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Q1:Case studies should be formatted according to APA guidelines for an executive summary. Case Study Garden Brook Center made decision to produce a newer line of gardening products resulted in the need to construct either a small, medium or a large plant. The best selection of plant size depends on how the marketplace reacts to the new product line. To conduct an analysis, marketing management has decided to view the possible long-run demand as low (L), medium (M), or high (H). The following payoff table shows the projected profit in millions of dollars: Plant Size Small L 100 M - 140 H - 200 Medium L 220 M-350 H - 500 Large L 250 M - 400 H - 650 Directions dr. Respond in detail to each question. Review the rubric prior to responding. Prepare a managerial report as described below. Based on the data respond to the following question: 1. What is the decision to be made, and what is the chance event for Garden Brook's problem? 2. Construct a decision tree. 3. Recommend a decision based on the use of the optimistic, conservative, and mini-max regret approaches. 4. Provide additional analysis of what you would recommend to management.See Answer
Q2: Example: Predicting Home Prices The purpose of this section is to give you an idea of what a finished project should look like. This example uses a different data set than the one you will be using for the project but it follows the same pattern. The scenario for this example project is that a group of real estate investors are looking to put a bid on a group of 10 homes that have come up for sale. They want a 20% margin so they can make a profit when they resell the homes. Your job is to predict the price for the homes up for bid and then factor in the margin to give the investors a final bid amount to make for these houses. At the bottom of the page you will see a project submission template that has a set questions for you to answer. You will also see a completed project submission for your review. Also, at the bottom of the page you will find an Excel workbook. One of the sheets in that workbook contains the past home sales (Past Sales Data). The past home sales data was used to build a linear regression model, which produced a formula to help predict a home's value based tells on the number of bedrooms, bathrooms, and the square footage of the home. That formula is: price = 51880.41 + 44.72 \ *square feet + 52613.9 * bedrooms + 27513.48 * bathrooms (This is based on fake data) This formula is then applied to the homes up for bid, which you can see in the predicted price column in the Homes up for Bid worksheet. Lastly, Data Combined for Graph shows how to graph bedroom vs. price for both the past home sales and predicted prices of the homes up for bid. Please use this project submission as an example to help you on the Predicting Diamond Prices Project. Supporting Materials Excel Workbook Submission Template . Completed Report Project Details Predicting Diamond Prices This project is designed for three main reasons: • • • To give you a feel for what you'll be doing throughout the Nanodegree Program To introduce you to Udacity's project submission and review process To make sure you feel comfortable with the basics before you begin. If it feels too easy, don't worry. We have some great stuff in store for you. Project Overview A jewelry company wants to put in a bid to purchase a large set of diamonds, but is unsure how much it should bid. In this project, you will use the results from a predictive model to make a recommendation on how much the jewelry company should bid for the diamonds. US Number System All numbers that will be presented in this Nanodegree program will be based on the US numbering system where 5,269 is "five thousand two hundred sixty nine" and 158.1 is "one hundred fifty eight point one" where 1 is a decimal number. This is very important so please take note of this. Project Details A diamond distributor has recently decided to exit the market and has put up a set of 3,000 diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewelry company has shown interest in making a bid. To decide how much to bid, the company's analytics team used a large database of diamond prices to build a linear regression model to predict the price of a diamond based on its attributes. You, as the business analysts, are tasked to apply that model to make a recommendation for how much the company should bid for the entire set of 