If the regression slopes of the dependent variable (Y) on the
covariate (X) are substantially different across the groups, one
would expect that
a. the adjusted means will be biased.
b. Y is independent of X.
c. the factor is not independent of X.
d. there is a modest effect with equal n's in quasi-experiment.
If the regression slopes of the dependent variable (Y) on the covariate (X) are substantially different...
If the regression slopes of the dependent variable (Y) on the covariate (X) are substantially different across the groups, one would expect that a. the adjusted means will be biased. b. Y is independent of X. c. the factor is not independent of X. d. there is a modest effect with equal n's in quasi-experiment.
In ANCOVA, suppose Y is the dependent variable, X is the covariate, and the factor has two levels. Also all assumptions of ANCOVA are met. Which of the following situations is the most desirable? a. rXY = 0.5; x̅₁=10, x̅₂=12, x̅ =11. b. rXY = 0.1; x̅₁=10, x̅₂=12, x̅ =11. c. rXY = -0.1; x̅₁=10, x̅₂=20, x̅ =15. d. rXY = -0.5; x̅₁=10, x̅₂.=20, x̅ =15.
The equation of the regression line between two variables x (independent variable) and y (dependent variable) is given by y-hat = -3x + 2; and the correlation coefficient is r = -.95. The possible x-values range from 1 to 10. Which of the following statements are correct? I. The variable y is strongly positive correlated to the variable x. II. The variable y is strongly negative correlated to the variable x. III. If x = 5, one would predict that...
In multiple regression, if I wanted to determine the effect on the dependent variable of a one unit increase in one independent variable, not if all other independent variables are held constant but for basically the value of the dependent variable after I fill out the regression equation with all of the estimated coefficients, how do I go about it? For example, if I wanted the effect of a one percent increase in x1 on the earnings of a 30...
. Write a regression equation for annual salary where y is the dependent variable (salary) and x is the independent variable (MONTHS of education) assuming you receive no fixed salary and you have 5 years of education
Given the regression line y=-1.32+2.342xy = − 1.32 + 2.342 x , where the independent variable is number of dollars ($) invested and the dependent variable is the time (hours) spend working. a) What is the y-intercept of the regression line and interpret what it means? b) If you spent $20 in an investment, how many hours did you work?
A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). Σx = 90 Σ(y - )(x - ) = 466 Σy = 170 Σ(x - )2 = 234 n = 10 Σ(y - )2 = 1434 The F-statistic is Question 37 options: a18.341 b1.834 c14.673 dNone is correct.
Dummy Variable Regression: Choose any metric variable as the dependent variable (you can use the same one that you used in Part A) and choose gender as an independent variable. Also choose one more metric variable as an additional independent variable. Once again, however, you must sort through the metric independent variables until you find one that, along with gender, produces a significant F-calc. Use alpha = .05 here as well. You only need to report the model that produced...
Find the least squares regression line for the data points. (Let x be the independent variable and y be the dependent variable.) Graph the points and the line on the same set of axes 3 -3 3 -3 Find the least squares regression line for the data points. (Let x be the independent variable and y be the dependent variable.) Graph the points and the line on the same set of axes 3 -3 3 -3
When evaluating a multiple regression model, for example when we regress dependent variable Y on two independent variables X1 and X2, a commonly used goodness of fit measure is: A. Correlation between Y and X1 B. Correlation between Y and X2 C. Correlation between X1 and X2 D. Adjusted-R2 E. None of the above