PLease answer these

Question 36 (2 points) According to the Boxplot figures provided in this case, which of the following conditions for two-way ANOVA are met? C] The boxplot of salary vs. industry appears symmetric. C] Since the executives were randomly selected, therefore the independence assumption holds. C] Boxplots of salary both industry sector and gender appear to satisfy the equal variance assumption. C] The executives were randomly selected. Question 28 (3 points) Which of the following statements is/are correct? You can select one or more. [3 There is no interaction between Factors A and B when the data values for each value of one factor behave in the same fashion across all levels of the other factor. C] In a twoway ANOVA procedure, the results of the hypothesis test for FactorA and Factor B are only reliable when the hypothesis test for the interaction of Factors A and B is statistically insignificant. C] The number of cells in a two-way ANOVA procedure is equal to the number of levels in FactorA minus one multiplied by the number of levels of Factor B minus one. D The degrees of freedom for the sum of squares between for one-way ANOVA equal the number of populations being compared minus one. [3 In a twoway ANOVA procedure, there are two hypotheses to be tested ~ the test for factor A and the test for factor B. Question 29 (1 point) The number of replications for the two~way ANOVA procedure above is: An advocacy group is interested in determining if gender (1 = Female, 2 = Male) affects executive level salaries. To this end, they took a random sample of executives in three different industries (1 = Consumer Goods, 2 = Financial, 3 = Health Care) and collected salary data. A partial two-way ANOVA table summarizing the results is provided below. Two-way ANOVA: Salary versus Industry. Gender Source DF 38 HE P P Industry 2 23342.6 Gender 1 4915.2 Interaction 2 880.2 440.1 Error 24 10794.0 449.? Total 29 39932.0 Question 30 (1 point) The correct null hypothesis for Factor A is: 0 Gender diversity affects executive salaries. O Mean executive salaries are equal across industry sectors. 0 Exactly two mean executive salaries are equal, and one is different. O Mean executive salaries are not all equal across the different industry sectors. Question 31 (1 point) Which of the following statements is true for the case study presented in the question above? 9 This is a randomized block design. 0 This is an experimental study involving a randomized block design. O This is a completely randomized design. O This is an experiment. \"D This is an observational study. Question 32 (1 point) According to the two-way ANOVA table above, what is the value of the standard error associated with this analysis? Using the Bonferroni method with a family confidence level of 95% and the ANOVA table below, answer the following questions: 0.2306 0.0256 0.44 0.9121 144 8.4087 0.0533- 159 25.3067 Question 38 (2 points) Considering the table provided in the previous question, and to compare the treatment means (treatment combinations), what is the number of observations that comprise each treatment mean? Question 39 (1 point) Assuming that the critical value associated with the Bonferroni approach is 3.61, then the value of the corresponding margin of error for comparing treatment combinations (rounded to two decimal places) is: '/ Question 40 (1 point) Assuming a Bonferroni margin of error of 83.66, this is regardless of your answer above, is the difference between female executive level salaries in the the Healthcare industry and male executive level salaries in the Financial industry significant? Why? Consider the mean salary values provided in the table below. Industry Female Male Healthcare 134.75 62.25 Financial 150.25 108.25 Consumer goods 142.50 142.00 O No, because their mean difference is less than the margin of error. 0 Yes, because their mean difference is greater than the margin of error. 0 Yes, because their mean difference is less than the margin of error. 0 No, because their mean difference is greater than the margin of error. Question 33 (2 points) The F-statistic value and the pvalue for factor Gender are: 0 F-statistic = 10.93, pvalue = 0.003. O F-statistic = 25.95, p-value = 0.000. O F-statistic = 5.464, p-value = 0.01. O Fstatistic = 0.98, pvalue = 0.390. Question 34 (1 point) At 0: = 0.05, what would it be the conclusion from testing the significance of the interaction term? Hint: you should complete the ANOVA table first and then select a right conclusion. 0 That mean executive salaries are not the same across the three different industry sectors and that mean executive salaries are not the same for males and females. 0 That there is a significant interaction effect. 0 That mean executive salaries are the same across the three different industry sectors. 0 That mean executive salaries are the same for males and females. 0 That the pvalue for the interaction term = 0.390 > 0.05. Thus, it is not appropriate to interpret the main effects separately. Question 35 (1 point] It is appropriate to interpret the main effects separately in this case since the pvalue for the interaction term indicates that it is not significant. Look at the interaction plots below and answer the following questions by selecting either True or False. Interaction Plot for Executive Level Salaries Data Mews-impart Fiance Heal-are Gender + Female l Male Industry + Consumer goods l Finance - + - Healthcare Femab Male Question 41 (1 point) The response to one factor seems to be "independent" of the other factor. This is because the changes in one factor translate into approximately the same average change in the executive level salaries regardless of the level of the other factor. Question 42 (1 point) If the interaction between Gender and industry is significant, we can only talk about the effect of Gender or Industry at a specific level of the other factor. Question 37 (1 point) Regardless of your answer to the previous questions, consider the following plot of the residuals vs. predicted values for the analysis above. 2.5 +Hit + +4+ Residuals + -2.5 + -5.0 12.5 25.0 37.5 50.0 Predicted Values The residual plot shows no increase in variance, which makes the analysis of avocacy case adequate. True False