3,000 diamonds. The following diagram represents the analysis at a high level. Since the model is already built, your analysis will focus on the right side of the diagram. Diamonds (Predictors & Price) New Diamonds (Predictors only) Regression Equation Predicted Diamond Prices The linear regression model provides an equation that you can use to predict diamond prices for the set of 3,000 diamonds. The equation is below: **Price** = -5,269 + 8,413 x **Carat** + 158.1 x **Cut** + 454 x **Clarity** Step 1 · Understand the data: There are two datasets. diamonds.csv contains the data used to build the regression model. new_diamonds.csv contains the data for the diamonds the company would like to purchase. carat cut cut_ord color clarity clarity_ord price 0.51 Premium 4F VS1 4 1749 2.25 Fair 1G |1 1 7069 0.7 Very Good 3 E VS2 5 2757 0.47 Good 2F VS1 4 1243 0.3 Ideal 5G VVS1 7 789 0.33 Ideal 5D SI1 3 728 2.01 Very Good 3 G SI1 3 18398 0.51 Ideal 5F VVS2 6 2203 1.7 Premium 4D SI1 3 15100 0.53 Premium 4D VS2 5 1857 Both datasets contain carat, cut, and clarity data for each diamond. Only the diamonds.csv dataset has prices. You'll be predicting prices for the new diamonds.csv dataset. • Carat represents the weight of the diamond, and is a numerical variable. Cut represents the quality of the cut of the diamond, and falls into 5 categories: fair, good, very good, ideal, and premium. Each of these categories are represented by a number, 1-5, in the Cut_Ord variable. Clarity represents the internal purity of the diamond, and falls into 8 categories: 11, SI2, SI1, VS1, VS2, VVS2, VVS1, and IF. Each of these categories are represented by a number, 1-8, in the Clarity_Ord variable. Note: Transforming category variables to ordinal variables like this is not always appropriate, but we've done it here for simplicity. Step 2- Calculate the predicted price for diamond: For each diamond, plug in the values for each of the variables into the linear model (equation). Then solve the equation to get the estimated, or predicted, diamond price. We suggest using a spreadsheet tool like Excel, Numbers, or Google Sheets. You could also do it in Alteryx and/or Tableau if you already have your license. - Step 3 – Make a recommendation: Now that you have the predicted price for each diamond, it's time to calculate the bid price for the whole set. Note: The diamond price that the model predicts represents the final retail price the consumer will pay. The company generally purchases diamonds from distributors at 70% of that price, so your recommended bid price should represent that. Project Submission To complete this project, you will be submitting a file in pdf format that contains the answers to the following questions across three steps. Step 1 - Understanding the Model: • According to the linear model provided, if a diamond is 1 carat heavier than another with the same cut and clarity, how much more would the retail price of the heavier diamond be? Why? . If you were interested in a 1.5 carat diamond with a Very Good cut (represented by a 3 in the model) and a VS2 clarity rating (represented by a 5 in the model), what retail price would the model predict for the diamond? Step 2 - Visualize the Data: Create two scatter plots. If you're not sure what a scatter plot is, see here (opens in a new tab) + Plot 1 - Plot the data for the diamonds in the database, with carat on the x-axis and price on the y-axis. . Plot 2 - Plot the data for the diamonds for which you are predicting prices with carat on the x-axis and predicted price on the y-axis. • Note: You can also plot both sets of data on the same chart in different colors. What strikes you about this comparison? After seeing this plot, do you feel confident in the model's ability to predict prices? Step 3 - The Recommendation: What bid do you recommend for the jewelry company? Please explain how you arrived at that number. Supporting Materials Use the submission template to submit your project. The submission template is available at the bottom of this page under Supporting Materials. Data • diamonds.csv - contains carat, cut, clarity, and price information for each diamond in the dataset used to build the regression model. • new_diamonds.csv - contains carat, cut, and clarity information for the diamonds the company would like to purchase. Spreadsheet Calculations Throughout this Nanodegree you will be presented with problems that require you to make calculations. While you are free to use whatever analytical tool ofSee Answer
