A time series regression equation for a surfboard manufacturing company in Australia is given below: yt = 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?

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

a. The seasonally adjusted US quarterly Industrial Production Index from the first quarter of 2001 to the fourth quarter of 2005 (yt, 2002 = 100) is shown in the table below. Would the linear or quadratic model fit better? \[\begin{array} { | r r | } \hline \text { Time period } & { y _ { t } } \\ \hline \text { Mar-01 } & 136.7 \\ \text { Jun-01 } & 124.1 \\ \text { Sep-01 } & 120.5 \\ \text { Dec-01 } & 117.4 \\ \text { Mar-02 } & 101.1 \\ \text { Jun-02 } & 102.5 \\ \text { Sep-02 } & 98.5 \\ \text { Dec-02 } & 97.9 \\ \text { Mar-03 } & 94.0 \\ \text { Jun-03 } & 86.7 \\ \text { Sep-03 } & 89.8 \\ \text { Dec-03 } & 92.3 \\ \text { Mar-04 } & 95.9 \\ \text { Jun-04 } & 89.6 \\ \text { Sep-04 } & 86.3 \\ \text { Dec-04 } & 84.5 \\ \text { Mar-05 } & 88.7 \\ \text { Jun-05 } & 109.9 \\ \text { Sep-05 } & 100.9 \\ \text { Dec-05 } & 108.4 \\ \hline \end{array}\] b. Use Excel and the regression technique to calculate the linear trend line and the quadratic trend line. Which model fits better?

a. @#IMG-DLM& The quadratic model appear

Exam 17: Time-Series Analysis and Forecasting

A time series regression equation for a surfboard manufacturing company in Australia is given below: yt = 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?

(Multiple Choice)

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Question 141

The result of a quadratic model fit to time-series data was $\ddot{\mathbf{p}}_{t}= 8.5 - 0.25 t + 2.5 t ^ { 2 }$ , where t = 1 for 1994. The forecast value for 2001 is 129.25.

(True/False)

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Question 142

We calculate the three-period moving average for a time series for all time periods except the first.

(True/False)

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Question 143

If we wanted to measure the seasonal variations on stock market performance by month, we would need:

(Multiple Choice)

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Question 144

Quarterly sales revenue (in $millions) for a particular company has been modelled using linear regression with indicator variables: Y = 132 + 2Q1 + 3Q2 - 5Q4 + 2t where t is time in quarters, with origin March 2012 and Q1, Q2 and Q4 are the indicator variables for March, June and December quarters, respectively. (a) What is the estimate for December quarter of 2017? (b) What is the estimate for March 2017? (c) Separate the differences from your two estimates in parts (a) and (b) into the trend component and the seasonal component.

(Essay)

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Question 145

a. The seasonally adjusted US quarterly Industrial Production Index from the first quarter of 2001 to the fourth quarter of 2005 (yt, 2002 = 100) is shown in the table below. Would the linear or quadratic model fit better? Time period Mar-01 136.7 Jun-01 124.1 Sep-01 120.5 Dec-01 117.4 Mar-02 101.1 Jun-02 102.5 Sep-02 98.5 Dec-02 97.9 Mar-03 94.0 Jun-03 86.7 Sep-03 89.8 Dec-03 92.3 Mar-04 95.9 Jun-04 89.6 Sep-04 86.3 Dec-04 84.5 Mar-05 88.7 Jun-05 109.9 Sep-05 100.9 Dec-05 108.4 b. Use Excel and the regression technique to calculate the linear trend line and the quadratic trend line. Which model fits better?

(Essay)

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Question 146

In determining monthly seasonal indexes for petrol consumption, the sum of the 12 means for petrol consumption as a percentage of the moving average is 1230. To get the seasonal indexes, each of the 12 monthly means is to be multiplied by:

(Multiple Choice)

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Question 147

The result of a quadratic model fit to time-series data was $\ddot{\mathbf{p}}_{t}= 8.5 - 0.25 t + 2.5 t ^ { 2 }$ , where t = 1 for 1994. The forecast value for 2001 is 129.25.

We calculate the three-period moving average for a time series for all time periods except the first.

If we wanted to measure the seasonal variations on stock market performance by month, we would need:

In determining monthly seasonal indexes for petrol consumption, the sum of the 12 means for petrol consumption as a percentage of the moving average is 1230. To get the seasonal indexes, each of the 12 monthly means is to be multiplied by:

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Techniques Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference and Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Index Numbers

Filters

Exam 17: Time-Series Analysis and Forecasting

The result of a quadratic model fit to time-series data was p¨t=8.5−0.25t+2.5t2\ddot{\mathbf{p}}_{t}= 8.5 - 0.25 t + 2.5 t ^ { 2 }p¨​t​=8.5−0.25t+2.5t2 , where t = 1 for 1994. The forecast value for 2001 is 129.25.

We calculate the three-period moving average for a time series for all time periods except the first.

If we wanted to measure the seasonal variations on stock market performance by month, we would need:

In determining monthly seasonal indexes for petrol consumption, the sum of the 12 means for petrol consumption as a percentage of the moving average is 1230. To get the seasonal indexes, each of the 12 monthly means is to be multiplied by:

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Techniques Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference and Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Describing a Single Population

Hypothesis Testing: Comparing Two Populations

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Index Numbers

Filters

The result of a quadratic model fit to time-series data was $\ddot{\mathbf{p}}_{t}= 8.5 - 0.25 t + 2.5 t ^ { 2 }$ , where t = 1 for 1994. The forecast value for 2001 is 129.25.