Question 1

In a regression setting, which, if any, of the following is not an assumption about the random error   ?

Accepted Answer

A)  The errors are linearly correlated. 
B)  The errors are normally distributed with a mean of 0 and a common variance. 
C)  The errors are independent in a probabilistic sense. 
D)  All of these are assumptions about the random error. 
E)  None of these. 
A)  The errors are linearly correlated. 
B)  The errors are normally distributed with a mean of 0 and a common variance. 
C)  The errors are independent in a probabilistic sense. 
D)  All of these are assumptions about the random error. 
E)  None of these.

Question 2

In the simple linear regression model   which of the following is false regarding the values of the random error term   ?

Accepted Answer

A)  They are independent. 
B)  They have a mean of 0 and a variance of 1, independent of x. 
C)  They are normally distributed. 
D)  None of these. 
E)  All of these. 
A)  They are independent. 
B)  They have a mean of 0 and a variance of 1, independent of x. 
C)  They are normally distributed. 
D)  None of these. 
E)  All of these.

Question 3

If a set of data contains no values of the independent variable x that are equal to zero, then the y-intercept regression coefficient   has no particular meaning.

Accepted Answer

A) True 
 B)False

Question 4

A regression analysis between sales (in $1000) and advertising (in $) resulted in the following least squares line:   = 60 + 5x. This implies that an increase of $1 in advertising is expected to result in an increase of $65 in sales.

Accepted Answer

A) True 
 B)False

Question 5

In a simple linear regression model, testing whether the slope   of the population regression line could be zero is the same as testing whether or not the population coefficient of correlation   equals zero.

Accepted Answer

A) True 
 B)False

In a regression setting, which, if any, of the following is not an assumption about the random error ?

In the simple linear regression model which of the following is false regarding the values of the random error term ?

If a set of data contains no values of the independent variable x that are equal to zero, then the y-intercept regression coefficient has no particular meaning.

A regression analysis between sales (in $1000) and advertising (in $) resulted in the following least squares line: = 60 + 5x. This implies that an increase of $1 in advertising is expected to result in an increase of $65 in sales.

In a simple linear regression model, testing whether the slope of the population regression line could be zero is the same as testing whether or not the population coefficient of correlation equals zero.

Describing Data With Graphs

Describing Data With Numerical Measures

Describing Bivariate Data

Probability and Probability Distributions

Several Useful Discrete Distributions

The Normal Probability Distribution

Sampling Distributions

Large-Sample Estimation

Large-Sample Tests of Hypotheses

Inference From Small Samples

The Analysis of Variance

Multiple Regression Analysis

Analysis of Categorical Data

Nonparametric Statistics

Filters

Exam 12: Linear Regression and Correlation

In a regression setting, which, if any, of the following is not an assumption about the random error ?

In the simple linear regression model which of the following is false regarding the values of the random error term ?

If a set of data contains no values of the independent variable x that are equal to zero, then the y-intercept regression coefficient has no particular meaning.

A regression analysis between sales (in $1000) and advertising (in $) resulted in the following least squares line: = 60 + 5x. This implies that an increase of $1 in advertising is expected to result in an increase of $65 in sales.

In a simple linear regression model, testing whether the slope of the population regression line could be zero is the same as testing whether or not the population coefficient of correlation equals zero.

Describing Data With Graphs

Describing Data With Numerical Measures

Describing Bivariate Data

Probability and Probability Distributions

Several Useful Discrete Distributions

The Normal Probability Distribution

Sampling Distributions

Large-Sample Estimation

Large-Sample Tests of Hypotheses

Inference From Small Samples

The Analysis of Variance

Multiple Regression Analysis

Analysis of Categorical Data

Nonparametric Statistics

Filters