Q3:51. Based on past sales experience, an appliance store stocks five window air conditioner units for the coming week. No orders for additional air conditioners will be made until next week. The weekly consumer demand for this type of appliance has the probability distribution given in the file P05_51.xlsx. a. Let X be the number of window air conditioner units left at the end of the week (if any), and let y be the number of special stockout orders required (if any), assuming that a special stockout order is required each time there is a demand and no unit is available in stock. Find the probability distributions of X and Y. b. Find the expected value of X and the expected value of Y. c. Assume that this appliance store makes a $60 profit on each air conditioner sold from the weekly available stock, but the store loses $20 for each unit sold on a spe- cial stockout order basis. Let Z be the profit that the store earns in the coming week from the sale of window air conditioners. Find the probability distribution of Z. d. Find the expected value of Z.See Answer
Q4:9. A business manager who needs to make many phone calls has estimated that when she calls a client, the prob- ability that she will reach the client right away is 60%. If she does not reach the client on the first call, the proba- bility that she will reach the client with a subsequent call in the next hour is 20%. a. Find the probability that the manager reaches her client in two or fewer calls. b. Find the probability that the manager reaches her client on the second call but not on the first call. c. Find the probability that the manager is unsuccessful on two consecutive calls. 53See Answer
Q5:7. The following table shows data on the average number of customers processed by several bank ser- vice units each day. The hourly wage rate is $25, the overhead rate is 1.0 times labor cost, and mate- rial cost is $5 per customer. Unit A B C D Employees 4 5 8 3 Customers Processed/Day 36 40 60 20 a. Compute the labor productivity and the multifactor productivity for each unit. Use an eight-hour day for multifactor productivity. b. Suppose a new, more standardized procedure is to be introduced that will enable each employee to process one additional customer per day. Compute the expected labor and multifactor produc- tivity rates for each unit.See Answer
Q6:11. It has been said that a typical Japanese automobile manufacturer produces more cars with fewer workers than its U.S. counterpart. What are some possible explanations for this, assuming that U.S. workers are as hardworking as Japanese workers?See Answer
Q7:urvivorship and column B. This is the total number of people in your group upon which death took its toll 1. To calculate the # alive, place your total number of deaths in the first row (0-9) of as they grew older. 2. Subtract the number who died in each age interval (column A) from the number who were "alive" in your sample from the beginning of that age interval. Write this number in the next row in column B. Repeat to fill out column 3. Calculate the survivorship - for each row in column C, divide the number in column B by the TOTAL you found at the bottom of column A. This gives you the fraction of people that survived to each age interval. By definition, the survivorship of the first age interval equals 1.0 (all living newborns have survived to that point) Females Who DiSee Answer
Q8:(a) Reconstruct the Op-Ed's argument (approx. 200 words) Explain whether the Op-Ed makes an inductive or deductive argument. Explicitly use the terminology of informal logic (such as premise, conclusion, conditional, connective, quantifier, etc.). Make sure to put it in valid premise-conclusion form (P1, P2, ... C). (b) Evaluate the Op-Ed's argument (approx. 250 words) Endorse or reject the argument, explicitly using the terminology of informal logic (such as validity, soundness, and fallacy). For deductive arguments, evaluate whether it is valid or invalid, giving support or objections to at least one of the premises. For inductive arguments, evaluate whether it is strong or weak, giving support or objections to at least one of the premises. (c) Develop a counter-argument (approx. 250 words) Provide at least one counterargument to your evaluation in Part B. Then, respond to your own evaluation using the language of informal logic as appropriate.See Answer
