Exam 16: Analyzing and Forecasting Time-Series Data

An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally,thereby giving a fairer method of developing a forecast.

(True/False)

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Question 61

If you suspect that a nonlinear trend exists in your data,one way to deal with it in a trend-based forecasting application is to transform the independent variable,for example by squaring the time measure or maybe taking the square-root of the time measure.

(True/False)

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Question 62

Which of the following forecasting methods allows the decision maker to weigh the past time series differently to make the model more sensitive to more recent data?

(Multiple Choice)

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Question 63

If you suspect that your trend forecasting model may have autocorrelated forecast errors,which the following should you compute?

(Multiple Choice)

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Question 64

In a single exponential smoothing model,one smoothing constant is used to weigh the historical data,and the model is of primary value when the data do not exhibit trend or seasonal components.

(True/False)

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Question 65

Which of the following statements about the Durbin-Watson d statistic is true?

(Multiple Choice)

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Question 66

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 sales on average have been growing by more than 48 thousand annually.

(True/False)

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Question 67

Prior to conducting double exponential smoothing a simple linear regression is conducted and the trend equation is = 42 + 38.3t,so the smoothed constant process value should be C₀ = 38.3 and the smoothed trend value should be T₀ = 42.

(True/False)

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Question 68

You are given the following linear trend model: F_t = 345.60 - 200.5(t).This model implies that in year 1,the dependent variable had a value of 145.1.

(True/False)

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Question 69

In measuring forecast errors,the MAD and the square root of the MSE will provide similar (but not identical)values,in that both provide a measure of the "typical" amount of error in forecasts.

(True/False)

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Question 70

Assume that the year 2000 is used as the index base period and that sales were 12 million in the year 2000.If sales were 18 million in the year 2006,the simple index number for the year 2006 is:

(Multiple Choice)

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Question 71

The owners of Hal's Cookie Company have collected sales data for the past 8 months.These data are shown as follows: Using a smoothing constant equal to 0.20 and starting forecast in period 1 of 100,the forecast value for period 9 is approximately 104.2.

(True/False)

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Question 72

Double exponential smoothing is used instead of single exponential smoothing when extra smooth forecasts are desired.

(True/False)

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Question 73

When using the multiplicative time-series model to determine the seasonal indexes,the first step is to isolate the seasonal and random components from the cyclical and trend components.

(True/False)

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Question 74

The Norton Industrial Company has 12 quarters of historical sales data and is interested in forecasting sales for quarter 13.The following data are available: Based on this data,using simple exponential smoothing with a smoothing constant value equal to 0.10 and a starting forecast value equal to the sales in quarter 1 to forecast sales for period 13,what is the MAD value for periods 2-12?

(Essay)

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Question 75

The Zocor Company is interested in forecasting period 13 sales for a product.It has 12 months of historical data.The following shows the data and the forecasted values for periods 1-12 using a single exponential smoothing model with a smoothing constant value equal to 0.20. What is the forecast bias value for the model over periods 2-12?

(Multiple Choice)

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Question 76

Large values of the Durbin-Watson d statistic indicate that positive autocorrelation among the forecast errors exists.

(True/False)

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Question 77

The Morgan Company is interested in developing a forecast for next month's sales.It has collected sales data for the past 12 months. Assuming that the company plans to use double exponential smoothing with starting values for the smoothed constant process value and smoothed trend value of 98.97 and 13.16 respectively,the MAD value for periods 2-12 is greater when alpha = 0.20 and beta = 0.20 than when alpha = 0.10 and beta = 0.10.

(True/False)

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Question 78

The Norton Industrial Company has 12 quarters of historical sales data and is interested in forecasting sales for quarter 13.The following data are available: Based on this data,use simple exponential smoothing with a smoothing constant value equal to 0.10 and a starting forecast value equal to the sales in quarter 1 to forecast sales for period 13.

(Essay)

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Question 79

A company has developed a linear trend regression model based on 16 quarters of data.The independent variable is the measure of time (t = 1 thru 16 where quarter 1 is winter quarter,2 is spring,etc.).The company has also developed seasonal indexes for each quarter as follows: The linear trend forecast equation is: = 120 + 56t. Given this information,what is the seasonally adjusted forecast for period 19?

