Question 1

In regression analysis,we can often use the standard error of estimate se to judge which of several potential regression equations is the most useful.

Accepted Answer

A) True 
 B)False  True

Question 2

The biggest challenge of regression is:

Accepted Answer

A)  differentiating the independent variable(s) from the dependent variable(s) 
B)  determining which independent variable(s) to include 
C)  collecting accurate data 
D)  properly coding the variables 
A)  differentiating the independent variable(s) from the dependent variable(s) 
B)  determining which independent variable(s) to include 
C)  collecting accurate data 
D)  properly coding the variables B

Question 3

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

Accepted Answer

A) True 
 B)False  True

Question 4

A "fan" shape in a scatterplot indicates:

Accepted Answer

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

Question 5

When using the moving average method,you must select ____ which represent(s)the number of terms in the moving average.

Accepted Answer

A)  a smoothing constant 
B)  the explanatory variables 
C)  an alpha value 
D)  a span 
A)  a smoothing constant 
B)  the explanatory variables 
C)  an alpha value 
D)  a span

Question 6

Exhibit 13-2
The station manager of a local television station is interested in predicting the amount of television (in hours) that people will watch in the viewing area. The explanatory variables are: X1 age (in years), X2 education (highest level obtained, in years) and X3 family size (number of family members in household). The multiple regression output is shown below:
​  
-Refer to Exhibit 13-2.Use the information above to estimate the linear regression model.

Accepted Answer

The answer of Exhibit 13-2
The station manager of a local...

Question 7

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

Accepted Answer

A)  Y when the associated X value increases by one unit, holding the other variables constant 
B)  X when the associated Y value increases by one unit, holding the other variables constant 
C)  Y when the associated X value decreases by one unit, holding the other variables constant 
D)  X when the associated Y value decreases by one unit, holding the other variables constant 
A)  Y when the associated X value increases by one unit, holding the other variables constant 
B)  X when the associated Y value increases by one unit, holding the other variables constant 
C)  Y when the associated X value decreases by one unit, holding the other variables constant 
D)  X when the associated Y value decreases by one unit, holding the other variables constant

Question 8

A model that uses temperature,season of the year (fall,winter,spring,summer),and whether or not it is a weekend,to predict the # of customers for the day would include how many independent variables

Accepted Answer

A)  3 
B)  5 
C)  6 
D)  7 
A)  3 
B)  5 
C)  6 
D)  7

Question 9

The percentage of variation explained R2 is the square of the correlation between the observed Y values and the fitted Y values.

Accepted Answer

A) True 
 B)False

Question 10

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 11

Exhibit 13-2
The station manager of a local television station is interested in predicting the amount of television (in hours) that people will watch in the viewing area. The explanatory variables are: X1 age (in years), X2 education (highest level obtained, in years) and X3 family size (number of family members in household). The multiple regression output is shown below:
​  
-Refer to Exhibit 13-2.Interpret each of the estimated regression coefficients of the regression model above.

Accepted Answer

This model shows that the number of hour

Question 12

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

Question 13

Exhibit 13-3
The quarterly numbers of applications for home mortgage loans at a branch office of a large bank are recorded in the table below.
  
-Refer to Exhibit 13-3.Obtain a time series chart.Which of the forecasting models (one or more)do you think should be used for forecasting based on this chart
 Why

Accepted Answer

There is no apparent trend or seasonalit

Question 14

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 15

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 16

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

Question 17

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 18

Exhibit 13-2
The station manager of a local television station is interested in predicting the amount of television (in hours) that people will watch in the viewing area. The explanatory variables are: X1 age (in years), X2 education (highest level obtained, in years) and X3 family size (number of family members in household). The multiple regression output is shown below:
​  
-Refer to Exhibit 13-2.Identify and interpret the percentage of variation explained (R2)for the model.

Accepted Answer

The percentage of variation ex

Question 19

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.

Accepted Answer

A) True 
 B)False

Question 20

The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages.

Accepted Answer

A) True 
 B)False

In regression analysis,we can often use the standard error of estimate se to judge which of several potential regression equations is the most useful.

The biggest challenge of regression is:

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

A "fan" shape in a scatterplot indicates:

When using the moving average method,you must select ____ which represent(s)the number of terms in the moving average.

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

A model that uses temperature,season of the year (fall,winter,spring,summer),and whether or not it is a weekend,to predict the # of customers for the day would include how many independent variables

The percentage of variation explained R² is the square of the correlation between the observed Y values and the fitted Y values.

The term autocorrelation refers to:

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

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

The adjusted R² adjusts R² for:

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

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.

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.

The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages.

Introduction to Modeling

Introduction to Spreadsheet Modeling

Introduction to Optimization Modeling

Linear Programming Models

Network Models

Optimization Models With Integer Variables

Nonlinear Optimization Models

Evolutionary Solver: An Alternative Optimization Procedure

Decision Making Under Uncertainty

Introduction to Simulation Modeling

Simulation Models

Queueing Models

Data Mining

Filters

Exam 13: Regression and Forecasting Models

In regression analysis,we can often use the standard error of estimate se to judge which of several potential regression equations is the most useful.

The biggest challenge of regression is:

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

A "fan" shape in a scatterplot indicates:

When using the moving average method,you must select ____ which represent(s)the number of terms in the moving average.

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

A model that uses temperature,season of the year (fall,winter,spring,summer),and whether or not it is a weekend,to predict the # of customers for the day would include how many independent variables

The percentage of variation explained R2 is the square of the correlation between the observed Y values and the fitted Y values.

The term autocorrelation refers to:

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

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

The adjusted R2 adjusts R2 for:

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

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.

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 X.

The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages.

Introduction to Modeling

Introduction to Spreadsheet Modeling

Introduction to Optimization Modeling

Linear Programming Models

Network Models

Optimization Models With Integer Variables

Nonlinear Optimization Models

Evolutionary Solver: An Alternative Optimization Procedure

Decision Making Under Uncertainty

Introduction to Simulation Modeling

Simulation Models

Queueing Models

Data Mining

Filters

The percentage of variation explained R² is the square of the correlation between the observed Y values and the fitted Y values.

The adjusted R² adjusts R² for:

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

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.