1. From a univariate linear regression this plot is produced. What can we conclude
about the relationship between x and y from this graph?
a. It is strong with a large sample size
b. It does not violate the assumption of linearity
c. Both of these are correct
d. Neither of these are correct
2. You perform a univariate regression analysis and find an R-value of 0.85. What can
you conclude about your data?
a. The relation between the outcome and the predictor is strongly linear.
b. The regression model offers a good fit for your data.
c. Your model explains 85% of the variation in Y.
d. Neither of these are correct.
3. The outlier in this scatterplot will have a:
a. large standardized residual; low Mahalanobis; moderate Cook’s distance
b. moderate standardized residual; high Mahalanobis; low Cook’s distance
c. large standardized residual; high Mahalanobis; high Cook’s distance
4. When your regression model has a lack of autocorrelation this means that:
a. You do not have a problem regarding multicollinearity.
b. Any two observations in your data have uncorrelated residual terms.
c. You cannot run a linear regression analysis.
, 5. A Cohen’s d value of -1.1 indicates:
a. A very small effect-size
b. A very large negative effect-size
c. That something went wrong in your calculations
6. I. effect sizes are heavily influenced by sample size
II. p-values are heavily influenced by sample size
a. I is true
b. II is true
c. Neither are true
d. Both are true
7. Considering the above graph from G*power, we can conclude that:
a. The power of this analysis is large
b. The type I error of this analysis is large
c. The analysis is two-sided
d. The effect size is large
8. From this SPSS output we can conclude that:
a. The expected increase in GPA with 1 standard deviation increase in high school
math grade, while keeping the other predictors constant, is 0.354.
b. The unique contribution to the prediction of GPA by math grade, while keeping
the other predictors constant, is 0.354.
c. Neither are correct
d. Both are correct
about the relationship between x and y from this graph?
a. It is strong with a large sample size
b. It does not violate the assumption of linearity
c. Both of these are correct
d. Neither of these are correct
2. You perform a univariate regression analysis and find an R-value of 0.85. What can
you conclude about your data?
a. The relation between the outcome and the predictor is strongly linear.
b. The regression model offers a good fit for your data.
c. Your model explains 85% of the variation in Y.
d. Neither of these are correct.
3. The outlier in this scatterplot will have a:
a. large standardized residual; low Mahalanobis; moderate Cook’s distance
b. moderate standardized residual; high Mahalanobis; low Cook’s distance
c. large standardized residual; high Mahalanobis; high Cook’s distance
4. When your regression model has a lack of autocorrelation this means that:
a. You do not have a problem regarding multicollinearity.
b. Any two observations in your data have uncorrelated residual terms.
c. You cannot run a linear regression analysis.
, 5. A Cohen’s d value of -1.1 indicates:
a. A very small effect-size
b. A very large negative effect-size
c. That something went wrong in your calculations
6. I. effect sizes are heavily influenced by sample size
II. p-values are heavily influenced by sample size
a. I is true
b. II is true
c. Neither are true
d. Both are true
7. Considering the above graph from G*power, we can conclude that:
a. The power of this analysis is large
b. The type I error of this analysis is large
c. The analysis is two-sided
d. The effect size is large
8. From this SPSS output we can conclude that:
a. The expected increase in GPA with 1 standard deviation increase in high school
math grade, while keeping the other predictors constant, is 0.354.
b. The unique contribution to the prediction of GPA by math grade, while keeping
the other predictors constant, is 0.354.
c. Neither are correct
d. Both are correct
