Simple regression

Question 1

Simple regression

Answer 1

• how well predictor value predicts outconr

- measures how one unit increase in X impacts change in Y

Question 2

Equation

Answer 2

Y=b0 + b1X1 + error

(perfect line) + error

Question 3

Regression line

Answer 3

• Used to predict the best fitting straight line through the data by minimizing distance between all data points
• tells us slope (direction and degree) and intercept (line intercepts Y axis)
• better than using mean

Question 4

Output

Answer 4

• model summary: R and R2 (0-1)
• coefficients table: beta values (model parameters)
• ANOVA: model fit (SST3 = SSM1 (variance explained) + SSR2 (error in model))

Question 5

model is greater than mean if

Answer 5

SSM is greater than SSR (error)

Question 6

Statistics

Answer 6

• Correlation coefficient (R): standardized measure of relationship strength (0-+1)
• Coefficient of determination (R2): proportion of variance explained by model
• Beta coefficient (b1): slope (change in outcome for one unit change in predictor
• Intercept (b0): what is Y when X = 0