Question 1

The time-series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called:

Accepted Answer

A)  trend. 
B)  cyclical. 
C)  seasonal. 
D)  random variation. 
A)  trend. 
B)  cyclical. 
C)  seasonal. 
D)  random variation.

Question 2

Which of the following represents the fluctuations up to a twelve month time period?

Accepted Answer

A)  Seasonal component 
B)  Irregular component 
C)  Trend 
D)  Cyclical component 
A)  Seasonal component 
B)  Irregular component 
C)  Trend 
D)  Cyclical component

Question 3

A company selling swimming goggles wants to analyze the company's Australian sales figure.
Describe the 4 components of the line graph of Swimming goggle quarterly sales (in $000's) given below.

Accepted Answer

Trend = 12.237 + 0.27t
The tre

Question 4

If autumn 2013 sales were $20 500 and the summer seasonal index was 1.17, then the deseasonalised 2010 autumn sales value would be:

Accepted Answer

A)  $19 422. 
B)  $13 675. 
C)  $14 188. 
D)  $18 720. 
A)  $19 422. 
B)  $13 675. 
C)  $14 188. 
D)  $18 720.

Question 5

The regression trend line for annual energy consumption for 1985-2005 is given by ŷt = 70 + 0.53t, where t = 1 for 1985. If the annual energy consumption for 2000 was 82.5, then the percentage of trend for 2000 was:

Accepted Answer

A)  105.122%. 
B)  95.127%. 
C)  104.321%. 
D)  98.462%. 
A)  105.122%. 
B)  95.127%. 
C)  104.321%. 
D)  98.462%.

Question 6

The trend line ?t = 14.13 - 0.54t was calculated from quarterly data for 2006-2010, where t = 1 for the first quarter of 2006. The trend value for the fourth quarter of the year 2011 is 1.170.

Accepted Answer

A) True 
 B)False

Question 7

The smoothing component is one of the four components of a time series.

Accepted Answer

A) True 
 B)False

Question 8

In an exponentially smoothed time series, the smoothing constant w is chosen on the basis of how much smoothing is required. In general:

Accepted Answer

A)  a small value of w, such as w = 0.1, results in very little smoothing, while a large value, such as w = 0.8, results in too much smoothing. 
B)  a small value of w, such as w = 0.1, results in too much smoothing, while a large value such as w = 0.8, results in very little smoothing. 
C)  a small value of w, such as w = 0.1, and a large value, such as w = 0.8, may both result in very little smoothing. 
D)  a small value of w, such as w = 0.1, and a large value, such as w = 0.8, may both result in too much smoothing. 
A)  a small value of w, such as w = 0.1, results in very little smoothing, while a large value, such as w = 0.8, results in too much smoothing. 
B)  a small value of w, such as w = 0.1, results in too much smoothing, while a large value such as w = 0.8, results in very little smoothing. 
C)  a small value of w, such as w = 0.1, and a large value, such as w = 0.8, may both result in very little smoothing. 
D)  a small value of w, such as w = 0.1, and a large value, such as w = 0.8, may both result in too much smoothing.

Question 9

We compute the five-period moving averages for all time periods except the first two and the last two.

Accepted Answer

A) True 
 B)False

Question 10

If summer 2010 sales were $26 800 and the summer seasonal index was 1.15, the deseasonalised 2010 summer sales value is $30 820.

Accepted Answer

A) True 
 B)False

Question 11

We calculate the three-period moving averages for a time series for all time periods except the:

Accepted Answer

A)  first and last period. 
B)  first two periods. 
C)  last two periods. 
D)  first and last two periods. 
A)  first and last period. 
B)  first two periods. 
C)  last two periods. 
D)  first and last two periods.

Question 12

Annual production (in millions) of computer chips in a large electronics company was recorded, as shown below. $$\begin{array} { | c | c c | } 
\hline \text { Year } & t & \text { Production } \
\hline 1990 & 1 & 26 \
1991 & 2 & 23 \
1992 & 3 & 21 \
1993 & 4 & 25 \
1994 & 5 & 32 \
1995 & 6 & 38 \
1996 & 7 & 43 \
1997 & 8 & 36 \
1998 & 9 & 29 \
1999 & 10 & 25 \
\hline
\end{array}$$ a. Calculate the percentage of trend for each time period.
b. Plot the percentage of trend.
c. Describe the cyclical effect (if there is one).

