Question 1 (1 point)

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Which one best describes about the difference between simple and multiple regression?

Question 1 options:

	Simple regression has an intercept term while multiple regression does not.
	In simple regression there is a single predictor while in multiple regression there are more than one predictors
	In simple regression the parameter estimates are averages while in multiple regression the parameters are summed over all the predictors
	In simple regression least square is used to estimate the parameters while multiple regression uses maximum likelihood

Question 2 (1 point)

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What does least square minimize? Select all that apply.

Question 2 options:

	The residual sum square
	The difference between the estimated value of the dependent variable and the largest parameter value
	The squared difference between actual and estimated values
	The loss (cost) function

Question 3 (1 point)

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Which of the following can be used as accuracy measures for linear regression? Select all that apply.

Question 3 options:

	Precision & Recall
	Residual Standard Error (RSE)
	R-Squared (R2)
	F2-Score

Question 4 (1 point)

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What does a linear regression model?

Question 4 options:

	The log of the dependent variable
	The aggregated weights of the independent variables
	The expected value of a numeric dependent variable
	The category of the dependent variable

Question 5 (1 point)

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A non-constant variance of the error terms in relation to the response variable is an issue involving:

Question 5 options:

	Heteroscedasticity
	Correlation with the error term
	The error terms are not normally distributed
	A non-linear relationship between the dependent and independent variables