Exam 11: Regression Analysis: Statistical Inference

One of the potential characteristics of an outlier is that the value of the dependent variable is much larger or smaller than predicted by the regression line.

(True/False)

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Question 41

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

(True/False)

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Question 42

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

(Multiple Choice)

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Question 43

If residuals separated by one period are autocorrelated,this is called:

(Multiple Choice)

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Question 44

If exact multicollinearlity exists,that means that there is redundancy in the data.

(True/False)

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Question 45

The t-value for testing $H _ { 0 } : B _ { i } = 0$ Is calculated using which of the following equations:

(Multiple Choice)

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Question 46

Many statistical packages have three types of equation-building procedures.They are:

(Multiple Choice)

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Question 47

In regression analysis,extrapolation is performed when you:

(Multiple Choice)

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Question 48

The value of the sum of squares due to regression,SSR,can never be larger than the value of the sum of squares total,SST.

(True/False)

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Question 49

A backward procedure is a type of equation building procedure that begins with all potential explanatory variables in the regression equation and deletes them two at a time until further deletion would reduce the percentage of variation explained to a value less than 0.50.

(True/False)

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Question 50

The appropriate hypothesis test for a regression coefficient is:

(Multiple Choice)

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Question 51

In simple linear regression,if the error variable $\varepsilon$ is normally distributed,the test statistic for testing $H _ { 0 } : B _ { 1 } = 0$ is t-distributed with n - 2 degrees of freedom.

(True/False)

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Question 52

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

(True/False)

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Question 53

The residuals are observations of the error variable $\varepsilon$ .Consequently,the minimized sum of squared deviations is called the sum of squared error,labeled SSE.

(True/False)

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Question 54

Multicollinearity is a situation in which two or more of the explanatory variables are highly correlated with each other.

(True/False)

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Question 55

A forward procedure is a type of equation building procedure that begins with only one explanatory variable in the regression equation and successively adds one variable at a time until no remaining variables make a significant contribution.

(True/False)

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Question 56

The assumptions of regression are: 1)there is a population regression line,2)the dependent variable is normally distributed,3)the standard deviation of the response variable remains constant as the explanatory variables increase,and 4)the errors are probabilistically independent.

(True/False)

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Question 57

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

(Multiple Choice)

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Question 58

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

(True/False)

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Question 59

When determining whether to include or exclude a variable in regression analysis,if the p-value associated with the variable's t-value is above some accepted significance value,such as 0.05,then the variable:

(Multiple Choice)

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Question 60

Showing 41 - 60 of 69

One of the potential characteristics of an outlier is that the value of the dependent variable is much larger or smaller than predicted by the regression line.

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

If residuals separated by one period are autocorrelated,this is called:

If exact multicollinearlity exists,that means that there is redundancy in the data.

The t-value for testing $H _ { 0 } : B _ { i } = 0$ Is calculated using which of the following equations:

Many statistical packages have three types of equation-building procedures.They are:

In regression analysis,extrapolation is performed when you:

The value of the sum of squares due to regression,SSR,can never be larger than the value of the sum of squares total,SST.

A backward procedure is a type of equation building procedure that begins with all potential explanatory variables in the regression equation and deletes them two at a time until further deletion would reduce the percentage of variation explained to a value less than 0.50.

The appropriate hypothesis test for a regression coefficient is:

In simple linear regression,if the error variable $\varepsilon$ is normally distributed,the test statistic for testing $H _ { 0 } : B _ { 1 } = 0$ is t-distributed with n - 2 degrees of freedom.

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

The residuals are observations of the error variable $\varepsilon$ .Consequently,the minimized sum of squared deviations is called the sum of squared error,labeled SSE.

Multicollinearity is a situation in which two or more of the explanatory variables are highly correlated with each other.

A forward procedure is a type of equation building procedure that begins with only one explanatory variable in the regression equation and successively adds one variable at a time until no remaining variables make a significant contribution.

The assumptions of regression are: 1)there is a population regression line,2)the dependent variable is normally distributed,3)the standard deviation of the response variable remains constant as the explanatory variables increase,and 4)the errors are probabilistically independent.

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

When determining whether to include or exclude a variable in regression analysis,if the p-value associated with the variable's t-value is above some accepted significance value,such as 0.05,then the variable:

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: Estimating Relationships

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 11: Regression Analysis: Statistical Inference

One of the potential characteristics of an outlier is that the value of the dependent variable is much larger or smaller than predicted by the regression line.

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

If residuals separated by one period are autocorrelated,this is called:

If exact multicollinearlity exists,that means that there is redundancy in the data.

The t-value for testing H0:Bi=0H _ { 0 } : B _ { i } = 0H0​:Bi​=0 Is calculated using which of the following equations:

Many statistical packages have three types of equation-building procedures.They are:

In regression analysis,extrapolation is performed when you:

The value of the sum of squares due to regression,SSR,can never be larger than the value of the sum of squares total,SST.

A backward procedure is a type of equation building procedure that begins with all potential explanatory variables in the regression equation and deletes them two at a time until further deletion would reduce the percentage of variation explained to a value less than 0.50.

The appropriate hypothesis test for a regression coefficient is:

In simple linear regression,if the error variable ε\varepsilonε is normally distributed,the test statistic for testing H0:B1=0H _ { 0 } : B _ { 1 } = 0H0​:B1​=0 is t-distributed with n - 2 degrees of freedom.

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

The residuals are observations of the error variable ε\varepsilonε .Consequently,the minimized sum of squared deviations is called the sum of squared error,labeled SSE.

Multicollinearity is a situation in which two or more of the explanatory variables are highly correlated with each other.

A forward procedure is a type of equation building procedure that begins with only one explanatory variable in the regression equation and successively adds one variable at a time until no remaining variables make a significant contribution.

The assumptions of regression are: 1)there is a population regression line,2)the dependent variable is normally distributed,3)the standard deviation of the response variable remains constant as the explanatory variables increase,and 4)the errors are probabilistically independent.

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

When determining whether to include or exclude a variable in regression analysis,if the p-value associated with the variable's t-value is above some accepted significance value,such as 0.05,then the variable:

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: Estimating Relationships

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 t-value for testing $H _ { 0 } : B _ { i } = 0$ Is calculated using which of the following equations:

In simple linear regression,if the error variable $\varepsilon$ is normally distributed,the test statistic for testing $H _ { 0 } : B _ { 1 } = 0$ is t-distributed with n - 2 degrees of freedom.

The residuals are observations of the error variable $\varepsilon$ .Consequently,the minimized sum of squared deviations is called the sum of squared error,labeled SSE.