1

Which of the following is NOT true about linear regression?

Review Later

Linear regression allows us to predict new values of the independent variable.

Linear regression allows us to model how the target variable changes with the independent variables.

In linear regression, the target variable is a continuous quantity.

Linear regression is used to predict new values of the target variable.

2

The ordinary least squares (OLS) algorithm ________________ .

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Maximizes the sum of square residuals

Minimizes the sum of square residuals

Minimizes the square of the sum of residuals

Maximizes the square of the sum of residuals

3

Overfitting occurs when _____________.

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The sum of square residuals is too large

Our model does not have enough complexity

The average of the errors is positive

Our model becomes too specific to the training data

4.

Using multiple linear regression to add in more independent variables ___________.

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can help explain more variation in the target variable

allows us to fit a non-linear model to the data

allows us to add more observational data to the model

reduces the overfitting of the data

5.

Multicollinearity is the phenomenon where _________________.

Review Later

the independent variables are strongly correlated with the residuals

the target variable is strongly correlated with the residuals

the independent variables are strongly correlated with other independent variables

the target variable is strongly correlated with an independent variable

6

Which of the following is NOTan assumption of ordinary least squares (OLS):

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Homoscedasticity of Errors

Endogeneity

Random Sampling

Linearity

7

Which assumption of OLS assumes that there is no correlation between the error and the independent variables?

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Zero Mean Errors

Multicollinearity

Endogeneity

Autocorrelation of Errors

8

A regression analysis between sales (S) (in $1000) and price (P) (in dollars) resulted in the following equation:

S = 50,000 - 8P

The above equation implies that an ___________.

Review Later

increase of $1 in price is associated with a decrease of $8 in sales

increase of $1 in price is associated with a decrease of $8000 in sales

increase of $1 in price is associated with a decrease of $42,000 in sales

increase of $8 in price is associated with an increase of $8,000 in sales

9

Which of the following is the formula for the mean square error?

Review Later

N Elyi - yil O E(vi- yi) 2 1 - E(vi-y)2 O LE(Vi - y 2 O E(vi - y)2