Question 1

The percentage of variation ( $R ^ { 2 }$
)can be interpreted as the fraction (or percent)of variation of the

Accepted Answer

A)  explanatory variable explained by the independent variable 
B)  explanatory variable explained by the regression line 
C)  response variable explained by the regression line 
D)  error explained by the regression line 
A)  explanatory variable explained by the independent variable 
B)  explanatory variable explained by the regression line 
C)  response variable explained by the regression line 
D)  error explained by the regression line C

Question 2

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $$\hat { Y }$$ 
= 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

Accepted Answer

A) True 
 B)False  True

Question 3

In a multiple regression problem with two explanatory variables if,the fitted regression equation is $\hat { Y } = 56.6 - 4.5 X _ { 1 } + 0.60 X _ { 2 } \text {, then the estimated value of } Y \text { when } X _ { 1 } = 2 \text { and } X _ { 2 } = 3 \text { is } 49.4$ 
.

Accepted Answer

A) True 
 B)False  True

Question 4

Correlation is a summary measure that indicates:

Accepted Answer

A)  a curved relationship among the variables 
B)  the rate of change in Y for a one unit change in X 
C)  the strength of the linear relationship between pairs of variables 
D)  the magnitude of difference between two variables 
A)  a curved relationship among the variables 
B)  the rate of change in Y for a one unit change in X 
C)  the strength of the linear relationship between pairs of variables 
D)  the magnitude of difference between two variables

Question 5

The covariance is not used as much as the correlation because

Accepted Answer

A)  is not always a valid predictor of linear relationships 
B)  it is difficult to calculate 
C)  it is difficult to interpret 
D)  all of these options 
A)  is not always a valid predictor of linear relationships 
B)  it is difficult to calculate 
C)  it is difficult to interpret 
D)  all of these options

Question 6

Regression analysis can be applied equally well to cross-sectional and time series data.

Accepted Answer

A) True 
 B)False

Question 7

The multiple R for a regression is the correlation between the observed Y values and the fitted Y values.

Accepted Answer

A) True 
 B)False

Question 8

A constant elasticity,or multiplicative,model the dependent variable is expressed as a product of explanatory variables raised to powers

Accepted Answer

A) True 
 B)False

Question 9

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

Accepted Answer

A)  the dependent variable will remain constant 
B)  the dependent variable will be allowed to vary 
C)  all of the other independent variables remain constant 
D)  all of the other independent variables be allowed to vary 
A)  the dependent variable will remain constant 
B)  the dependent variable will be allowed to vary 
C)  all of the other independent variables remain constant 
D)  all of the other independent variables be allowed to vary

Question 10

If a scatterplot of residuals shows a parabola shape,then a logarithmic transformation may be useful in obtaining a better fit

Accepted Answer

A) True 
 B)False

Question 11

The two primary objectives of regression analysis are to study relationships between variables and to use those relationships to make predictions.

Accepted Answer

A) True 
 B)False

Question 12

An interaction variable is the product of an explanatory variable and the dependent variable.

Accepted Answer

A) True 
 B)False

Question 13

A regression analysis between weight (Y in pounds)and height (X in inches)resulted in the following least squares line: $\hat { y }$ 
= 140 + 5X.This implies that if the height is increased by 1 inch,the weight is expected to increase on average by 5 pounds.

Accepted Answer

A) True 
 B)False

Question 14

The R2 can only increase when extra explanatory variables are added to a multiple regression model

Accepted Answer

A) True 
 B)False

Question 15

An outlier is an observation that falls outside of the general pattern of the rest of the observations on a scatterplot.

Accepted Answer

A) True 
 B)False

Question 16

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable $X _ { 1 }$ 
on the response variable Y depends on the value of another explanatory variable $X _ { 2 }$ 
.

Accepted Answer

A) True 
 B)False

Question 17

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line: $\hat { Y }$ 
= 32 + 8X.This implies that an increase of $1 in advertising is expected to result in an increase of $40 in sales.

Accepted Answer

A) True 
 B)False

Question 18

For the multiple regression model $\bar { Y } = 40 + 15 X _ { 1 } - 10 X _ { 2 } + 5 X _ { 3 }$ 
,if $X _ { 2 }$ 
were to increase by 5 units,holding $X _ { 1 }$ 
and $X _ { 3 }$ 
constant,the value of Y would be expected to decrease by 50 units.

Accepted Answer

A) True 
 B)False

Question 19

The least squares line is the line that minimizes the sum of the residuals.

Accepted Answer

A) True 
 B)False

Question 20

Correlation is measured on a scale from 0 to 1,where 0 indicates no linear relationship between two variables,and 1 indicates a perfect linear relationship.

