BUSN 4000 Exam 2 CORRECT 100%
Outliers - ANSWER Extreme Y value
How to detect: Standard residuals +-2
Leverage Points - ANSWER Extreme X value
How to detect: Highest leverage values from SmartREG
Influential Observations - ANSWER An observation that has a strong influence on the
regression model
How to detect: Highest Cook's D or DFFITS
Model Assumptions - ANSWER 1. That the relationship is linear
2. That the variance is constant
3. That the errors/disturbances are normally distributed
This is because average or mean values are always more stable than individual values.
Confidence interval is for the prediction of an average/mean value, whereas prediction
interval is for the prediction of a single value.
Coefficient of Determination - ANSWER R-squared
R^2= SSR/SST
the proportion of variance in Y that is predicted by our model
t-test statistic when testing a specific value - ANSWER b1-B1/sb1
MSR - ANSWER MSR=SSR/k
MSE - ANSWER MSE=SSE/n-k-1
Constructing a confidence interval - ANSWER (b1-t*x sb1, b1+t*x sb1)
where:
b1 = regression coefficient
t* = T.INV((1-% confidence we are testing/2), residual df)
SSE - ANSWER Sum Square due to Error
sum(y-yhat)^2
Cross-Sectional Data - ANSWER data on a number of different analysis units taken at a
single point in time
Full F-Test input in excel - ANSWER F.DIST(k, n-k-1, F)
t-Test input in excel - ANSWER T.DIST(-t-stat, n-k-1, TRUE) x 2
Outliers - ANSWER Extreme Y value
How to detect: Standard residuals +-2
Leverage Points - ANSWER Extreme X value
How to detect: Highest leverage values from SmartREG
Influential Observations - ANSWER An observation that has a strong influence on the
regression model
How to detect: Highest Cook's D or DFFITS
Model Assumptions - ANSWER 1. That the relationship is linear
2. That the variance is constant
3. That the errors/disturbances are normally distributed
This is because average or mean values are always more stable than individual values.
Confidence interval is for the prediction of an average/mean value, whereas prediction
interval is for the prediction of a single value.
Coefficient of Determination - ANSWER R-squared
R^2= SSR/SST
the proportion of variance in Y that is predicted by our model
t-test statistic when testing a specific value - ANSWER b1-B1/sb1
MSR - ANSWER MSR=SSR/k
MSE - ANSWER MSE=SSE/n-k-1
Constructing a confidence interval - ANSWER (b1-t*x sb1, b1+t*x sb1)
where:
b1 = regression coefficient
t* = T.INV((1-% confidence we are testing/2), residual df)
SSE - ANSWER Sum Square due to Error
sum(y-yhat)^2
Cross-Sectional Data - ANSWER data on a number of different analysis units taken at a
single point in time
Full F-Test input in excel - ANSWER F.DIST(k, n-k-1, F)
t-Test input in excel - ANSWER T.DIST(-t-stat, n-k-1, TRUE) x 2
