(a) Briefly discuss one-way Analysis of Variance (ANOVA). (b) Obtain the mean for each technique and ANOVA table output using SPSS. (c) Test whether the effectiveness of these techniques is different. Use level of significance of 1%. (d) Briefly discuss the implication of the result in part (c).

SPSS Data Entry, Data Dictionary, Frequency Procedure Assignment Overview The public health professional should have a basic understanding and familiarity with statistical and analytic software. Correct data entry and variable naming and labeling are crucial aspects of data management and analysis.

When should you use a two-way chi-square test to analyze data? When should you use an independent t-test to analyze data? Outline an example of a study that would use a two-way chi-square design and explain why this is the right test.

Fit a binary logistic regression model with admission decision as the dependent variable, GRE and GPA as the independent variables. Need to use SPSS Evaluate the goodness of fit of the model. Determine the significance of independent variables. Interpret odds ratios for independent variables. State the binary logistic regression equation. Evaluate the classification accuracy of the model. Check if the residuals are independent. Submission Details: Submit a 3-4 page Microsoft Word document, using APA style. The relevant SPSS output should be copied to this document.

Question 1 Using the data in Section A to answer this question. (a) Use two sample independent t-test to assess whether there is difference in means of amount of body fat between different genders. (i) State the null and alternative hypothesis. (ii) Show the t-statistics output and identify the value of test statistics. (iii) Find the critical value. (iv) Interpret the result using (ii) and (iii). (v) Identify the p value from the output in (ii). (vi) Interpret the result using (v). (vii) Draw conclusion based on (iv) and (vi). (viii) Identify the confidence interval based on the output. (b) The researcher wants to identify the quantitative variables which are significant predictors (root causes) of amount of body fat. Carry out hypothesis test using multiple linear regression. Your SPSS analysis result outputs, interpretation and conclusion should cover: Model Summary, ANOVA table and Coefficient of Regression table. (i) For model summary: Show your SPSS outputs; Interpret the meaning of R-square. (ii) For ANOVA table: Show your SPSS outputs; Write down the relevant hypotheses; State the conclusion to make. (iii) For Coefficient of Regression table: Show your SPSS outputs; Write down the respective hypotheses; State the respective conclusion to make. (iv) From your SPSS results obtained in part (a), rank in order of importance the three causes of amount of body fat.

Tasks: Examine if the given data is suitable for the application of linear discriminant analysis. Create a linear discriminant function predicting admission decisions. Comment on the classification accuracy. Predict the admission decision given GRE score = 690 and GPA = 3.2. Perform logistic regression analysis for the data. Compare the classification accuracies of both methods. Submission Details: Submit a 3-4 page Microsoft Word document, using APA style. The relevant SPSS output should be copied to this document.

I recently went around with a friend at a convention and asked 150 random people one of two questions. I either asked them if they would believe that half of the people in Maryland smoke marijuana OR if they would believe that 58.6% of people in Maryland smoke marijuana. Using the differences in the presentation of the information to our populous, determine if people are more likely to believe data that is more specific, or that is general based.

Using this data, are people who use listerine more likely to have visited haunted houses than those who have not used listerine? In your write-up, be sure to include percentages and statistics in APA format!

Based on this data, are girls significantly more likely than boys to have left out cookies for Santa in 2016? In your write-up, be sure to mention the percentage of boys in this sample who left out cookies, as well as the percentage of girls in this sample who have left out cookies.

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