Accepted Answer

a. @#IMG-DLM& b. @#IMG-DLM& c. There app

Question 13

A time series is shown in the table below: $\quad\quad\quad\quad\quad\quad\quad\quad\text { Week }$
$\begin{array}{|l|cccc|}
\hline D a y & 1 & 2 & 3 & 4 \
\hline \text { Monday } & 16 & 15 & 18 & 21 \
\text { Tuesday } & 22 & 21 & 20 & 25 \
\text { Wednesday } & 20 & 23 & 20 & 24 \
\text { Thursday } & 29 & 28 & 32 & 28 \
\text { Friday } & 35 & 31 & 29 & 36 \
\hline
\end{array}$ a. Compute the five-day moving averages to remove the seasonal and random variation.
b. Calculate the seasonal (daily) indexes.
c. What do the daily indexes tell us?
d. Find the regression trend line.
e. Calculate the seasonal indexes, based on the regression trend line developed in (d).

Accepted Answer

a. @#IMG-DLM& b. @#IMG-DLM& c. The daily indexes tell us tha

Question 14

A company selling swimming goggles wants to analyze the company's Australian sales time figures.
Time series forecasting with regression was used to generate Excel output to estimate trend of the time series of Swimming goggle sales (in thousands of dollars) where the origin is the March Quarter 2000. $\begin{array}{l}
\text { SUMMARV OUTPUT }\
\hline\text { Regression Stotitics }\
\begin{array}{lc}
\hline \text { Multiple R } & 0.37281 \
\text { R Square } & 0.13899 \
\text { Adjusted R } & \
\text { Square } & 0.12243 \
\text { StandardError } & 10.3925 \
\text { Observations } & 54\
\hline
\end{array}
\end{array}$

$\text { ANOVA }$
$\begin{array}{lccccc} 
\hline& & & & & \text { Sgnificance } \
& d f & S & M S & F & F \
\hline \text { Regression } & 1 & 906.5867925 & 906.59 & 8.39406 & 0.005497292 \
\text { Residual } & 52 & 5616.172467 & 108 & & \
\text { Total } & 53 & 6522.759259 & & & \\hline
\end{array}$

$\begin{array}{lcrrrrr}
\hline&& \text { Standard } & & && \text { Upper } \
&\text { Coefficients } & \text { Error } & \text { t Stat } & \text {p-value } & \text { Lower } 95 \%&  95 \%\
\hline\text { Intercept } & 12.237 & 2.789633876 & 4.3866 & 5.6 \mathrm{E}-05 & 6.639227133 & 17.8348469 \
\mathrm{t} & 0.26289 & 0.090738795 & 2.8973 & 0.0055 & 0.080812368 & 0.4449738 \\hline
\end{array}$ Interpret the 95% confidence interval for time t.

Accepted Answer

95% confidence interval =(0.0808, 0.445)

Question 15

In determining monthly seasonal indexes, the first step is to construct a centred moving average with a period of:

Accepted Answer

A)  24 months. 
B)  12 months. 
C)  6 months. 
D)  3 months. 
A)  24 months. 
B)  12 months. 
C)  6 months. 
D)  3 months.

Question 16

Which of the following methods may be used to smooth a time series sufficiently to remove the random variation and to discover the existence of the other time-series components?

Accepted Answer

A)  Moving averages and exponential smoothing. 
B)  Exponential smoothing and deseasonalising. 
C)  Deseasonalising and mean absolute deviation. 
D)  Mean absolute deviation and the percentage of trend. 
A)  Moving averages and exponential smoothing. 
B)  Exponential smoothing and deseasonalising. 
C)  Deseasonalising and mean absolute deviation. 
D)  Mean absolute deviation and the percentage of trend.

Question 17

Which method would you recommend in selecting the appropriate forecasting model if avoiding large errors is extremely important?

Accepted Answer

A)  Mean absolute deviation (MA
D) . 
B)  Either Mean absolute deviation (MA
D)  or Sum of squares for forecast error (SS
E) . 
C)  Sum of squares for forecast error (SS
E) . 
D)  Neither Mean absolute deviation (MA
D)  or Sum of squares for forecast error (SS
E)  
A)  Mean absolute deviation (MA
D) . 
B)  Either Mean absolute deviation (MA
D)  or Sum of squares for forecast error (SS
E) . 
C)  Sum of squares for forecast error (SS
E) . 
D)  Neither Mean absolute deviation (MA
D)  or Sum of squares for forecast error (SS
E)

Question 18

A time series regression equation for a surfboard manufacturing company in Australia is given below: Y = 35 + 4Q1 + 0.5Q3 + 8Q4 + 3t
With t in quarters, the origin is December 2010 and Q1 is the indicator variable for March, Q3 is the indicator variable for September and Q4 is the indicator variable for December.
Which of the following is the correct value of the estimate for the number of surfboards sold by this manufacturing company in March 2015?

Accepted Answer

A)  86 
B)  90 
C)  93 
D)  51 
A)  86 
B)  90 
C)  93 
D)  51

Question 19

To measure the seasonal variation, we compute seasonal indexes, which gauge the degree to which the seasons differ from one another.

