The key statistics reported in a regression output, which are critical for business uses are:
R - Coefficient of Correlation: The correlation between the predicted and the actual value of dependent variable
R square - Coefficient of Determination: The amount of variation in the dependent variable explained by the independent variable
Standard Error: This signifies the standard error between the actual and predicted values in the regression model. If the error is high, the model needs to be investigated carefully.
ANOVA Table: The ...
This posting contains answers to following questions on output of regresion.
Basic Business Statistics regression and correlation
1. In a simple linear regression problem, r and b1:
a. may have opposite signs.
b. must have the same sign.
c. must hae opposite signs.
d. are equal
2. A professor of industrial relations believes that an individual's wage rae at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:
Employee Y($) X1 X2
1 10 3 0
2 12 1 5
3 15 8 1
4 17 5 8
5 20 7 12
6 25 10 9
Referring to the table, for these data, what is the estimatd coefficient for performance rating, b1?
3. In a multiple regression problem involving two independent variables, if b1 is computed to be +2.0, it means that:
a. the relationship between X1 and Y is significant.
b. the estimated average of Y increases by 2 units for each increase of 1 unit of X1, holding X2 constant.
c. the estimated average of Y increases by 2 units for each increase of 1 unit of X1, without regard to X2.
d. the estimated average of Y is 2 when X1 equals zero.
4. If the correlation coefficient (r)=1.00, then:
a. the y-intercept (b0) must equal 0.
b. the explained variation equals the unexplained variation.
c. there is no unexplained variation.
d. there is no explained variation.