(Multiple Choice)

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Question 80

Showing 61 - 80 of 133

An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally,thereby giving a fairer method of developing a forecast.

If you suspect that a nonlinear trend exists in your data,one way to deal with it in a trend-based forecasting application is to transform the independent variable,for example by squaring the time measure or maybe taking the square-root of the time measure.

Which of the following forecasting methods allows the decision maker to weigh the past time series differently to make the model more sensitive to more recent data?

If you suspect that your trend forecasting model may have autocorrelated forecast errors,which the following should you compute?

In a single exponential smoothing model,one smoothing constant is used to weigh the historical data,and the model is of primary value when the data do not exhibit trend or seasonal components.

Which of the following statements about the Durbin-Watson d statistic is true?

Prior to conducting double exponential smoothing a simple linear regression is conducted and the trend equation is = 42 + 38.3t,so the smoothed constant process value should be C₀ = 38.3 and the smoothed trend value should be T₀ = 42.

You are given the following linear trend model: F_t = 345.60 - 200.5(t).This model implies that in year 1,the dependent variable had a value of 145.1.

In measuring forecast errors,the MAD and the square root of the MSE will provide similar (but not identical)values,in that both provide a measure of the "typical" amount of error in forecasts.

Assume that the year 2000 is used as the index base period and that sales were 12 million in the year 2000.If sales were 18 million in the year 2006,the simple index number for the year 2006 is:

The owners of Hal's Cookie Company have collected sales data for the past 8 months.These data are shown as follows: Using a smoothing constant equal to 0.20 and starting forecast in period 1 of 100,the forecast value for period 9 is approximately 104.2.

Double exponential smoothing is used instead of single exponential smoothing when extra smooth forecasts are desired.

When using the multiplicative time-series model to determine the seasonal indexes,the first step is to isolate the seasonal and random components from the cyclical and trend components.

Large values of the Durbin-Watson d statistic indicate that positive autocorrelation among the forecast errors exists.

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

Filters

Exam 16: Analyzing and Forecasting Time-Series Data

An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally,thereby giving a fairer method of developing a forecast.

If you suspect that a nonlinear trend exists in your data,one way to deal with it in a trend-based forecasting application is to transform the independent variable,for example by squaring the time measure or maybe taking the square-root of the time measure.

Which of the following forecasting methods allows the decision maker to weigh the past time series differently to make the model more sensitive to more recent data?

If you suspect that your trend forecasting model may have autocorrelated forecast errors,which the following should you compute?

In a single exponential smoothing model,one smoothing constant is used to weigh the historical data,and the model is of primary value when the data do not exhibit trend or seasonal components.

Which of the following statements about the Durbin-Watson d statistic is true?

Prior to conducting double exponential smoothing a simple linear regression is conducted and the trend equation is = 42 + 38.3t,so the smoothed constant process value should be C0 = 38.3 and the smoothed trend value should be T0 = 42.

You are given the following linear trend model: Ft = 345.60 - 200.5(t).This model implies that in year 1,the dependent variable had a value of 145.1.

In measuring forecast errors,the MAD and the square root of the MSE will provide similar (but not identical)values,in that both provide a measure of the "typical" amount of error in forecasts.

Assume that the year 2000 is used as the index base period and that sales were 12 million in the year 2000.If sales were 18 million in the year 2006,the simple index number for the year 2006 is:

The owners of Hal's Cookie Company have collected sales data for the past 8 months.These data are shown as follows: Using a smoothing constant equal to 0.20 and starting forecast in period 1 of 100,the forecast value for period 9 is approximately 104.2.

Double exponential smoothing is used instead of single exponential smoothing when extra smooth forecasts are desired.

When using the multiplicative time-series model to determine the seasonal indexes,the first step is to isolate the seasonal and random components from the cyclical and trend components.

Large values of the Durbin-Watson d statistic indicate that positive autocorrelation among the forecast errors exists.

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

Filters

Prior to conducting double exponential smoothing a simple linear regression is conducted and the trend equation is = 42 + 38.3t,so the smoothed constant process value should be C₀ = 38.3 and the smoothed trend value should be T₀ = 42.

You are given the following linear trend model: F_t = 345.60 - 200.5(t).This model implies that in year 1,the dependent variable had a value of 145.1.