Question 1

The term autocorrelation refers to:

Accepted Answer

A)  the analyzed data refers to itself 
B)  the sample is related too closely to the population 
C)  the data are in a loop (values repeat themselves) 
D)  time series variables are usually related to their own past values 
A)  the analyzed data refers to itself 
B)  the sample is related too closely to the population 
C)  the data are in a loop (values repeat themselves) 
D)  time series variables are usually related to their own past values

Question 2

In a multiple regression analysis with three explanatory variables,suppose that there are 60 observations and the sum of the residuals squared is 28.The standard error of estimate must be 0.7071.

Accepted Answer

A) True 
 B)False

Question 3

In a simple linear regression problem,suppose that $\sum e _ { i } ^ { 2 } = 12.48 \text { and } \sum \left( Y _ { i } - Y \right) ^ { 2 } = 124.8$ 
.Then the percentage of variation explained $R ^ { 2 }$ 
must be 0.90.

Accepted Answer

A) True 
 B)False

Question 4

In linear regression,we can have an interaction variable.Algebraically,the interaction variable is the other variables in the regression equation.

Accepted Answer

A)  sum 
B)  ratio 
C)  product 
D)  mean 
A)  sum 
B)  ratio 
C)  product 
D)  mean

Question 5

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 
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

Question 6

In the multiple regression model $\hat { Y } = 6.75 + 2.25 X _ { 1 } + 3.5 X _ { 2 }$ 
we interpret X1 as follows: holding X2 constant,if X1 increases by 1 unit,then the expected value of Y will increase by 9 units.

Accepted Answer

A) True 
 B)False

Question 7

In choosing the "best-fitting" line through a set of points in linear regression,we choose the one with the:

Accepted Answer

A)  smallest sum of squared residuals 
B)  largest sum of squared residuals 
C)  smallest number of outliers 
D)  largest number of points on the line 
E)  None of these options 
A)  smallest sum of squared residuals 
B)  largest sum of squared residuals 
C)  smallest number of outliers 
D)  largest number of points on the line 
E)  None of these options

Question 8

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

Accepted Answer

A) True 
 B)False

Question 9

The regression line $\hat { Y } = 3 + 2.5 X$
Has been fitted to the data points (28,60), (20,50), (10,18),and (25,55).The sum of the squared residuals will be:

Accepted Answer

A)  20.25 
B)  16.00 
C)  49.00 
D)  94.25 
A)  20.25 
B)  16.00 
C)  49.00 
D)  94.25

Question 10

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 11

Outliers are observations that

Accepted Answer

A)  lie outside the sample 
B)  render the study useless 
C)  lie outside the typical pattern of points on a scatterplot 
D)  disrupt the entire linear trend 
A)  lie outside the sample 
B)  render the study useless 
C)  lie outside the typical pattern of points on a scatterplot 
D)  disrupt the entire linear trend

Question 12

Scatterplots are used for identifying outliers and quantifying relationships between variables.

Accepted Answer

A) True 
 B)False

Question 13

In simple linear regression,the divisor of the standard error of estimate $S _ { e }$ 
is n - 1;simply because there is only one explanatory variable of interest.

Accepted Answer

A) True 
 B)False

Question 14

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 15

A logarithmic transformation of the response variable Y is often useful when the distribution of Y is symmetric.

Accepted Answer

A) True 
 B)False

Question 16

is/are especially helpful in identifying outliers.

Accepted Answer

A)  Linear regression 
B)  Regression analysis 
C)  Normal curves 
D)  Scatterplots 
E)  Multiple regression 
A)  Linear regression 
B)  Regression analysis 
C)  Normal curves 
D)  Scatterplots 
E)  Multiple regression

Question 17

In multiple regression,the coefficients reflect the expected change in:

Accepted Answer

A)  Y when the associated X value increases by one unit 
B)  X when the associated Y value increases by one unit 
C)  Y when the associated X value decreases by one unit 
D)  X when the associated Y value decreases by one unit 
A)  Y when the associated X value increases by one unit 
B)  X when the associated Y value increases by one unit 
C)  Y when the associated X value decreases by one unit 
D)  X when the associated Y value decreases by one unit

Question 18

The adjusted R2 is adjusted for the number of explanatory variables in a regression equation,and it has the same interpretation as the standard R2.

Accepted Answer

A) True 
 B)False

Question 19

In regression analysis,we can often use the standard error of estimate $S _ { e }$ 
to judge which of several potential regression equations is the most useful.

Accepted Answer

A) True 
 B)False

Question 20

When the scatterplot appears as a shapeless swarm of points,this can indicate that there is no relationship between the response variable Y and the explanatory variable X,or at least none worth pursuing.

Accepted Answer

A) True 
 B)False

The term autocorrelation refers to:

In a multiple regression analysis with three explanatory variables,suppose that there are 60 observations and the sum of the residuals squared is 28.The standard error of estimate must be 0.7071.

