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Exam 14: Regression and Forecasting Models

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Question 1

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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.

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B) False

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Question 2

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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.

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B) False

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Question 3

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

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

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

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

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

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

Exhibit 14-2 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 14-2.Interpret each of the estimated regression coefficients of the regression model above.

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

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

Exhibit 14-3 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 14-3.Obtain a time series chart.Which of the forecasting models do you think should be used for forecasting based on this chart? Why?

Exhibit 14-2 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 14-2.Use the information above to estimate the linear regression model.

The term autocorrelation refers to:

Exhibit 14-1 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 14-1.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.

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

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

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

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for:

Exhibit 14-3 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 14-3.Obtain a simple exponential smoothing forecast again,this time optimizing the smoothing constant.Does it make much of an improvement?