Homework Help is Here – Start Your Trial Now!
Math
Statistics
Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. A company that produces and markets video games wants to estimate the predictability of per capita consumer spending on video games in a particular country. For the most recent 7 years, the amount of annual spending per person per year is shown here. Original Time Series Year $ per capita 2 3 37.86 1 5 4 32.29 34.01 43.31 44.63 49.62 51.87 (a) To construct a serial correlation, we use data pairs (x, y) where x = original data and y = original data shifted ahead by one time period. Construct the data set (x, Y) for serial correlation by filling in the following table. 32.29 34.01 37.86 43.31 44.63 49.62 y (b) For the (x, y) data set of part (a), compute the equation of the sample least-squares line ý = a + bx. (Use 4 decimal places.)

Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. A company that produces and markets video games wants to estimate the predictability of per capita consumer spending on video games in a particular country. For the most recent 7 years, the amount of annual spending per person per year is shown here. Original Time Series Year $ per capita 2 3 37.86 1 5 4 32.29 34.01 43.31 44.63 49.62 51.87 (a) To construct a serial correlation, we use data pairs (x, y) where x = original data and y = original data shifted ahead by one time period. Construct the data set (x, Y) for serial correlation by filling in the following table. 32.29 34.01 37.86 43.31 44.63 49.62 y (b) For the (x, y) data set of part (a), compute the equation of the sample least-squares line ý = a + bx. (Use 4 decimal places.)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2CYU
See similar textbooks
icon
Related questions
icon
Concept explainers
Question
Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. A company that produces and markets video games wants to estimate the predictability of per capita consumer spending on video games in a particular country. For the most recent 7 years, the amount of annual spending per person per year is shown here. Original Time Series Year $ per capita 1 3 4 5 6 7 32.29 34.01 37.86 43.31 44.63 49.62 51.87 (a) To construct a serial correlation, we use data pairs (x, y) where x = original data and y = original data shifted ahead by one time period. Construct the data set (x, y) for serial correlation by filling in the following table. 32.29 34.01 37.86 43.31 44.63 49.62 y (b) For the (x, y) data set of part (a), compute the equation of the sample least-squares line ŷ = a + bx. (Use 4 decimal places.) a If the per capita spending was x = $39 one year, what do you predict for the spending the next year? (Use 2 decimal places.) $4 per capita (c) Compute the sample correlation coefficient r and the coefficient of determination 2. (Use 4 decimal places.) Test p > 0 at the 1% level of significance. (Use 2 decimal places.) critical t Conclusion O Reject the null hypothesis, there is sufficient evidence that p > 0. O Reject the null hypothesis, there is insufficient evidence that p > 0. O Fail to reject the null hypothesis, there is insufficient evidence that p > 0. O Fail to reject the null hypothesis, there is sufficient evidence that p > 0. Would you say the time series of per capita spending on video games is relatively predictable from one year to the next? Explain. O Yes, the data support a high negative serial correlation and indicate a predictable original time series from one year to the next. O Yes, the data support a high positive serial correlation and indicate a predictable original time series from one year to the next. No, the data do not support a high serial correlation and do not indicate a predictable original time series from one year to the next.
Transcribed Image Text:Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. A company that produces and markets video games wants to estimate the predictability of per capita consumer spending on video games in a particular country. For the most recent 7 years, the amount of annual spending per person per year is shown here. Original Time Series Year $ per capita 1 3 4 5 6 7 32.29 34.01 37.86 43.31 44.63 49.62 51.87 (a) To construct a serial correlation, we use data pairs (x, y) where x = original data and y = original data shifted ahead by one time period. Construct the data set (x, y) for serial correlation by filling in the following table. 32.29 34.01 37.86 43.31 44.63 49.62 y (b) For the (x, y) data set of part (a), compute the equation of the sample least-squares line ŷ = a + bx. (Use 4 decimal places.) a If the per capita spending was x = $39 one year, what do you predict for the spending the next year? (Use 2 decimal places.) $4 per capita (c) Compute the sample correlation coefficient r and the coefficient of determination 2. (Use 4 decimal places.) Test p > 0 at the 1% level of significance. (Use 2 decimal places.) critical t Conclusion O Reject the null hypothesis, there is sufficient evidence that p > 0. O Reject the null hypothesis, there is insufficient evidence that p > 0. O Fail to reject the null hypothesis, there is insufficient evidence that p > 0. O Fail to reject the null hypothesis, there is sufficient evidence that p > 0. Would you say the time series of per capita spending on video games is relatively predictable from one year to the next? Explain. O Yes, the data support a high negative serial correlation and indicate a predictable original time series from one year to the next. O Yes, the data support a high positive serial correlation and indicate a predictable original time series from one year to the next. No, the data do not support a high serial correlation and do not indicate a predictable original time series from one year to the next.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 5 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Knowledge Booster
Correlation, Regression, and Association
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897…
Glencoe Algebra 1, Student Edition, 9780079039897…
Algebra
ISBN:
9780079039897
Author:
Carter
Publisher:
McGraw Hill
Big Ideas Math A Bridge To Success Algebra 1: Stu…
Big Ideas Math A Bridge To Success Algebra 1: Stu…
Algebra
ISBN:
9781680331141
Author:
HOUGHTON MIFFLIN HARCOURT
Publisher:
Houghton Mifflin Harcourt
SEE MORE TEXTBOOKS