1. What is the explanatory variable, and what is the response variable? 2. What are the populations for the F-test? 3. State your hypotheses 4. 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. SampleP 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. A Enter a descriptive title above your table. B. 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. Summary statistics for Sample(IAT-Weight-Score): Group by: Sample(Prefers) Sample(Prefers) n Mean Std. dev. + Strong preference for fat people 2 Moderate preference for 3 Sight preference for fa 4Ukes thin people and t people equally 5 Sight preference for thi people 6 Moderate preference for nin people 7 Strong preference for thi 5 0.53423182 0.26592702 10 0.39895934 0.4163703 23 0.26406347 0.35412012 375 0.368696 0.45337069 208 0.50965906 0.37765583 145 0.57691726 0.37108836 34 0.77391345 0.32384944 5. Conducting the ANOVA F-test at the 5% significance level: If conditions are met, A. Use StatCrunch to conduct the ANOVA F-test (copy and paste the contents of the StatCrunch output window into your response). B. 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 that meets the conditions. If conditions are not met for all categorical variables listed above for your data set, Contact Analysis of Variance results: Responses: Sample(IAT-Weight-Score) Factors: Sample(Prefers) Response statistics by factor Sample(Prefers) ⚫n Mean Std. Dev. Std. Error 1 Strong preference for fat 5 0.53423182 0.26592702 0.11892618 people 2 Moderate preference for ft 10 0.39895934 04163703 0.13166785 people 3 Sight preference for fat 23 0.26406347 0.35412012 0.07383915 people 4 Likes thin people and fat 375 0.368696 0.45337069 0.023411962 people equaly 5 Sight preference for thin 208 0.50965906 0.37765583 0.02618572 people 6 Moderate preference for thig 45 0.57691726 0.37108836 0.030817211 people 7 Strong preference for thin people ANOVA table 34 0.77391345 0.32384944 0.05553972 Source DF MS F-Stat P-value Sample(Prefers) 6 9.9180873 1.6530145 9.7612901 <0.0001 Error 793 134.28968 0.16934386 Total 799 144.20777