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
The right answer is D, as Adjusted R square is adjusted for the degrees of freedom and explains the variation in dependent variable explained by the independent variables. On the other hand, correlation just measures how the two variables are related to each other and it is not among the good measure of goodness of fit.
When evaluating a multiple regression model, for example when we regress dependent variable Y on two...
Consider the multiple regression model shown next between the dependent variable Y and four independent variables X1, X2, X3, and X4, which result in the following function: Y = 33 + 8X1 – 6X2 + 16X3 + 18X4 For this multiple regression model, there were 35 observations: SSR= 1,400 and SSE = 600. Assume a 0.01 significance level. What is the predictions for Y if: X1 = 1, X2 = 2, X3 = 3, X4 = 0
Once the dependent variable is determined when building a bivariate or multiple-regression model, what is the next step? Multiple Choice Determine what factors contribute to the change in the dependent variable. Define the data series for the model. Specify the correlation between the dependent variables. Identify the other dependent variables.
Consider a multiple regression model of the dependent variable y on independent variables x1, X2, X3, and x4: Using data with n 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: y0.35 0.58x1 + 0.45x2-0.25x3 - 0.10x4 He would like to conduct significance tests for a multiple regression relationship. He uses the F test to determine whether a significant relationship exists between the dependent variable and He uses the t...
4. Testing for significance Aa Aa Consider a multiple regression model of the dependent variable y on independent variables x1, x2, X3, and x4: Using data with n = 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: 0.04 + 0.28X1 + 0.84X2-0.06x3 + 0.14x4 y She would like to conduct significance tests for a multiple regression relationship. She uses the F test to determine whether a significant relationship exists...
The β 1 term indicates a. the Y value for a given value of X. b. the average change in Y for a unit change in X. c. the Y value when X equals zero. d. the change in observed X for a given change in Y. What does regression analysis attempt to establish? a. linearity in the relationship between independent variables b. a mathematical relationship between a dependent variable, for which future values will be forecast, and one or...
1. In order to test whether the multiple linear regression model y bo +b,x1 + b2X2 is better than the average model (lazy model), which of the following null hypotheses is correct: a. Ho' b1 = b2 = 0 Но: B1 B2-0 с. We have a dataset Company with three variables: Sales, employees and stores. To build a multiple linear regression model using Sales as dependent variable, number of stores and number of employees as independent variables, which of the...
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...
Suppose we have the following values for a dependent variable, Y, and three independent variables, X1, X2, and X3. The variable X3 is a dummy variable where 1 = male and 2 = female: X1 X2 X3 Y 0 40 1 30 0 50 0 10 2 20 0 40 2 50 1 50 4 90 0 60 4 60 0 70 4 70 1 80 4 40 1 90 6 40 0 70 6 50 1 90 8 80 ...
Applying Simple Linear Regression to Your favorite Data. Many dependent variables in business serve as the subjects of regression modeling efforts. We list such variables here: Rate of return of a stock Annual unemployment rate Grade point average of an accounting student Gross domestic product of a country Salary cap space available for your favorite NFL team Choose one of these dependent variables, or choose some other dependent variable, for which you want to construct a prediction model. There may...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...