Suppose an economist has developed a model for forecasting annual consumption,yt,as function of total labor income,x1t,and total property income,x2t based on 20 years on annual data.The following regression model has been developed: = 7.81 + 0.91x1t + 0.57x2t with the standard error = 1.29 and the Durbin-Watson d statistic = 2.09.Using an alpha = .05,which of the following is the correct critical value for testing whether the residuals are autocorrelated?

A) 1.20 and 1.41 B) 1.10 and 1.54 C) 1.08 and 1.53 D) Can't be determined without seeing the residuals. A) 1.20 and 1.41 B) 1.10 and 1.54 C) 1.08 and 1.53 D) Can't be determined without seeing the residuals.

Exam 16: Analyzing and Forecasting Time-Series Data

The Baker's Candy Company has been in business for three years.The quarterly sales data for the company are shown as follows: Based on this information,the data reflect both a linear trend and seasonal components.

(True/False)

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Question 21

Some stocks are referred to as cyclical stock because they tend to be in favor for several years and then out of favor for several years.This is a correct use of the term cyclical.

(True/False)

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Question 22

The forecasting interval is the unit of time for which forecasts are made.

(True/False)

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Question 23

While virtually all time series exhibit a random component,not all time series exhibit other components.

(True/False)

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Question 24

A company has recorded the 12 months of sales data for the past year.It wishes to use the regression approach to develop a trend-based approach for forecasting future months.The following data are provided: Based on this information,which of the following is forecast for month 15?

(Multiple Choice)

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Question 25

A seasonally unadjusted forecast is one that is made from seasonal data without any adjustment for the seasonal component in the time series.

(True/False)

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Question 26

In a recent meeting,the marketing manager for a large hardware company stated that he needed to have a forecast prepared for the next three months.The three-month period is called:

(Multiple Choice)

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Question 27

Which of the following is true about forecast bias?

(Multiple Choice)

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Question 28

Suppose an economist has developed a model for forecasting annual consumption,y_t,as function of total labor income,x₁_t,and total property income,x₂_t based on 20 years on annual data.The following regression model has been developed: = 7.81 + 0.91x₁_t+ 0.57x₂_t with the standard error = 1.29 and the Durbin-Watson d statistic = 2.09.Using an alpha = .05,which of the following is the correct critical value for testing whether the residuals are autocorrelated?

(Multiple Choice)

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Question 29

The reason for testing for the presence of autocorrelation in a regression-based trend forecasting model is that one assumption of the regression analysis is that the residuals are not correlated.

(True/False)

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Question 30

If the Durbin-Watson test leads you to reject the null hypothesis,then you are concluding that the forecast errors are positively autocorrelated.

(True/False)

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Question 31

Which of the following approaches might be used to determine which value for the smoothing constant to use in a single exponential smoothing model?

(Multiple Choice)

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Question 32

If a manager is planning for an expansion of the factory,a forecast model with a long-term planning horizon would probably be used.

(True/False)

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Question 33

In a double smoothing model,the second smoothing constant is introduced to account for the trend in the data if one exists.

(True/False)

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Question 34

If a forecasting model produces forecast errors (residuals)that are negatively correlated,then we expect a negative residual to be followed by another negative residual to be followed by another negative residual and so forth.

(True/False)

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Question 35

One of the basic tools for creating a trend-based forecasting model is regression analysis.

(True/False)

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Question 36

In a time series with quarterly sales data,assume that the seasonal index for the summer quarter has been found to be 0.87,this can be interpreted to mean that sales tend to be 87 percent higher in the summer quarter when compared to the other quarters.

(True/False)

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Question 37

The Morgan Company is interested in developing a forecast for next month's sales.It has collected sales data for the past 12 months. After analyzing these data,if the company wishes to use exponential smoothing with alpha = 0.20 and beta = 0.20,the starting values for the constant process and the trend process can be derived from a linear trend regression model by using the intercept and slope coefficient respectively.

(True/False)

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Question 38

Renton Industries makes replacement parts for the automobile industry.As part of the company's capacity planning,it needs a long-range total demand forecast.The following information was generated based on 10 years of historical data on total number of parts sold each year. Based on this information we can conclude that the linear trend model explains a significant proportion of the variation in the number of parts sold,because the p-value is much smaller than any reasonable α that we might use.

(True/False)

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Question 39

Given the following time series data: and using smoothing constants of alpha = 0.3,the exponential smoothing forecast for time period 3 is

(Multiple Choice)

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Question 40

Showing 21 - 40 of 133

The Baker's Candy Company has been in business for three years.The quarterly sales data for the company are shown as follows: Based on this information,the data reflect both a linear trend and seasonal components.

Some stocks are referred to as cyclical stock because they tend to be in favor for several years and then out of favor for several years.This is a correct use of the term cyclical.

The forecasting interval is the unit of time for which forecasts are made.

While virtually all time series exhibit a random component,not all time series exhibit other components.

A company has recorded the 12 months of sales data for the past year.It wishes to use the regression approach to develop a trend-based approach for forecasting future months.The following data are provided: Based on this information,which of the following is forecast for month 15?

A seasonally unadjusted forecast is one that is made from seasonal data without any adjustment for the seasonal component in the time series.

In a recent meeting,the marketing manager for a large hardware company stated that he needed to have a forecast prepared for the next three months.The three-month period is called:

Which of the following is true about forecast bias?

The reason for testing for the presence of autocorrelation in a regression-based trend forecasting model is that one assumption of the regression analysis is that the residuals are not correlated.

If the Durbin-Watson test leads you to reject the null hypothesis,then you are concluding that the forecast errors are positively autocorrelated.

Which of the following approaches might be used to determine which value for the smoothing constant to use in a single exponential smoothing model?

If a manager is planning for an expansion of the factory,a forecast model with a long-term planning horizon would probably be used.

In a double smoothing model,the second smoothing constant is introduced to account for the trend in the data if one exists.

If a forecasting model produces forecast errors (residuals)that are negatively correlated,then we expect a negative residual to be followed by another negative residual to be followed by another negative residual and so forth.

One of the basic tools for creating a trend-based forecasting model is regression analysis.

In a time series with quarterly sales data,assume that the seasonal index for the summer quarter has been found to be 0.87,this can be interpreted to mean that sales tend to be 87 percent higher in the summer quarter when compared to the other quarters.

Given the following time series data: and using smoothing constants of alpha = 0.3,the exponential smoothing forecast for time period 3 is

The Where, why, and How of Data Collection

Graphs,charts and Tablesdescribing Your Data

Describing Data Using Numerical Measures

Using Probability and Probability Distributions

Discrete Probability Distributions

Introduction to Continuous Probability Distributions

Introduction to Sampling Distributions

Estimating Single Population Parameters

Introduction to Hypothesis Testing

Estimation and Hypothesis Testing for Two Population Parameters

Hypothesis Tests and Estimation for Population Variances

Analysis of Variance

Goodness-Of-Fit Tests and Contingency Analysis

Introduction to Linear Regression and Correlation Analysis

Multiple Regression Analysis and Model Building

Introduction to Nonparametric Statistics

Introduction to Quality and Statistical Process Control

Introduction to Decision Analysis

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