Question 1

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

Accepted Answer

A) True 
 B)False

Question 2

In a simple linear regression analysis,the following sums of squares are produced:   The proportion of the variation in Y that is explained by the variation in X is

Accepted Answer

A) 20%. 
B) 80%. 
C) 25%. 
D) 50%. 
A) 20%. 
B) 80%. 
C) 25%. 
D) 50%.

Question 3

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

Accepted Answer

A) True 
 B)False

Question 4

The residual is defined as the difference between the actual and predicted,or fitted values of the response variable.

Accepted Answer

A) True 
 B)False

Question 5

The regression line   = 3 + 2X has been fitted to the data points (4,14),(2,7),and (1,4).The sum of the residuals squared will be 8.0.

Accepted Answer

A) True 
 B)False

Question 6

In a constant elasticity,or multiplicative,relationship the dependent variable is expressed as a product of explanatory variables raised to powers.

Accepted Answer

A) True 
 B)False

Question 7

A scatterplot that appears as a shapeless mass of data points indicates _____ relationship among the variables.

Accepted Answer

A) a curved 
B) a linear 
C) a nonlinear 
D) no 
A) a curved 
B) a linear 
C) a nonlinear 
D) no

Question 8

In multiple regression,the constant

Accepted Answer

A) is the expected value of the dependent variable Y when all of the independent variables have the value zero. 
B) is necessary to fit the multiple regression line to set of points. 
C) must be adjusted for the number of independent variables 
D) is all of these options. 
A) is the expected value of the dependent variable Y when all of the independent variables have the value zero. 
B) is necessary to fit the multiple regression line to set of points. 
C) must be adjusted for the number of independent variables 
D) is all of these options.

Question 9

In regression analysis,which of the following causal relationships are possible?

Accepted Answer

A) X causes Y to vary. 
B) Y causes X to vary. 
C) Other variables cause both X and Y to vary. 
D) All of these options are possible. 
A) X causes Y to vary. 
B) Y causes X to vary. 
C) Other variables cause both X and Y to vary. 
D) All of these options are possible.

Question 10

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be used.

Accepted Answer

A) True 
 B)False

Question 11

In reference to the equation,   ,the value 0.10 is the expected change in Y per unit change in X.

Accepted Answer

A) True 
 B)False

Question 12

The multiple standard error of estimate will be

Accepted Answer

A) 0.901. 
B) 0.888. 
C) 0.800. 
D) 0.953. 
A) 0.901. 
B) 0.888. 
C) 0.800. 
D) 0.953.

Question 13

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

Accepted Answer

A) True 
 B)False

Question 14

A regression analysis between X = sales (in $1000s)and Y = advertising ($)resulted in the following least squares line:   = 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

Question 15

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric.

Accepted Answer

A) True 
 B)False

Question 16

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

Accepted Answer

A) True 
 B)False

Question 17

In a simple linear regression problem,if   = 0.95,this means that 95% of the variation in the explanatory variable X can be explained by the regression.

Accepted Answer

A) True 
 B)False

Question 18

In simple linear regression,the divisor of the standard error of estimate   is n - 1,because there is only one explanatory variable.

Accepted Answer

A) True 
 B)False

Question 19

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 20

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.

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

In a simple linear regression analysis,the following sums of squares are produced: The proportion of the variation in Y that is explained by the variation in X is

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

The residual is defined as the difference between the actual and predicted,or fitted values of the response variable.

The regression line = 3 + 2X has been fitted to the data points (4,14),(2,7),and (1,4).The sum of the residuals squared will be 8.0.

In a constant elasticity,or multiplicative,relationship the dependent variable is expressed as a product of explanatory variables raised to powers.

A scatterplot that appears as a shapeless mass of data points indicates _____ relationship among the variables.

In multiple regression,the constant

In regression analysis,which of the following causal relationships are possible?

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be used.

In reference to the equation, ,the value 0.10 is the expected change in Y per unit change in X.

The multiple standard error of estimate will be

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

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

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric.

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

In a simple linear regression problem,if = 0.95,this means that 95% of the variation in the explanatory variable X can be explained by the regression.

In simple linear regression,the divisor of the standard error of estimate is n - 1,because there is only one explanatory variable.

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

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

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability 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

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 10: Regression Analysis: Estimating Relationships

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

In a simple linear regression analysis,the following sums of squares are produced: The proportion of the variation in Y that is explained by the variation in X is

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

The residual is defined as the difference between the actual and predicted,or fitted values of the response variable.

The regression line = 3 + 2X has been fitted to the data points (4,14),(2,7),and (1,4).The sum of the residuals squared will be 8.0.

In a constant elasticity,or multiplicative,relationship the dependent variable is expressed as a product of explanatory variables raised to powers.

A scatterplot that appears as a shapeless mass of data points indicates _____ relationship among the variables.

In multiple regression,the constant

In regression analysis,which of the following causal relationships are possible?

If a categorical variable is to be included in a multiple regression,a dummy variable for each category of the variable should be used,but the original categorical variables should not be used.

In reference to the equation, ,the value 0.10 is the expected change in Y per unit change in X.

The multiple standard error of estimate will be

A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis)versus fitted values (on the horizontal axis),where a "good" fit not only has small residuals,but it has residuals scattered randomly around zero with no apparent pattern.

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

The effect of a logarithmic transformation on a variable that is skewed to the right by a few large values is to "squeeze" the values together and make the distribution more symmetric.

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

In a simple linear regression problem,if = 0.95,this means that 95% of the variation in the explanatory variable X can be explained by the regression.

In simple linear regression,the divisor of the standard error of estimate is n - 1,because there is only one explanatory variable.

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

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

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability 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

Analysis of Variance and Experimental Design

Statistical Process Control

Filters