Question 1

The regression line $$\hat { Y }$$ 
= 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 2

In every regression study there is a single variable that we are trying to explain or predict.This is called the response variable or dependent variable.

Accepted Answer

A) True 
 B)False

Question 3

Cross-sectional data are usually data gathered from approximately the same period of time from a cross-sectional of a population.

Accepted Answer

A) True 
 B)False

Question 4

The adjusted R2 adjusts R2 for:

Accepted Answer

A)  non-linearity 
B)  outliers 
C)  low correlation 
D)  the number of explanatory variables in a multiple regression model 
A)  non-linearity 
B)  outliers 
C)  low correlation 
D)  the number of explanatory variables in a multiple regression model

Question 5

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 6

A negative relationship between an explanatory variable X and a response variable Y means that as X increases,Y decreases,and vice versa.

Accepted Answer

A) True 
 B)False

Question 7

In multiple regression,the constant $C X$
:

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)  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)  All of these options

Question 8

In a simple linear regression problem,if the percentage of variation explained $R ^ { 2 }$ 
is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

Accepted Answer

A) True 
 B)False

Question 9

In reference to the equation, $\hat { Y } = - 0.70 + 0.10 X$ 
,the value 0.10 is the expected change in Y per unit change in $$X _ { 1 }$$ 
.

Accepted Answer

A) True 
 B)False

Question 10

In linear regression,we fit the least squares line to a set of values (or points on a scatterplot).The distance from the line to a point is called the:

Accepted Answer

A)  fitted value 
B)  residual 
C)  correlation 
D)  covariance 
E)  None of these options 
A)  fitted value 
B)  residual 
C)  correlation 
D)  covariance 
E)  None of these options

Question 11

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

Accepted Answer

A) True 
 B)False

Question 12

The percentage of variation explained $R ^ { 2 }$ 
is the square of the correlation between the observed Y values and the fitted Y values.

Accepted Answer

A) True 
 B)False

Question 13

In linear regression,a dummy variable is used:

Accepted Answer

A)  to represent residual variables 
B)  to represent missing data in each sample 
C)  to include hypothetical data in the regression equation 
D)  to include categorical variables in the regression equation 
E)  when "dumb" responses are included in the data 
A)  to represent residual variables 
B)  to represent missing data in each sample 
C)  to include hypothetical data in the regression equation 
D)  to include categorical variables in the regression equation 
E)  when "dumb" responses are included in the data

Question 14

In a simple regression analysis,if the standard error of estimate $s _ { e }$ 
= 15 and the number of observations n = 10,then the sum of the residuals squared must be 120.

Accepted Answer

A) True 
 B)False

Question 15

A scatterplot that appears as a shapeless mass of data points indicates:

Accepted Answer

A)  a curved relationship among the variables 
B)  a linear relationship among the variables 
C)  a nonlinear relationship among the variables 
D)  no relationship among the variables 
A)  a curved relationship among the variables 
B)  a linear relationship among the variables 
C)  a nonlinear relationship among the variables 
D)  no relationship among the variables

Question 16

In regression analysis,the variable we are trying to explain or predict is called the

Accepted Answer

A)  independent variable 
B)  dependent variable 
C)  regression variable 
D)  statistical variable 
E)  residual variable 
A)  independent variable 
B)  dependent variable 
C)  regression variable 
D)  statistical variable 
E)  residual variable

Question 17

The standard error of the estimate ( $s _ { e }$
)is essentially the

Accepted Answer

A)  mean of the residuals 
B)  standard deviation of the residuals 
C)  mean of the explanatory variable 
D)  standard deviation of the explanatory variable 
A)  mean of the residuals 
B)  standard deviation of the residuals 
C)  mean of the explanatory variable 
D)  standard deviation of the explanatory variable

Question 18

A "fan" shape in a scatterplot indicates:

Accepted Answer

A)  unequal variance 
B)  a nonlinear relationship 
C)  the absence of outliers 
D)  sampling error 
A)  unequal variance 
B)  a nonlinear relationship 
C)  the absence of outliers 
D)  sampling error

Question 19

The adjusted R2 is used primarily to monitor whether extra explanatory variables really belong in a multiple regression model

Accepted Answer

A) True 
 B)False

The regression line $\hat { Y }$ = 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 every regression study there is a single variable that we are trying to explain or predict.This is called the response variable or dependent variable.