Accepted Answer

A) True 
 B)False

The percentage of variation ( $R ^ { 2 }$ )can be interpreted as the fraction (or percent)of variation of the

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $\hat { Y }$ = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

In a multiple regression problem with two explanatory variables if,the fitted regression equation is $\hat { Y } = 56.6 - 4.5 X _ { 1 } + 0.60 X _ { 2 } \text {, then the estimated value of } Y \text { when } X _ { 1 } = 2 \text { and } X _ { 2 } = 3 \text { is } 49.4$ .

Correlation is a summary measure that indicates:

The covariance is not used as much as the correlation because

Regression analysis can be applied equally well to cross-sectional and time series data.

The multiple R for a regression is the correlation between the observed Y values and the fitted Y values.

A constant elasticity,or multiplicative,model the dependent variable is expressed as a product of explanatory variables raised to powers

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

If a scatterplot of residuals shows a parabola shape,then a logarithmic transformation may be useful in obtaining a better fit

The two primary objectives of regression analysis are to study relationships between variables and to use those relationships to make predictions.

An interaction variable is the product of an explanatory variable and the dependent variable.

A regression analysis between weight (Y in pounds)and height (X in inches)resulted in the following least squares line: $\hat { y }$ = 140 + 5X.This implies that if the height is increased by 1 inch,the weight is expected to increase on average by 5 pounds.

The R² can only increase when extra explanatory variables are added to a multiple regression model

An outlier is an observation that falls outside of the general pattern of the rest of the observations on a scatterplot.

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable $X _ { 1 }$ on the response variable Y depends on the value of another explanatory variable $X _ { 2 }$ .

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line: $\hat { Y }$ = 32 + 8X.This implies that an increase of $1 in advertising is expected to result in an increase of $40 in sales.

For the multiple regression model $\bar { Y } = 40 + 15 X _ { 1 } - 10 X _ { 2 } + 5 X _ { 3 }$ ,if $X _ { 2 }$ were to increase by 5 units,holding $X _ { 1 }$ and $X _ { 3 }$ constant,the value of Y would be expected to decrease by 50 units.

The least squares line is the line that minimizes the sum of the residuals.

Correlation is measured on a scale from 0 to 1,where 0 indicates no linear relationship between two variables,and 1 indicates a perfect linear relationship.

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 10: Regression Analysis: Estimating Relationships

The percentage of variation ( R2R ^ { 2 }R2 )can be interpreted as the fraction (or percent)of variation of the

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: Y^\hat { Y }Y^ = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

Correlation is a summary measure that indicates:

The covariance is not used as much as the correlation because

Regression analysis can be applied equally well to cross-sectional and time series data.

The multiple R for a regression is the correlation between the observed Y values and the fitted Y values.

A constant elasticity,or multiplicative,model the dependent variable is expressed as a product of explanatory variables raised to powers

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

If a scatterplot of residuals shows a parabola shape,then a logarithmic transformation may be useful in obtaining a better fit

The two primary objectives of regression analysis are to study relationships between variables and to use those relationships to make predictions.

An interaction variable is the product of an explanatory variable and the dependent variable.

A regression analysis between weight (Y in pounds)and height (X in inches)resulted in the following least squares line: y^\hat { y }y^​ = 140 + 5X.This implies that if the height is increased by 1 inch,the weight is expected to increase on average by 5 pounds.

The R2 can only increase when extra explanatory variables are added to a multiple regression model

An outlier is an observation that falls outside of the general pattern of the rest of the observations on a scatterplot.

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable X1X _ { 1 }X1​ on the response variable Y depends on the value of another explanatory variable X2X _ { 2 }X2​ .

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line: Y^\hat { Y }Y^ = 32 + 8X.This implies that an increase of $1 in advertising is expected to result in an increase of $40 in sales.

The least squares line is the line that minimizes the sum of the residuals.

Correlation is measured on a scale from 0 to 1,where 0 indicates no linear relationship between two variables,and 1 indicates a perfect linear relationship.

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

The percentage of variation ( $R ^ { 2 }$ )can be interpreted as the fraction (or percent)of variation of the

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: $\hat { Y }$ = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

A regression analysis between weight (Y in pounds)and height (X in inches)resulted in the following least squares line: $\hat { y }$ = 140 + 5X.This implies that if the height is increased by 1 inch,the weight is expected to increase on average by 5 pounds.

The R² can only increase when extra explanatory variables are added to a multiple regression model

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable $X _ { 1 }$ on the response variable Y depends on the value of another explanatory variable $X _ { 2 }$ .

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line: $\hat { Y }$ = 32 + 8X.This implies that an increase of $1 in advertising is expected to result in an increase of $40 in sales.