Q9:Format and Style: This research paper should follow APA 7th edition formatting guidelines Use side headings in each part to organize the paper Use Times New Roman font size 12, double space, number all pages except for title page Include a title page, a correctly formatted table of contents page, and a references page(s) The paper should be detailed, with correct in-text citations and references Direct quotes/quoted material should not exceed 20% of the paper The report should be at least 15 pages long excluding cover page, table of content, references, appendices, graphs, tables Use a minimum of 5 sources/references Proofread the paper for spelling, grammar, and style errorsSee Answer
Q10:idea. • Perform a PESTEL Analysis to understand understand the external environment. Step 2 • Use 2-3 online tools to assess the popularity of your business idea. You should choose a product that people are searching for and that has a high volume of search. • You can use this Link to select the tool. Please only choose the tool that is free or has a free trial. • Provide screenshots of the tools that you have used.See Answer
Q11:Case Study North-South Airline In January 2012, Northern Airlines merged with Southeast Air- lines to create the fourth largest U.S. carrier. The new North- South Airline inherited both an aging fleet of Boeing 727-300 aircraft and Stephen Ruth. Stephen was a tough former Secre- tary of the Navy who stepped in as new president and chairman of the board. Stephen's first concern in creating a financially solid com- pany was maintenance costs. It was commonly surmised in the airline industry that maintenance costs rise with the age of the aircraft. He quickly noticed that historically there had been a significant difference in the reported B727-300 maintenance costs (from ATA Form 41s) in both the airframe and the engine areas between Northern Airlines and Southeast Airlines, with Southeast having the newer fleet. On February 12, 2012, Peg Jones, vice president for op- erations and maintenance, was called into Stephen's office and asked to study the issue. Specifically, Stephen wanted to know whether the average fleet age was correlated to direct airframe maintenance costs and whether there was a relationship be- tween average fleet age and direct engine maintenance costs. Peg was to report back by February 26 with the answer, along with quantitative and graphical descriptions of the relationship. Peg's first step was to have her staff construct the average age of the Northern and Southeast B727-300 fleets, by quarter, North-South Airline Data for Boeing 727-300 Jets YEAR 2001 2002 2003 2004 2005 2006 2007 AIRFRAME COST PER AIRCRAFT ($) 51.80 54.92 69.70 68.90 NORTHERN AIRLINES DATA 63.72 84.73 78.74 ENGINE COST PER AIRCRAFT ($) 43.49 38.58 51.48 58.72 45.47 50.26 79.60 AVERAGE AGE (HOURS) 6,512 8,404 11,077 11,717 13,275 15,215 18,390 Bibliography Berran, Mark L., David M. Levine, and Timothy C. Krehhiel Basic Business Statistics, 12 d. Upper Saddle River, NJ: Pearson, 2012 Black, Ken Burners Statistics: For Contemporary Decision Making, 8th ed John Wiley & Sons, Inc, 2014. Draper, Norman R., and Harry Smith. Applied Regrenzion Analysis, 3rd ed. Now York: John Wiley & Sons, Inc., 1998. since the introduction of that aircraft to service by each airline in late 1993 and early 1994. The average age of each fleet was calculated by first multiplying the total number of calendar days each aircraft had been in service at the pertinent point in time by the average daily utilization of the respective fleet to determine the total fleet hours flown. The total fleet hours flown was then divided by the number of aircraft in service at that time, giving the age of the "average" aircraft in the fleet. The average utilization was found by taking the actual total fleet hours flown on September 30, 2011, from Northern and Southeast data, and dividing by the total days in service for all aircraft at that time. The average utilization for Southeast was 8.3 hours per day, and the average utilization for Northern was 8.7 hours per day. Because the available cost data were calcu- lated for each yearly period ending at the end of the first quarter, average fleet age was calculated at the same points in time. The fleet data are shown in the following table. Airframe cost data and engine cost data are both shown paired with fleet average age in that table Discussion Questions 1. Prepare Peg Jones's response to Stephen Ruth. Note: Dates and names of airlines and individuals have been changed in this case to maintain confidarialty. The