Question 1

Which model is the most accurate? Why?

Accepted Answer

Model 2 is most accu

Question 2

Based on the following data,a forecaster used simple exponential smoothing and determined the following: SS1U1B10S1U1B0 = 19,SS1U1B11S1U1B0 = 18.6,SS1U1B12S1U1B0 = 19.08,SS1U1B13S1U1B0 = 19.064,SS1U1B14S1U1B0 = 19.851 and SS1U1B15S1U1B0 = 19.481. $$\begin{array} { c c } 
\text { Time } & \text { Demand } \
1 & 17 \
2 & 21 \
3 & 19 \
4 & 23 \
5 & 18
\end{array}$$
-Consider the following data and calculations.Calculate the value of b1 and b0 and state the linear trend regression prediction equation.

Accepted Answer

@#IMG-DLM& 
 @#IMG-DLM& 
= 28

Question 3

When using simple exponential smoothing,the value of the smoothing constant &#61537;must be between ____ and ____.

Accepted Answer

A) 0,× 
B) 0,100 
C) -1,1 
D) -1,0 
E) 0,1 
A) 0,× 
B) 0,100 
C) -1,1 
D) -1,0 
E) 0,1

Question 4

The purpose of computing moving averages and centred moving averages is to eliminate seasonal variations and irregular component from time series data.

Accepted Answer

A) True 
 B)False

Question 5

A restaurant has been experiencing higher sales during the weekends compared to the weekdays.Daily restaurant sales patterns for this restaurant over a week are an example of _________ component of time series.

Accepted Answer

A) trend 
B) seasonal variation 
C) cycle 
D) irregular component 
E) random 
A) trend 
B) seasonal variation 
C) cycle 
D) irregular component 
E) random

Question 6

When deseasonalizing time series observations,we divide the actual time series observation by its ___________.

Accepted Answer

The answer of When deseasonalizing time series observations,we divide the...

Question 7

Use the following information for the three grains. $$\begin{array} { l c c c c } 
& \text { Price } & 1990 \text { Quantity } & \text { Price } & 2000 \text { Quantity } \
\text { Rice } & 30 & 2 & 35 & 1 \
\text { Wheat } & 25 & 3 & 30 & 2 \
\text { Oats } & 20 & 4 & 25 & 3
\end{array}$$
-Calculate the year 2000 aggregate price index.

Accepted Answer

The answer of Use the following information for the three...

Question 8

When deseasonalizing a time series observation,the actual time series observation is divided by its seasonal factor.

Accepted Answer

A) True 
 B)False

Question 9

The basic difference between MAD and MSD is that MSD,unlike MAD,penalizes a forecasting technique much more for _____ errors.

Accepted Answer

The answer of The basic difference between MAD and MSD...

Question 10

In the multiplicative decomposition method,the _________ moving average provides an estimate of TRt × CLt

Accepted Answer

Question 11

If a time series exhibits increasing seasonal variation,one approach is to first use a ______________ transformation that produces a transformed time series that exhibits constant seasonal variation.

Accepted Answer

The answer of If a time series exhibits increasing seasonal...

Question 12

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: $$\begin{array} { l l l } 
\text { Forecast Model 1 } & \text { Forecast Model 2 } & \text { Actual Value } \
7.5 & 6.3 & 6.0 \
6.3 & 6.7 & 6.6 \
5.4 & 7.1 & 7.3 \
8.2 & 7.5 & 9.4
\end{array}$$
-Calculate the mean absolute deviation (MAD)for Model 2.

Accepted Answer

The answer of Two forecasting models were used to predict...

Question 13

A time series exhibits no trend,no seasonal variation,and no cycle.However,the average measurement is changing slowly over time.The most appropriate way to model this time series would be to use

Accepted Answer

A) simple exponential smoothing. 
B) multiplicative decomposition. 
C) a multiple regression model with four predictor variables;one for time and three dummy variables. 
D) Holt-Winters' double exponential smoothing model. 
E) a no-trend regression model. 
A) simple exponential smoothing. 
B) multiplicative decomposition. 
C) a multiple regression model with four predictor variables;one for time and three dummy variables. 
D) Holt-Winters' double exponential smoothing model. 
E) a no-trend regression model.

Question 14

Use the following information for the three grains. $$\begin{array} { l c c c c } 
& \text { Price } & 1990 \text { Quantity } & \text { Price } & 2000 \text { Quantity } \
\text { Rice } & 30 & 2 & 35 & 1 \
\text { Wheat } & 25 & 3 & 30 & 2 \
\text { Oats } & 20 & 4 & 25 & 3
\end{array}$$
-Calculate the year 2000 simple price index for each grain separately.

Accepted Answer

The answer of Use the following information for the three...

Question 15

$$\begin{array} { c c c } 
\text { Week } & \text { Revenue } & \text { Forecasted Revenue } \
1 & 200 & 225 \
2 & 240 & 220 \
3 & 300 & 285 \
4 & 270 & 290 \
5 & 230 & 250 \
6 & 260 & 240 \
7 & 210 & 250 \
8 & 275 & 240
\end{array}$$
-Compute the mean absolute deviation (MAD).

