BUAL 2650 EXAM 2 QUESTIONS & ANSWERS
1. In a simple linear regression analysis, the correlation coefficient (r) and the
slope (b) ...: always have the same sign
2. characteristics off error terms: - independent
- normally distributed
- mean of 0
- have a constant variance
3. correlation coefficient is always: between -1 and 1
4. If p<alpha, then: you reject the null hypothesis, not significant
5. SSE: residual sum of squares (unexplained)
6. SSR: regression Sum of Squares (Explained Variation)
7. t-test: test for individual significance
8. f-test: test for overall significance
9. in a simple regression analysis, the quantity that gives the amount by which
the Y (dependent variable) changes for a unit change in X (independent vari-
able) is called the...: slope of the regression line
10. you want r^2 to be...: as close to 1 as possible
11. you want SE (standard error) to be...: small
12. correlation coefficient: measures the strength of the linear relationship between the dependent variable
and the independent variable
13. if the correlation coefficient is -1: it is perfectly negative
14. if the correlation coefficient is 1: it is perfectly positive
15. multiple regression model: has multiple variables
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1. In a simple linear regression analysis, the correlation coefficient (r) and the
slope (b) ...: always have the same sign
2. characteristics off error terms: - independent
- normally distributed
- mean of 0
- have a constant variance
3. correlation coefficient is always: between -1 and 1
4. If p<alpha, then: you reject the null hypothesis, not significant
5. SSE: residual sum of squares (unexplained)
6. SSR: regression Sum of Squares (Explained Variation)
7. t-test: test for individual significance
8. f-test: test for overall significance
9. in a simple regression analysis, the quantity that gives the amount by which
the Y (dependent variable) changes for a unit change in X (independent vari-
able) is called the...: slope of the regression line
10. you want r^2 to be...: as close to 1 as possible
11. you want SE (standard error) to be...: small
12. correlation coefficient: measures the strength of the linear relationship between the dependent variable
and the independent variable
13. if the correlation coefficient is -1: it is perfectly negative
14. if the correlation coefficient is 1: it is perfectly positive
15. multiple regression model: has multiple variables
1/3