data and is described here are real AIRFRAME COST PER AIRCRAFT ($) 13.29 25.15 32.18 31.78 SOUTHEAST AIRLINES DATA 25.34 32.78 35.56 ENGINE COST PER AIRCRAFT ($) 18.86 31.55 40.43 22.10 19.69 32.58 38.017 AVERAGE AGE (HOURS) 5,107 8,145 7,360 5,773 7,150 9,364 8,259 APPENDIX 4.1: FORMULAS FOR REGRESSION CALCULATIONS 145 Kuiper, S., and J. Skla. Practicing Statistier: Guided Investigations for the Second Coarse Upper Saddle River, NJ: Pearson, 2013. Kutner, Michael, John Neter, and Chris J. Nachsheim. Appled Linear Regres xion Models, 4th ed. Boston, New York: McGraw-Hillwin, 2004. Mendenhall, William, and Terry L. Sincich. A Second Course in Statistica: Region Analysis, 7th ed. Upper Saddle River, NJ. Pearson, 2012.See Answer
Q12:Municipal bonds should constitute at least 20% of the investment. At least 40% of the funds should be placed in a combination of electronic firms, aerospace firms, and drug manufacturers. No more than 50% of the amount invested in municipal bonds should be placed in a high-risk, high-yield nursing home stock. Subject to these restraints, the client's goal is to max- imize projected return on investments. The analysts at Heinlein and Krampf, aware of these guidelines, prepare a list of high-quality stocks and bonds and their corresponding rates of return: INVESTMENT Los Angeles municipal bonds Thompson Electronics, Inc. United Aerospace Corp. Palmer Drugs Happy Days Nursing Homes PROJECTED RATE OF RETURN (%) 5.3 6.8 4.9 8.4 11.8 (a) Formulate this portfolio selection problem using LP. (b) Solve this problem./n8-2 (Investment decision problem) The Heinlein and Krampf Brokerage firm has just been instructed by one of its clients to invest $250,000 of her money obtained recently through the sale of land holdings in Ohio. The client has a good deal of trust in the investment house, but she also has her own ideas about the distribution of the funds being invested. In particular, she requests that the firm select what- ever stocks and bonds they believe are well rated but within the following guidelines:See Answer
Q13:Read the Case study and provide with a Business Case Analysis (addressing everything which is mentioned) 2000-2500 words. Follow the rubricSee Answer
Q14:Q6. Employee Engagement, Turnover and Unit Performance Over Time a) Prepare the data for and conduct a multiple regression analysis. Y = Unit sales (2022) Coefficients X1 = Average Unit Engagement Rating from 2022 Raw Engagement Data Subset (Average of the nine items) X2 = Unit Turnover Rates (2022) X3 = Prior Unit Sales (2021) Number of Observations in Regression Model: X1 = Average Unit Engagement Rating for Non-Exempt Employees from Engagement Survey Results (Average of then nine items) X2 = Unit Turnover Rates (2022) X3 = Prior Unit Sales (2021) P-value (Use 4 Decimal Places) b) Correlation between your computed unit-level average engagement score, and the value provided for Non-Exempt employees by unit for 2022. r= Number of Observations in Regression Model: c) Conduct the same regression as for Question 6a, but now use the average non-exempt score from the Engagement Survey Results tab. Y = Unit sales (2022) Coefficients Significant Effect? P-value (Use 4 Decimal Places) (p<.05) (Yes or No) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) d) Compare these two regressions. Which one do you think is better? Why? (Yes or No) (Yes or No)See Answer
Q15:Q4. The Relationship between Unit Turnover and Performance over time a) Compute a correlation between the unit turnover rate of 2021 and 2022. b) Regression Model 1 Y=Unit sales (2022) X= Unit Turnover Rate (2022) Number of Observations in Regression Model 1: c) Regression Model 2 Y-Unit sales (2022) X1=Unit Turnover Rate (2022) X2= Unit Prior Sales (2021) Coefficients d) Regression Model 3 Number of Observations in Regression Model 2: Y=Change in Unit sales (2021-2022) X= Unit Turnover Rate (2022) Coefficients Coefficients Number of Observations in Regression Model 3: P-value (Use 4 Decimal Places) P-value (in 4 Decimal Places) P-value (Use 4 Decimal Places) Significant Effect? (p<.05) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No)/ne) Regression Model 4 Y=Change in Unit sales (2021-2022) X= Change in Unit Turnover Rate (2022 rate-2021 rate) Number of Observations in Regression Model 4: Regression Model 5 Y= Unit sales (2022) Coefficients Xl=Change in Unit Turnover Rate (2022 rate-2021 rate) X2= Prior Unit Sales (2021) Coefficients Number of Observations in Regression Model 