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What are the three aspects of statistical relationships discussed in correlation?
Form, strength, and direction.
What does the linear form of a relationship mean?
A straight line (regression line) can represent how the variables relate.
What statistic quantifies the strength of a linear relationship?
Pearson product-moment correlation coefficient (r).
What does the sign (+/−) of the correlation coefficient indicate?
The direction of the relationship (positive or negative), not its strength.
What does a positive relationship look like on a scatterplot?
Points slope upward from left to right.
What does a negative relationship look like on a scatterplot?
Points slope downward from left to right.
What is regression used for?
Prediction — predicting values of one variable (Y) from another (X).
What are the two components of a regression line?
Slope (β₁) and intercept (β₀).
What does the slope (β₁) represent in regression?
The expected change in Y for a one-unit increase in X.
What does the intercept (β₀) represent?
The predicted value of Y when X = 0.
What is the formula for the simple linear regression model?
Yi=β0+β1Xi+εi
What is the predicted value formula in regression?
Y^i=β0+β1Xi
What is a residual (εᵢ) in regression?
The difference between actual and predicted Y values: Yi−Y^iYi−Y^i
How do you calculate the slope (β₁)?
β1=Scp ∑(Xi−Xˉ)(Yi−Yˉ)
———- = ————————
SsX ∑(Xi−Xˉ)2
How do you calculate the intercept (β₀)?
β0=Yˉ−β1Xˉ
What does model fit mean in regression?
How well the regression model represents the actual data.
What two methods are used to assess regression model fit?
ANOVA and R² (coefficient of determination).
What does R² represent?
The proportion of total variance in Y explained by the regression model.
How do you calculate R²?
R2=SSREG
——————-
SSTOTAL
Or square the correlation r if only one predictor is used.
What is the formula for the F-ratio in regression ANOVA?
F=MSREG SSREG/dfREG
————— = ———————
MSRES SSRES/dfRES
How are the degrees of freedom partitioned in regression?
dfTOTAL=N−1
dfREG=K
dfRES=N−K−1
Where K = number of predictors.