Accepted Answer

21.875 @#IMG-DLM& 
M

Question 16

A time series obtained from quarterly data exhibits an increasing linear trend and increasing seasonal variation.Which one of the following would be the most appropriate way to model this time series?

Accepted Answer

A) Simple exponential smoothing. 
B) Multiplicative decomposition. 
C) Box-Jenkins methodology. 
D) Holt-Winters' double exponential smoothing model. 
E) Durbin-Watson statistic. 
A) Simple exponential smoothing. 
B) Multiplicative decomposition. 
C) Box-Jenkins methodology. 
D) Holt-Winters' double exponential smoothing model. 
E) Durbin-Watson statistic.

Question 17

The linear trend equation for the following data is $$\hat { y } = 1.4286 + 2.5 ( t )$$ $$\begin{array} { c c } 
\text { Month } & \text { Value of Fund } \
1 & 4 \
2 & 6 \
3 & 9 \
4 & 12 \
5 & 14 \
6 & 16 \
7 & 19
\end{array}$$
-Find the forecast errors for period 3,and period 7.

Accepted Answer

The answer of The linear trend equation for the following...

Question 18

The following data on prices and quantities for the years 1995 and 2000 are given for three products. $$\begin{array} { c c c c c c } 
\text { Product } & 1995 \text { Price } & \text { Quantity } & \text { Product } & 2000 \text { Price } & \text { Quantity } \
\mathrm{X} & \$ 4 & 10 & \mathrm{X} & \$ 8 & 15 \
\mathrm{Y} & \$ 8 & 15 & \mathrm{Y} & \$ 6 & 10 \
\mathrm{Z} & \$ 3 & 8 & \mathrm{Z} & \$ 4 & 12
\end{array}$$
-Calculate the Paasche index.

Accepted Answer

123.91
Paa

Question 19

Consider the following time series with forecast values and errors.
$$\begin{array} { | l | l | } 
\hline \begin{array} { l } 
\text { Actual } \
\text { Demand }
\end{array} & \begin{array} { l } 
\text { Forecast } \
\text { Demand }
\end{array} \
\hline 10 & 9 \
\hline 13 & 15 \
\hline 15 & 16 \
\hline 11 & 13 \
\hline 12 & 10 \
\hline
\end{array}$$
-Calculate the mean absolute deviation (MAD).

Accepted Answer

A) 2.8 
B) 13.4 
C) 7.5 
D) 1.6 
E) 2.4 
A) 2.8 
B) 13.4 
C) 7.5 
D) 1.6 
E) 2.4

Question 20

Consider the following time series with forecast values and errors.
$$\begin{array} { | l | l | } 
\hline \begin{array} { l } 
\text { Actual } \
\text { Demand }
\end{array} & \begin{array} { l } 
\text { Forecast } \
\text { Demand }
\end{array} \
\hline 10 & 9 \
\hline 13 & 15 \
\hline 15 & 16 \
\hline 11 & 13 \
\hline 12 & 10 \
\hline
\end{array}$$
-Calculate the mean absolute deviation (MAPE).

Accepted Answer

A) 2.8 
B) 13.4 
C) 7.5 
D) 1.6 
E) 2.4 
A) 2.8 
B) 13.4 
C) 7.5 
D) 1.6 
E) 2.4

Which model is the most accurate? Why?

When using simple exponential smoothing,the value of the smoothing constant must be between and .

The purpose of computing moving averages and centred moving averages is to eliminate seasonal variations and irregular component from time series data.

A restaurant has been experiencing higher sales during the weekends compared to the weekdays.Daily restaurant sales patterns for this restaurant over a week are an example of _________ component of time series.

When deseasonalizing time series observations,we divide the actual time series observation by its ___________.

Use the following information for the three grains. Price 1990 Quantity Price 2000 Quantity Rice 30 2 35 1 Wheat 25 3 30 2 Oats 20 4 25 3 -Calculate the year 2000 aggregate price index.

When deseasonalizing a time series observation,the actual time series observation is divided by its seasonal factor.

The basic difference between MAD and MSD is that MSD,unlike MAD,penalizes a forecasting technique much more for _____ errors.

In the multiplicative decomposition method,the _________ moving average provides an estimate of TR_t × CL_t

If a time series exhibits increasing seasonal variation,one approach is to first use a ______________ transformation that produces a transformed time series that exhibits constant seasonal variation.

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: Forecast Model 1 Forecast Model 2 Actual Value 7.5 6.3 6.0 6.3 6.7 6.6 5.4 7.1 7.3 8.2 7.5 9.4 -Calculate the mean absolute deviation (MAD)for Model 2.

A time series exhibits no trend,no seasonal variation,and no cycle.However,the average measurement is changing slowly over time.The most appropriate way to model this time series would be to use

Use the following information for the three grains. Price 1990 Quantity Price 2000 Quantity Rice 30 2 35 1 Wheat 25 3 30 2 Oats 20 4 25 3 -Calculate the year 2000 simple price index for each grain separately.