5: P-value (Use 4 Decimal Places) P-value (in 4 Decimal Places) Significant Effect? (p<.05) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) g) Considering the five regression models you conducted in Question 4b through Question 4f, briefly interpret the results of regression models in a plain language, explaining the nature of relationships between unit sales and turnover at a given year and over years. Is there any interesting finding here for Mr. Macky's management? Answer:/nQ5. Computing the percent of unit-level variance of Employee Engagement Data a) Compute the following for each of the nine items of employee engagement survey. Item Total variance Variance of group averages Percent of variance attributable to group membership Job Satisfaction Collaboration Communication Support Customer Focus Personal Growth Inclusion Empowerment Accountability b) Percent of variance attributable to group membership: c) Based on your findings in Question 5a and Question 5b above, which individual-level engagement items can be also considered for aggregated effect at the unit level on other unit-level? Would it be appropriate to aggregate the overall measure of employee engagement? Answer:See Answer
Q16:Q3. Computing SDy based on Performance Rating and Unit Performance a) Using the standardized scores of performance ratings for managers and assistant managers, compute SDy by running regressions on the unit sales of the same years. Y=Unit sales (all years) Significant Effect? (p<.05) X1=Standardized Performance Rating of Managers X2= Standardized Performance Rating of Assistant Managers Number of Observations in Regression Model: b) SDy of Managers = $_ c) SDy of Assistant Managers = $_ Y=One-Year Change in Unit sales (for all years possible) Coefficients X1=Standardized Performance Rating of Managers X2= Standardized Performance Rating of Asst. Managers d) Repeat Question 3a and compute SDy for managers and assistant managers yet by regressing one-year change in unit sales rather than the unit sales of concurrent years. Fill in the table below and blanks for SDy. Coefficients P-value (Use 4 Decimal Places) Number of Observations in Regression Model: e) SDy of Managers = $_ f) SDy of Assistant Managers = $_ (Yes or No) P-value (Use 4 Decimal Places) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No)/ng) Repeat Question 3a and compute SDy for managers and assistant managers yet after controlling for one-year prior unit sales and store market information. Fill in the table below and blanks for SDy. Y=Unit sales (for all possible years except 2016) X1= Standardized Performance Rating of Managers X2= Standardized Performance Rating of Asst. Managers X3= Store Market X4=Prior unit sales (one-year) Coefficients Number of Observations in Regression Model: h) SDy of Managers = $_ i) SDy of Assistant Managers = $_ P-value (Use 4 Decimal Places) Significant Effect? (p<.05) (Yes or No) (Yes or No) (Yes or No) (Yes or No) ₁) Compare the SDy's that you have computed above. Among them, (1) which SDy do you think provides the most precise estimate? (2) why? "I think the best estimates of SDy for managers and assistant managers are from (please check): I Question 3a Question 3d Question 3g k) "I have chosen the SDy above because..." (briefly state with 100 words maximum):See Answer
Q17:Q1. Analyzing the Performance Data of Mr. Macky's. Compute the correlation for all years of data (so, include all data from 2016 to 2022) Number of observations on which your analyses are based: i. ii. Fill in the correlations in the table Performance Rating (Manager) 1 Manager's Performance Rating Scores Assistant Manager's Performance Rating (or average of ratings) Store Market (1-Large, 0-Medium) Unit Sales r= r= r= Performance Rating (Asst. Manager) r= r= 1 Store Market Unit Sales (1=Large) r= 1 1/nX= Performance Rating of Managers (2022) Number of Observations in Regression Model 1: d) Regression Model 2 Y=Unit sales (2022) X= Performance Rating of Assistant Managers (2022) Number of Observations in Regression Model 2: e) Regression Model 3 Y=Unit sales (2022) X1= Performance Rating of Managers (2022) X2= Performance Rating of Assistant Managers (2022) f) Regression Model 4 Y=Unit sales (2022) Coefficients Number of Observations in Regression Model 3: X1= Performance Rating of Managers (2022) X2= Store Market Coefficients g) Regression Model 5 Y=Unit sales (2022) Coefficients Number of Observations in Regression Model 4: Coefficients (Use 4 Decimal