 Identify the parameter, Part II. For each of the following situations, state whether the parameter of interest is a mean or a proportion. (a) A poll shows that 64% of Americans personally worry a great deal about federal spending and the budget deficit. (b) A survey reports that local TV news has shown a 17% increase in revenue within a two year period while newspaper revenues decreased by 6.4% during this time period. (c) In a survey, high school and college students are asked whether or not they use geolocation services on their smart phones. (d) In a survey, smart phone users are asked whether or not they use a web-based taxi service. (e) In a survey, smart phone users are asked how many times they used a web-based taxi service over the last year.See Answer
    • Q15:Part 2: You are a business analytics analyst, and your director has given you the task of analyzing automobile data. Using the attached "Automobiles" dataset, perform a K-Means cluster analysis on these variables. It is recommended that you perform a cluster analysis for 2, 3, 4, and 5 clusters and select the optimal model based on the results. In a 300-word summary, provide the requested screenshots and address the following: 1. Explain your approach to the problem. 2. State the optimal number of clusters and the cluster sizes, and discuss the most logical cluster characteristics. Include discussion of how you would explain the attributes of each cluster group and justify your explanation. 3. Provide at least three scatterplots of selected input variables of your choice that help explain the cluster categories. 4. Provide a line chart of the silhouette coefficients for each cluster and the overall silhouette coefficient for the model. 5. Export the cluster assignment for each record to an Excel file. Name the file "Clusters.xlsx." Provide overall conclusion based upon the results of the K-Means analysis. Note that you are required to submit the completed KNIME *.knwf file to your instructor. Specifically, export your KNIME model to a KNIME workflow file. To perform this task in KNIME, ensure that your KNIME model is active (i.e., displayed). Then, go to File - >Export KNIME Workflow. In the "Destination workflow file name (.knwf)" area, browse to a specific location on your computer. Click "Save" and then click "Finish."See Answer
    • Q16:Problem 2: (50 pts) You are given 204 observations from a travel survey conducted in the Seattle Metropolitan area. The purpose of the survey was to study the number of times (per week) commuters changed their departure time on their work-to-home trip to avoid traffic congestion. The data are non-negative integers with the mean approximately equal to the variance. Your task is to estimate the appropriate count-data model. • Following the forward stepwise process, find your best fit model specification. Some things to consider as you fit your model: - Is there acceptable correlation among explanatory variables? Do the signs of the coefficients make sense? You will need to create indicators for some of the variables. - Is the model you have chosen to use appropriate? Double-check after you have arrived at your best fit specifications. Explain your process of determining if the count-data model you are using is appropriate or not be specific. • After you have arrived at your best fit model specifications, provide a discussion of the logical process that led you to the selection of your final specification. • Present the descriptive statistics of the variables in your final model specification. You do not have to categorize variables by category, but are welcome to do so. Points deducted for incorrect model presentation. • Present your model as shown in the document on Canvas. You do not have to categorize variables by category, but are welcome to do so. Points deducted for incorrect model presentation. • Provide a discussion for each of the variables in your final model specification, including their quantitative effect on your dependent variable. What are some plausible reasons for the significance of the variable and its effect on changing the number of times (per week) a commuter changed their departure time? You are welcome to use your intuition or find sources that confirm/validate your results. • Based on the distribution of your dependent variable, would a truncated model, cen- sored model, or zero-inflated model be appropriate? Explain and be specific. Definitions of variables are given on the following page.See Answer
    • Q17:Problem 1: (50 pts) You are given injury severity data for 2,273 single-vehicle motorcycle crashes in Indiana. There are four possible severity outcomes: no injury (property damage only and possible injury), non-incapacitating injury, incapacitating injury, and fatality. You want to know what the likelihood of an individual being involved in a crash is based on the available crash data characteristics. Your task is to estimate an ordered probability model of motorcyclists' injury severity. • Following a forward stepwise process, find your best fit model specification. Some things to consider as you fit your model: Make sure the dependent variable is ordered and follows the format required for model estimation. - Do the signs of the coefficients make sense? - Is there acceptable correlation among explanatory variables? You must create some indicators that I did not cover in the tutorial documents. - Do you need to correct for heteroskedasticity? • After you have arrived at your best fit model specifications, provide a detailed discus- sion of the logical process that led you to the selection of your final specification. Present the descriptive statistics of the variables included in your final model specifica- tions as shown in the document on Canvas. You do not have to categorize the variables by category, but are welcome to do so. Are there any statistics worth highlighting? Points will be deducted for incorrect descriptive statistics presentation. • Present your model as shown in the document on Canvas. You do not have to categorize the variables by category, but are welcome to do so. Points will be deducted for incorrect model presentation. • Provide a discussion for each of the variables in your final model specifica- tion. Based on your model, what are the effects of the variables on fatal/injury crash probability? What are some plausible reasons for the significance of the variable and its effect on injury severity outcome? You are welcome to use your intuition, but also encouraged to find sources that confirm/validate your results. • Summarize your findings and provide some potential solutions for at least three of the variables in your final model specification. Use the countermeasure selection resources to determine your proposed solutions, explain why a countermeasure may be effective, and what the anticipated increase in safety would be should the countermeasure be implemented. • Prepare your deliverable as a mini-report based on what is being asked in the bullet points above. Definitions of available variables are given on the following page.See Answer
    • Q18:Question 3 of 7 < View Policies Current Attempt in Progress Disks of polycarbonate plastic from a supplier are analyzed for scratch and shock resistance. The results from 127 disks are summarized as follows: Scratch Resistance Shock Resistance High High 62 Low 17 (a) P(A) = i (b) P(B) i (c) P(A)= i (d) P(ANB) (e) P(AUB) = i Low 18 -/10 30 Let A denote the event that a disk has high shock resistance, and let B denote the event that a disk has high scratch resistance. If a disk is selected at random, determine the following probabilities. Round your answers to two decimal places (e.g. 0.98). IhSee Answer
    • Q19:3. Define the following, Maximum Sustainable Yield and Carrying Capacity. 4. Let F(X) be the Growth Function and X be the fish stock then F'(X) = dF(X)/dX is the first-order derivative of the growth function. Translate the following mathematical expression into words: F'(x) = 0 F'(x) > 0 F'(x) < 0See Answer
    • Q20:2. Consider the graph. What is the y axis measuring? What is the x-axis measuring? For each point, (a), (b), and (c), answer the following: What is happening to growth at that value of x? Why? If left un-altered how would the fish population grow at this level of x? 3. Define the following. Maximum Sustainable Yield and Carrying Capacity.See Answer
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