Linear Regression
A quick recap
Made by @classy
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1: Which residual plot shows homoscedasticity?
30s
Plot A
Plot B
2: Which plot shows LOWER OLS variance?
30s
Plot A
Plot B
Both show the SAME OLS variance
Can't be answered with this plots
3: Which variables is not significant? (Hint: alpha-value=0.05)
30s
x1
x2
x3
x4
4: What is the correct interpretation of R-squared = 0.9637?
30s
96.37% of the data points fall on the regression line
There is a 96.37% probability that X influences Y
96.37% of the variance in Y is explained by the regression model
The model makes correct predictions 96.37% of the time
5: Comparing the two regression models below, which model is BETTER?
35s
Model 1
Model 2
6: What is t hedifference between R-squared and ADJUSTED R-squared?
30s
-squared measures linearity, Adjusted R-squared measures non-linearity
R-squared measures prediction accuracy, Adjusted R-squared measures causality
R-squared always increases with more variables, Adjusted R-squared penalizes unnecessary complexity
R-squared is for small samples, Adjusted R-squared is for large samples
7: What is heteroskedasticity?
30s
When residuals are not normally distributed
When predictors are correlated with each other
When the variance of residuals is not constant
When the regression line is curved
8: What is the opposite of heteroskedasticity?
30s
Multicollinearity
Homoskedasticity
Autocorrelation
Normality
9: Based on the regression output below, which is the correct final model description? (Hint: alpha-value=0.05)
30s
y = 13.271 + 2.094*x1 + 0.182*x2 - 1.868*x3 + 0.041*x4
y = 13.271 + 2.094*x1 - 1.868*x3
y = 13.271 + 2.094*x1 + 0.182*x2 - 1.868*x3
y = 2.094*x1 + 0.182*x2 - 1.868*x3
10: Which plot shows a negative linear relationship?
30s