Places) P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) P-value Module 2 Answers: Page 9 (p<.05) (Yes or No) Significant Effect? (p<.05) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) Significant Effect? (p<.05)/nX1= Performance Rating of Assistant Managers (2022) X2= Store Market Number of Observations in Regression Model 5: h) Regression Model 6 Y=Unit sales (2022) X1= Performance Rating of Managers (2022) X2= Performance Rating of Assistant Managers (2022) X3= Store Market Number of Observations in Regression Model 6: i) Regression Model 7 Y=Unit sales (all years) Coefficients j) Regression Model 8 Y=Unit sales (all years) Coefficients X1= Performance Rating of Managers (all years) X2= Store Market Number of Observations in Regression Model 7: Coefficients X1= Performance Rating of Assistant Managers (all years) X2= Store Market Number of Observations in Regression Model 8: (Use Decimal Places) P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) Module 2 Answers: Page 10 (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No)/nk) Regression Model 9 Y=Unit sales (all years) X1= Performance Rating of Managers (all years) X2= Performance Rating of Assistant Managers (all years) X3= Store Market Number of Observations in Regression Model 9: 1) Regression Model 10 Y=One-Year Change in Unit sales (for all possible years) Xl-Performance Rating of Managers X2= Performance Rating of Assistant Managers X3= Store Market m) Regression Model 11 Y=Unit sales (all years except 2016) Coefficients Number of Observations in Regression Model 10: X1= Performance Rating of Managers X2= Performance Rating of Assistant Managers X3= Store Market X4=Prior Unit sales (from prior year) Coefficients Coefficients Number of Observations in Regression Model 11: P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) P-value (Use 4 Decimal Places) Module 2 Answers: Page 12 Significant Effect? (p<.05) (Yes or No) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) (Yes or No) Significant Effect? (p<.05) (Yes or No) (Yes or No) (Yes or No) (Yes or No)/nn) To answer this question, (1) identify which model (preferably one model, but few more are okay if you want to make any comparison) you will bring and present to the Head of HR Analytics, (2) briefly interpret your finding(s) and takeaway(s) from the model you picked, and (3) justify why you think it is better than any other regression models. Please concisely make your point in a plain language for the Head of HR department using no more than 200 words at maximum. Answer:See Answer
Q18:Q6. Summary Information: Manager's Pay and Performance a) Provide the descriptive statistics for wages of currently employed managers and assistant managers in 2022. Managers' Wage Job Title Mean Standard Deviation Range Minimum Maximum Number of Observations b) For the previous three years, what are the means and standard deviations of wage rate and performance rating scores for ALL (both employed and no-longer employed) managers and assistant managers? (Note that Assistant Managers can be promoted to Managers, so make sure you are using the right job title in the right year.) Wage Rate (S) Performance Rating* Job Title Year Mean Assistant 2020 Manager 2021 2022 Manager 2020 2021 Assistant Managers' Wage 2022 Standard Deviation Mean Standard DeviationSee Answer
Q19:Q5. Tenure a) Look at current employees. What is the average tenure of the following employees? Employee Group Average Tenure (in years) (Use two decimal places) All employees Assistant Managers Managers b) Which store unit has the highest average tenure in 2022? Unit # Average tenure in this unit in 2022: c) Which store unit has the low est average tenure in 2022? Unit # Average tenure in this unit in 2022:See Answer
Q20:Q4. Turnover a) Using the Past and Current Employees tab, summarize the nature and total amount of turnover in each of the following years: Year 2020 2021 2022 b) What is the overall turnover rate in each year? Unit # 20 40 60 80 100 120 140 Discharged Number of Individuals Employed on January 1, 2022 Year 2020 2021 2022 c) Report the requested information, ultimately so that you can compute the unit turnover rate in 2022, for the stores listed below. Quit Number of Individuals Employed on December 31, 2022 Turnover Rate Total Number Who Left the Company Average 2022 Headcount Number of people who left unit in 2022 (i.e., turnover count) Unit Turnover Rate/nSee Answer

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