A data contains 17 predictors with DS and DW as categorical variable. X1, X2, X3, X4, X5, DS, DW, X1*DS, X1*DW, X2* DS, X2*DW, X3*DS, X3*DW, X4*DS, X4*DW, X3-squared, X4- squared and X3-squared *DW. Predictor variable X5 is omitted from the model. Which of the following plots should be considered to check for Linearity, Independence, Normal Distribution, and Equal Variance Condition? Select all that apply: ☐ Scatterplot of residuals (vertical) and fitted values (horizontal) ☐ Scatterplot of residuals (vertical) and X₁ (horizontal) ☐ Scatterplot of residuals (vertical) and X2 (horizontal) Scatterplot of residuals (vertical) and X3 (horizontal) ☐ Scatterplot of residuals (vertical) and X4 (horizontal) ☐ Scatterplot of residuals (vertical) and X5 (horizontal) ☐ Scatterplot of residuals (vertical) and Y (horizontal) ☐ Normal probability plot of the residuals Normal probability plot of Y

3 Which scenario would most likely show a negative association between the variables? A the height of a tree, x, and the amount of time it takes to climb to the top of the tree, y B the number of people in the mall, x, and the number of cars in the parking lot, y miles traveled in a car, z, and the amount of gasoline used, y D time spent reading a book, r, and the number of pages left to read, y

How to find independent and defendant variables

Suppose you have access to a database with the variables listed below. Choose two variables you are interested in exploring that may be related (a predictor and outcome). Which variable is your y (outcome) and which is your x (predictor)?  *Hint: which one may predict the other? Example: hot weather may predict sunburns.