In a simple linear regression problem,suppose that $\sum e _ { i } ^ { 2 } = 12.48 \text { and } \sum \left( Y _ { i } - Y \right) ^ { 2 } = 124.8$ .Then the percentage of variation explained $R ^ { 2 }$ must be 0.90.

In linear regression,we can have an interaction variable.Algebraically,the interaction variable is the other variables in the regression equation.

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

In the multiple regression model $\hat { Y } = 6.75 + 2.25 X _ { 1 } + 3.5 X _ { 2 }$ we interpret X₁ as follows: holding X₂ constant,if X₁ increases by 1 unit,then the expected value of Y will increase by 9 units.

In choosing the "best-fitting" line through a set of points in linear regression,we choose the one with the:

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

The regression line $\hat { Y } = 3 + 2.5 X$ Has been fitted to the data points (28,60), (20,50), (10,18),and (25,55).The sum of the squared residuals will be:

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.

Outliers are observations that

Scatterplots are used for identifying outliers and quantifying relationships between variables.

In simple linear regression,the divisor of the standard error of estimate $S _ { e }$ is n - 1;simply because there is only one explanatory variable of interest.

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

A logarithmic transformation of the response variable Y is often useful when the distribution of Y is symmetric.

is/are especially helpful in identifying outliers.

In multiple regression,the coefficients reflect the expected change in:

The adjusted R² is adjusted for the number of explanatory variables in a regression equation,and it has the same interpretation as the standard R².

In regression analysis,we can often use the standard error of estimate $S _ { e }$ to judge which of several potential regression equations is the most useful.

When the scatterplot appears as a shapeless swarm of points,this can indicate that there is no relationship between the response variable Y and the explanatory variable X,or at least none worth pursuing.

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 term autocorrelation refers to:

In a multiple regression analysis with three explanatory variables,suppose that there are 60 observations and the sum of the residuals squared is 28.The standard error of estimate must be 0.7071.

In a simple linear regression problem,suppose that ∑ei2=12.48 and ∑(Yi−Y)2=124.8\sum e _ { i } ^ { 2 } = 12.48 \text { and } \sum \left( Y _ { i } - Y \right) ^ { 2 } = 124.8∑ei2​=12.48 and ∑(Yi​−Y)2=124.8 .Then the percentage of variation explained R2R ^ { 2 }R2 must be 0.90.

In linear regression,we can have an interaction variable.Algebraically,the interaction variable is the other variables in the regression equation.

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

In the multiple regression model Y^=6.75+2.25X1+3.5X2\hat { Y } = 6.75 + 2.25 X _ { 1 } + 3.5 X _ { 2 }Y^=6.75+2.25X1​+3.5X2​ we interpret X1 as follows: holding X2 constant,if X1 increases by 1 unit,then the expected value of Y will increase by 9 units.

In choosing the "best-fitting" line through a set of points in linear regression,we choose the one with the:

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

The regression line Y^=3+2.5X\hat { Y } = 3 + 2.5 XY^=3+2.5X Has been fitted to the data points (28,60), (20,50), (10,18),and (25,55).The sum of the squared residuals will be:

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.

Outliers are observations that

Scatterplots are used for identifying outliers and quantifying relationships between variables.

In simple linear regression,the divisor of the standard error of estimate SeS _ { e }Se​ is n - 1;simply because there is only one explanatory variable of interest.

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

A logarithmic transformation of the response variable Y is often useful when the distribution of Y is symmetric.

is/are especially helpful in identifying outliers.

In multiple regression,the coefficients reflect the expected change in:

The adjusted R2 is adjusted for the number of explanatory variables in a regression equation,and it has the same interpretation as the standard R2.

In regression analysis,we can often use the standard error of estimate SeS _ { e }Se​ to judge which of several potential regression equations is the most useful.

When the scatterplot appears as a shapeless swarm of points,this can indicate that there is no relationship between the response variable Y and the explanatory variable X,or at least none worth pursuing.

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

In a simple linear regression problem,suppose that $\sum e _ { i } ^ { 2 } = 12.48 \text { and } \sum \left( Y _ { i } - Y \right) ^ { 2 } = 124.8$ .Then the percentage of variation explained $R ^ { 2 }$ must be 0.90.

In the multiple regression model $\hat { Y } = 6.75 + 2.25 X _ { 1 } + 3.5 X _ { 2 }$ we interpret X₁ as follows: holding X₂ constant,if X₁ increases by 1 unit,then the expected value of Y will increase by 9 units.

The regression line $\hat { Y } = 3 + 2.5 X$ Has been fitted to the data points (28,60), (20,50), (10,18),and (25,55).The sum of the squared residuals will be:

In simple linear regression,the divisor of the standard error of estimate $S _ { e }$ is n - 1;simply because there is only one explanatory variable of interest.

The adjusted R² is adjusted for the number of explanatory variables in a regression equation,and it has the same interpretation as the standard R².

In regression analysis,we can often use the standard error of estimate $S _ { e }$ to judge which of several potential regression equations is the most useful.