Accepted Answer

A) True 
 B)False

Question 20

A time series regression equation for a surfboard manufacturing company in Australia is given below: Y = 35 + 4Q1 + 0.5Q3 + 8Q4 + 3t
With t in quarters, the origin is December 2010 and Q1 is the indicator variable for March, Q3 is the indicator variable for September and Q4 is the indicator variable for December.
Which of the following is the correct value of the estimate for the number of surfboards sold by this manufacturing company in June 2013?

Accepted Answer

A)  68 
B)  77.5 
C)  65 
D)  None of these choices are correct 
A)  68 
B)  77.5 
C)  65 
D)  None of these choices are correct

The time-series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called:

Which of the following represents the fluctuations up to a twelve month time period?

A company selling swimming goggles wants to analyze the company's Australian sales figure. Describe the 4 components of the line graph of Swimming goggle quarterly sales (in $000's) given below.

If autumn 2013 sales were $20 500 and the summer seasonal index was 1.17, then the deseasonalised 2010 autumn sales value would be:

The regression trend line for annual energy consumption for 1985-2005 is given by ŷ_t = 70 + 0.53t, where t = 1 for 1985. If the annual energy consumption for 2000 was 82.5, then the percentage of trend for 2000 was:

The trend line ?_t = 14.13 - 0.54t was calculated from quarterly data for 2006-2010, where t = 1 for the first quarter of 2006. The trend value for the fourth quarter of the year 2011 is 1.170.

The smoothing component is one of the four components of a time series.

In an exponentially smoothed time series, the smoothing constant w is chosen on the basis of how much smoothing is required. In general:

We compute the five-period moving averages for all time periods except the first two and the last two.

If summer 2010 sales were $26 800 and the summer seasonal index was 1.15, the deseasonalised 2010 summer sales value is $30 820.

We calculate the three-period moving averages for a time series for all time periods except the:

In determining monthly seasonal indexes, the first step is to construct a centred moving average with a period of:

Which of the following methods may be used to smooth a time series sufficiently to remove the random variation and to discover the existence of the other time-series components?

Which method would you recommend in selecting the appropriate forecasting model if avoiding large errors is extremely important?

To measure the seasonal variation, we compute seasonal indexes, which gauge the degree to which the seasons differ from one another.

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Methods Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Introduction

Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Inference About Population Variances

Analysis of Variance

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Techniques

Statistical Inference: Conclusion

Index Numbers

Decision Analysis

Filters

Exam 23: Time-Series Analysis and Forecasting

The time-series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called:

Which of the following represents the fluctuations up to a twelve month time period?

A company selling swimming goggles wants to analyze the company's Australian sales figure. Describe the 4 components of the line graph of Swimming goggle quarterly sales (in $000's) given below.

If autumn 2013 sales were $20 500 and the summer seasonal index was 1.17, then the deseasonalised 2010 autumn sales value would be:

The regression trend line for annual energy consumption for 1985-2005 is given by ŷt = 70 + 0.53t, where t = 1 for 1985. If the annual energy consumption for 2000 was 82.5, then the percentage of trend for 2000 was:

The trend line ?t = 14.13 - 0.54t was calculated from quarterly data for 2006-2010, where t = 1 for the first quarter of 2006. The trend value for the fourth quarter of the year 2011 is 1.170.

The smoothing component is one of the four components of a time series.

In an exponentially smoothed time series, the smoothing constant w is chosen on the basis of how much smoothing is required. In general:

We compute the five-period moving averages for all time periods except the first two and the last two.

If summer 2010 sales were $26 800 and the summer seasonal index was 1.15, the deseasonalised 2010 summer sales value is $30 820.

We calculate the three-period moving averages for a time series for all time periods except the:

In determining monthly seasonal indexes, the first step is to construct a centred moving average with a period of:

Which of the following methods may be used to smooth a time series sufficiently to remove the random variation and to discover the existence of the other time-series components?

Which method would you recommend in selecting the appropriate forecasting model if avoiding large errors is extremely important?

To measure the seasonal variation, we compute seasonal indexes, which gauge the degree to which the seasons differ from one another.

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Methods Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference: Introduction

Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Inference About Population Variances

Analysis of Variance

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Techniques

Statistical Inference: Conclusion

Index Numbers

Decision Analysis

Filters

The regression trend line for annual energy consumption for 1985-2005 is given by ŷ_t = 70 + 0.53t, where t = 1 for 1985. If the annual energy consumption for 2000 was 82.5, then the percentage of trend for 2000 was:

The trend line ?_t = 14.13 - 0.54t was calculated from quarterly data for 2006-2010, where t = 1 for the first quarter of 2006. The trend value for the fourth quarter of the year 2011 is 1.170.