Question

In: Statistics and Probability

Select all TRUE statements below. a) A multiple regression analysis can be performed with more than...

Select all TRUE statements below.

a)	A multiple regression analysis can be performed with more than one continuous dependent variable at a time.

b)	In a two-way between-subjects ANOVA, is it possible to have a significant interaction, but not a significant main effect for either factor.

c)	A one-way between-subjects ANOVA cannot be conducted with 5 groups.

d)	The normality assumption of multiple linear regression can be checked by examining a scatterplot of fitted values versus standardized residuals.

e)	The normality assumption underlying between-subjects ANOVA states that the sample mean is normally distributed.

f)	Simple main effects examine whether there are significant differences among marginal means in a two-way between-subjects ANOVA.

g)	In multiple regression analysis, it is possible that a statistical test for the overall model is significant, indicating that there is significant variation in the outcome variable explained by the predictors, but no individual regression coefficient is statistically significant.

h)	If the correlation between X1 and Y is positive, then the coefficient for X1 will be positive if it is included in a multiple regression model in which there are several correlated predictors.

i)	The Fisher Z transformation is used to transform a non-normal dependent variable to meet the assumptions of ANOVA.

j)	If random assignment is used, we can infer that the independent variable caused changes in the dependent variable if ANOVA is used to analyze the data, but not if multiple linear regression is used.

k)	In repeated-measures ANOVA, an F-test can be computed for the between-participant effect.

Expert Solution

The TRUE statements are:

b) In a two-way between-subjects ANOVA, is it possible to have a significant interaction, but not a significant main effect for either factor.

f) In a two-way between-subjects ANOVA, is it possible to have a significant interaction, but not a significant main effect for either factor.

g) In multiple regression analysis, it is possible that a statistical test for the overall model is significant, indicating that there is significant variation in the outcome variable explained by the predictors, but no individual regression coefficient is statistically significant.

j) If random assignment is used, we can infer that the independent variable caused changes in the dependent variable if ANOVA is used to analyze the data, but not if multiple linear regression is used.

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orchestra answered 2 years ago

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