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(9) Weight IAT Variables Quantitative Variable IAT-Weight-Score: Score on the Weight IAT Categorical Variable (choose one) Race: 1=American Indian/Alaska Native; 2=East Asian; 3=South Asian; 4=Native Hawaiian or other Pacific Islander; 5=Black or African American; 6=White; 7=Other or Unknown; 8=Multiracial Political-ID:  1=strongly conservative; 2=moderately conservative; 3=slightly conservative; 4=neutral; 5=slightly liberal; 6=moderately liberal; 7=strongly liberal Religiosity:   1=Not at all; 2=Slightly; 3=Moderately; 4=Very; 5=Extremely Prefers:   Subject reports: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people Most-Prefer:   Subject’s perception of what most people prefer: 1=Strong preference for fat people; 2=Moderate preference for fat people; 3=Slight preference for fat people; 4=Likes thin people and fat people equally; 5=Slight preference for thin people; 6=Moderate preference for thin people; 7=Strong preference for thin people Body-Image:   Subject’s reported body image: 1=Very underweight; 2=Moderately underweight; 3=Slightly underweight; 4=Neither underweight nor overweight; 5=Slightly overweight; 6=Moderately overweight; 7=Very overweight Important:  Importance of weight to subject’s sense of self: 1=Not at all important; 2=Moderately unimportant; 3=Somewhat unimportant; 4=Neither unimportant nor important; 5=Somewhat important; 6=Moderately important; 7=Very important Weight IAT variable descriptions (opens in a new tab). Prompt Work through each of the following items to conduct an ANOVA F-test using the variables listed above for your unique IAT sample. What is the explanatory variable, and what is the response variable? What are the populations for the F-test? State your hypotheses. Create side-by-side (or stacked) boxplots for the quantitative variable (IAT Score) grouped by your chosen categorical variable. Select the option to display the mean within the boxplots (directions) . Download the StatCrunch output window (your boxplots) and embed the .png file with your response. Do the boxplots suggest that the samples come from populations with different means? Briefly explain. Next, we need to create a table with these summary statistics: sample size, mean, and standard deviation for each of the populations you listed above. To do this, use StatCrunch to create a table of the indicated summary statistics for the quantitative variable (IAT Score) grouped by your chosen categorical variable. The summary statistics should be listed in the order given with no other statistics in your table. Copy the table in the StatCrunch output window and paste it into your response. To make your table readily understood by any reader, complete each of the following. Enter a descriptive title above your table. In your table, each group from your chosen categorical variable is labeled with a number. A reader will not understand what the number represents. Replace the numeric labels with descriptive words for each group of your selected categorical variable (see the Variables section above for your data set). Determine whether conditions are met to use the ANOVA F-test. For each condition explain why the condition is met or not met. Conducting the ANOVA F-test at the 5% significance level: If conditions are met, use StatCrunch to conduct the ANOVA F-test (copy and paste the contents of the StatCrunch output window into your response). Identify the F-statistic and the P-value. Then state your conclusion in context. If conditions are not met for your selected categorical variable, start over and use the list of categorical variables provided in the Variables section above to select a different categorical variable for which conditions are met.

Assignment: AssessmentThe testing of assumptions, recognition of limitations, and proper use of diagnostics are all necessary elements in the use of multiple linear regression for public health research. All of these elements allow biostatisticians to better assess the results of multiple linear regression models.For this Assignment, you test that assumptions for multiple linear regression have been met, use SPSS to create a multiple linear regression, evaluate results to determine whether the model is appropriate, and finally interpret the relationships uncovered through this statistical test between the independent and dependent variables. Use the Week 4 Dataset (SPSS document) from the Learning Resources area to complete this assignment.The AssignmentExplain the assumptions of Linearity, Sampling independence, Normality, and Homoscedasticity (or equal variance). (30 points)How would you test whether these have been met? (Note: for the exam you do not need to test these assumptions)Using SPSS, test the assumption of Linearity between the independent and dependent variables.Using SPSS, test the assumption of Normality for the dependent variable.Conduct a multiple linear regression using SPSS. Provide relevant SPSS output and assess the statistical significance of the effects of mother’s Age, BMI, and Coffee (Cups per Day) on Birth weight. (30 points)Explain the practical implications of your finding. Include a reference to the R square of the model in your discussion. (20 points)Discuss whether or not there is interaction (effect modification) first between Age and BMI and second between BMI and Coffee. (20 points)