Deck 13: Regression and Forecasting Models

Question 1

The biggest challenge of regression is:

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

Answer 1

B

Question 2

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.

Answer 2

True

Question 3

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

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

Answer 3

B

Question 4

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

Question 5

A "fan" shape in a scatterplot indicates:

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

Question 6

The adjusted R2 adjusts R2 for:

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

Question 7

Which of the following is not one of the commonly used summary measures for forecast errors

A) MAE (mean absolute error) 
B) MFE (mean forecast error) 
C) RMSE (root mean square error) 
D) MAPE (mean absolute percentage error)

Question 8

The term autocorrelation refers to:

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 9

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

Question 10

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

Question 11

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

Question 12

The least squares line is the line that minimizes the sum of the residuals.

Question 13

Forecasting models can be divided into three groups.They are:

A) time series, optimization, and simulation methods 
B) judgmental, regression, and extrapolation methods 
C) judgmental, random, and linear methods 
D) linear, non-linear, and extrapolation methods

Question 14

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

Question 15

Winter's method is an exponential smoothing method,which is appropriate for a series with trend but no seasonality.

Question 16

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

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 17

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

Question 18

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

A) 3 
B) 5 
C) 6 
D) 7

Question 19

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.

Question 20

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

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

Question 21

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.

Question 22

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.

Question 23

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.Use simple exponential smoothing to forecast these data,requesting 4 quarters of future forecasts.Use the default smoothing constant of 0.10.Is this better than the moving average model

Question 24

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 simple exponential smoothing forecast again,this time optimizing the smoothing constant.Does it make much of an improvement

Question 25

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.Use a moving average model to forecast these data,requesting 4 quarters of future forecasts.Use a span of 4 quarters.

Question 26

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.

Question 27

Exhibit 13-1
An express delivery service company recently conducted a study to investigate the relationship between the cost of shipping a package (Y), the package weight in pounds (X1), and the distance shipped in miles (X2). Twenty packages were randomly selected from among the large number received for shipment, and a detailed analysis of the shipping cost was conducted for each package. The sample information is shown in the table below:

Refer to Exhibit 13-1.Add the second explanatory variable (distance shipped)to the regression model.Estimate and interpret the slopes of this expanded model.

Question 28

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

Question 29

Exhibit 13-1
An express delivery service company recently conducted a study to investigate the relationship between the cost of shipping a package (Y), the package weight in pounds (X1), and the distance shipped in miles (X2). Twenty packages were randomly selected from among the large number received for shipment, and a detailed analysis of the shipping cost was conducted for each package. The sample information is shown in the table below:

Refer to Exhibit 13-1.Estimate a simple linear regression model involving shipping cost and package weight.Interpret the slope coefficient of the least squares line as well as R2.

Question 30

Exhibit 13-1
An express delivery service company recently conducted a study to investigate the relationship between the cost of shipping a package (Y), the package weight in pounds (X1), and the distance shipped in miles (X2). Twenty packages were randomly selected from among the large number received for shipment, and a detailed analysis of the shipping cost was conducted for each package. The sample information is shown in the table below:

Refer to Exhibit 13-1.How does the R2 value for this multiple regression model compare to that of the simple regression model estimated above
Interpret the adjusted R2 values for the two models.