Question 1

A correlation value of zero indicates.

Accepted Answer

A) a strong linear relationship 
B) a weak linear relationship 
C) no linear relationship 
D) a perfect linear relationship 
A) a strong linear relationship 
B) a weak linear relationship 
C) no linear relationship 
D) a perfect linear relationship

Question 2

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 3

Use what you have learned about transformations to fit an alternative model to the one in Question 135.

Accepted Answer

@#IMG-DLM& The above output is for a regression of

Question 4

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 5

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

Accepted Answer

A) mutually exclusive 
B) inversely related 
C) directly related 
D) highly correlated 
E) None of the above 
A) mutually exclusive 
B) inversely related 
C) directly related 
D) highly correlated 
E) None of the above

Question 6

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 7

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 8

(A)Use the information above to estimate the linear regression model.
(B)Interpret each of the estimated regression coefficients of the regression model in (A).
(C)Would any of the variables in this model be considered a dummy variable? Explain your answer.
(D)Identify and interpret the coefficient of determination (   )and the standard error of the estimate (se)for the model in (A).

Accepted Answer

(A) @#IMG-DLM& (B)This model shows that the occupa

Question 9

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

Accepted Answer

A) True 
 B)False

Question 10

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 11

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 12

The multiple standard error of estimate will be:

Accepted Answer

A) 0.901 
B) 0.888 
C) 0.800 
D) 0.953 
E) 0.894 
A) 0.901 
B) 0.888 
C) 0.800 
D) 0.953 
E) 0.894

Question 13

The correlation value ranges from

Accepted Answer

A) 0 to +1 
B) -1 to +1 
C) -2 to +2 
D) -¥ to+ ¥ 
A) 0 to +1 
B) -1 to +1 
C) -2 to +2 
D) -¥ to+ ¥

Question 14

A regression analysis between sales (in $1000)and advertising (in $)resulted in the following least squares line:   = 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 15

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 16

For the multiple regression model   ,if   were to increase by 5 units,holding   and   constant,the value of Y would be expected to decrease by 50 units.

Accepted Answer

A) True 
 B)False

Question 17

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

Accepted Answer

A) True 
 B)False

Question 18

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 19

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) 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 20

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

Accepted Answer

A) True 
 B)False

A correlation value of zero indicates.

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

Use what you have learned about transformations to fit an alternative model to the one in Question 135.

The term autocorrelation refers to:

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

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

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

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

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

The multiple standard error of estimate will be:

The correlation value ranges from

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

Outliers are observations that

For the multiple regression model ,if were to increase by 5 units,holding and constant,the value of Y would be expected to decrease by 50 units.

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

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

In multiple regression,the constant :

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

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters

Exam 10: Regression Analysis: Estimating Relationships

A correlation value of zero indicates.

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

Use what you have learned about transformations to fit an alternative model to the one in Question 135.

The term autocorrelation refers to:

A single variable X can explain a large percentage of the variation in some other variable Y when the two variables are:

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

In a nonlinear transformation of data,the Y variable or the X variables may be transformed,but not both.

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

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

The multiple standard error of estimate will be:

The correlation value ranges from

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

Outliers are observations that

For the multiple regression model ,if were to increase by 5 units,holding and constant,the value of Y would be expected to decrease by 50 units.

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

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

In multiple regression,the constant :

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

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters