What test of significance would you use for this case of bivariate or multivariate analysis? Assume that each variable is measured at its most detailed possible level of measurement. Meaning, for instance, that age is always ratio (0, 1, 2, 3, 4, 5, … infinite), not ordinal (i.e., age categories). College major by gender and race ANOVA Chi-square Regression T-test

see picture for graphic and questions. Explanation The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation =y−41.67 - 0.49x. This line is shown in the scatter plot below.

I need help with these questions  1. do the covariates and factors interact? 2. can you conclude a homogeneity of regression slopes? 3. can you conclude homogeneity of variance?

If we want to compare average charges for males and females, we would use O two sample z- test O single sample t-test O Correlation OTwo sample t-test

Question attached    Bottom question asks Is the mode a useful measure of center for this data set? Yes or no.

Choose correct option.. And explain all question. Ty

When there are omitted variables in your regression, then... Group of answer choices This has no effect on the estimation of the explanatory variable because the variable is omitted This will always bias the OLS estimation of the explanatory variable the estimation of the explanatory variable(s) is unaffected The OLS estimation is biased if the omitted variables are correlated with the included variable

After performing an ANOVA analysis, which of the following tests are performed in order to determine whether the means of specific categories are significantly different from each other?      Goodness-of-fit     Linear regression     Bonferonni      Comparing proportions