Cross-sectional data are usually data gathered from approximately the same period of time from a cross-sectional of a population.

The adjusted R² adjusts R² for:

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

A negative relationship between an explanatory variable X and a response variable Y means that as X increases,Y decreases,and vice versa.

In multiple regression,the constant $C X$ :

In a simple linear regression problem,if the percentage of variation explained $R ^ { 2 }$ is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

In reference to the equation, $\hat { Y } = - 0.70 + 0.10 X$ ,the value 0.10 is the expected change in Y per unit change in $X _ { 1 }$ .

In linear regression,we fit the least squares line to a set of values (or points on a scatterplot).The distance from the line to a point is called the:

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

The percentage of variation explained $R ^ { 2 }$ is the square of the correlation between the observed Y values and the fitted Y values.

In linear regression,a dummy variable is used:

In a simple regression analysis,if the standard error of estimate $s _ { e }$ = 15 and the number of observations n = 10,then the sum of the residuals squared must be 120.

A scatterplot that appears as a shapeless mass of data points indicates:

In regression analysis,the variable we are trying to explain or predict is called the

The standard error of the estimate ( $s _ { e }$ )is essentially the

A "fan" shape in a scatterplot indicates:

The adjusted R² is used primarily to monitor whether extra explanatory variables really belong in a multiple regression model

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 regression line Y^\hat { Y }Y^ = 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 every regression study there is a single variable that we are trying to explain or predict.This is called the response variable or dependent variable.

Cross-sectional data are usually data gathered from approximately the same period of time from a cross-sectional of a population.

The adjusted R2 adjusts R2 for:

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

A negative relationship between an explanatory variable X and a response variable Y means that as X increases,Y decreases,and vice versa.

In multiple regression,the constant CXC XCX :

In a simple linear regression problem,if the percentage of variation explained R2R ^ { 2 }R2 is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

In reference to the equation, Y^=−0.70+0.10X\hat { Y } = - 0.70 + 0.10 XY^=−0.70+0.10X ,the value 0.10 is the expected change in Y per unit change in X1X _ { 1 }X1​ .

In linear regression,we fit the least squares line to a set of values (or points on a scatterplot).The distance from the line to a point is called the:

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

The percentage of variation explained R2R ^ { 2 }R2 is the square of the correlation between the observed Y values and the fitted Y values.

In linear regression,a dummy variable is used:

In a simple regression analysis,if the standard error of estimate ses _ { e }se​ = 15 and the number of observations n = 10,then the sum of the residuals squared must be 120.

A scatterplot that appears as a shapeless mass of data points indicates:

In regression analysis,the variable we are trying to explain or predict is called the

The standard error of the estimate ( ses _ { e }se​ )is essentially the

A "fan" shape in a scatterplot indicates:

The adjusted R2 is used primarily to monitor whether extra explanatory variables really belong in a multiple regression model

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 regression line $\hat { Y }$ = 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.

The adjusted R² adjusts R² for:

In multiple regression,the constant $C X$ :

In a simple linear regression problem,if the percentage of variation explained $R ^ { 2 }$ is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

In reference to the equation, $\hat { Y } = - 0.70 + 0.10 X$ ,the value 0.10 is the expected change in Y per unit change in $X _ { 1 }$ .

The percentage of variation explained $R ^ { 2 }$ is the square of the correlation between the observed Y values and the fitted Y values.

In a simple regression analysis,if the standard error of estimate $s _ { e }$ = 15 and the number of observations n = 10,then the sum of the residuals squared must be 120.

The standard error of the estimate ( $s _ { e }$ )is essentially the

The adjusted R² is used primarily to monitor whether extra explanatory variables really belong in a multiple regression model