Week Revenue Forecasted Revenue 1 200 225 2 240 220 3 300 285 4 270 290 5 230 250 6 260 240 7 210 250 8 275 240 -Compute the mean absolute deviation (MAD).

A time series obtained from quarterly data exhibits an increasing linear trend and increasing seasonal variation.Which one of the following would be the most appropriate way to model this time series?

The linear trend equation for the following data is $\hat { y } = 1.4286 + 2.5 ( t )$ Month Value of Fund 1 4 2 6 3 9 4 12 5 14 6 16 7 19 -Find the forecast errors for period 3,and period 7.

The following data on prices and quantities for the years 1995 and 2000 are given for three products. Product 1995 Price Quantity Product 2000 Price Quantity \ 4 10 \ 8 15 \ 8 15 \ 6 10 \ 3 8 \ 4 12 -Calculate the Paasche index.

Consider the following time series with forecast values and errors. Actual Demand Forecast Demand 10 9 13 15 15 16 11 13 12 10 -Calculate the mean absolute deviation (MAD).

Consider the following time series with forecast values and errors. Actual Demand Forecast Demand 10 9 13 15 15 16 11 13 12 10 -Calculate the mean absolute deviation (MAPE).

An Introduction to Business Statistics

Descriptive Statistics

Probability

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Correlation Coefficient and Simple Linear Regression Analysis

Multiple Regression and Model Building

Nonparametric Methods

Chi-Square Tests

Decision Theory

Filters

Exam 16: Time Series Forecasting

Which model is the most accurate? Why?

When using simple exponential smoothing,the value of the smoothing constant must be between ____ and ____.

The purpose of computing moving averages and centred moving averages is to eliminate seasonal variations and irregular component from time series data.

A restaurant has been experiencing higher sales during the weekends compared to the weekdays.Daily restaurant sales patterns for this restaurant over a week are an example of _________ component of time series.

When deseasonalizing time series observations,we divide the actual time series observation by its ___________.

Use the following information for the three grains. Price 1990 Quantity Price 2000 Quantity Rice 30 2 35 1 Wheat 25 3 30 2 Oats 20 4 25 3 -Calculate the year 2000 aggregate price index.

When deseasonalizing a time series observation,the actual time series observation is divided by its seasonal factor.

The basic difference between MAD and MSD is that MSD,unlike MAD,penalizes a forecasting technique much more for _____ errors.

In the multiplicative decomposition method,the _________ moving average provides an estimate of TRt × CLt

If a time series exhibits increasing seasonal variation,one approach is to first use a ______________ transformation that produces a transformed time series that exhibits constant seasonal variation.

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: Forecast Model 1 Forecast Model 2 Actual Value 7.5 6.3 6.0 6.3 6.7 6.6 5.4 7.1 7.3 8.2 7.5 9.4 -Calculate the mean absolute deviation (MAD)for Model 2.

A time series exhibits no trend,no seasonal variation,and no cycle.However,the average measurement is changing slowly over time.The most appropriate way to model this time series would be to use

Use the following information for the three grains. Price 1990 Quantity Price 2000 Quantity Rice 30 2 35 1 Wheat 25 3 30 2 Oats 20 4 25 3 -Calculate the year 2000 simple price index for each grain separately.

Week Revenue Forecasted Revenue 1 200 225 2 240 220 3 300 285 4 270 290 5 230 250 6 260 240 7 210 250 8 275 240 -Compute the mean absolute deviation (MAD).

A time series obtained from quarterly data exhibits an increasing linear trend and increasing seasonal variation.Which one of the following would be the most appropriate way to model this time series?

The linear trend equation for the following data is y^=1.4286+2.5(t)\hat { y } = 1.4286 + 2.5 ( t )y^​=1.4286+2.5(t) Month Value of Fund 1 4 2 6 3 9 4 12 5 14 6 16 7 19 -Find the forecast errors for period 3,and period 7.

The following data on prices and quantities for the years 1995 and 2000 are given for three products. Product 1995 Price Quantity Product 2000 Price Quantity \ 4 10 \ 8 15 \ 8 15 \ 6 10 \ 3 8 \ 4 12 -Calculate the Paasche index.

Consider the following time series with forecast values and errors. Actual Demand Forecast Demand 10 9 13 15 15 16 11 13 12 10 -Calculate the mean absolute deviation (MAD).

Consider the following time series with forecast values and errors. Actual Demand Forecast Demand 10 9 13 15 15 16 11 13 12 10 -Calculate the mean absolute deviation (MAPE).

An Introduction to Business Statistics

Descriptive Statistics

Probability

Discrete Random Variables

Continuous Random Variables

Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Correlation Coefficient and Simple Linear Regression Analysis

Multiple Regression and Model Building

Nonparametric Methods

Chi-Square Tests

Decision Theory

Filters

When using simple exponential smoothing,the value of the smoothing constant must be between and .

In the multiplicative decomposition method,the _________ moving average provides an estimate of TR_t × CL_t

The linear trend equation for the following data is $\hat { y } = 1.4286 + 2.5 ( t )$ Month Value of Fund 1 4 2 6 3 9 4 12 5 14 6 16 7 19 -Find the forecast errors for period 3,and period 7.