
Concept explainers
(a)
To find: The predicted count values.
(a)

Answer to Problem 73E
Solution: The predicted count values are:
Explanation of Solution
Calculation: The provided least-squares regression equation is:
The time values are provided in the data as 1, 3, 5 and 7.
Substituting the values of time in the above linear regression equation, the following results are obtained:
For
For
For
For
Hence, the predicted count values obtained are
(b)
Section 1
To find: The difference between the observed and the predicted counts.
(b)
Section 1

Answer to Problem 73E
Solution: The differences obtained are
Explanation of Solution
Calculation: The respective observed counts are provided in the exercise as:
for the times
The respective predicted counts for the above times are obtained from part (a) as:
The differences
For
For
For
For
Hence, the differences obtained are
Section 2
The number of positive differences between the observed and the predicted counts.
Section 2

Answer to Problem 73E
Solution: The two positive differences are
Explanation of Solution
Clearly, the positive difference values are
The number of negative differences between the observed and the predicted counts.

Answer to Problem 73E
Solution: The two negative differences are
Explanation of Solution
Clearly, the negative difference values are
(c)
Section 1
To find: Squares of the differences obtained in part(b).
(c)
Section 1

Answer to Problem 73E
Solution: The squares of the differences are:
Explanation of Solution
Calculation: The differences obtained in the part (b) above are:
The squares of the differences obtained are calculated as follows:
The square of the difference
The square of the difference
The square of the difference
The square of the difference
Hence, the squares of the differences obtained are
Section 2
To find: The sum of the squares of the differences obtained in Section 1 above.
Section 2

Answer to Problem 73E
Solution: The sum of the differences obtained is
Explanation of Solution
Calculation: The squares of the differences are obtained in Section 1 above as:
The sum of the differences
Hence, the sum of the differences obtained is
(d)
Section 1
To find: The predicted count values.
(d)
Section 1

Answer to Problem 73E
Solution: The predicted count values obtained are:
Explanation of Solution
Calculation: The provided least-squares regression equation is:
The time values are provided in the data as 1, 3, 5 and 7.
Substituting the values of time in the above linear regression equation, the following results are obtained:
For
For
For
For
Hence, the predicted count values obtained are:
For the times 1, 3, 5 and 7 respectively.
Section 2:
To find: The difference between the observed and the predicted counts.
Section 2:

Answer to Problem 73E
Solution: The differences obtained are
Explanation of Solution
The differences
For
For
For
For
Hence, the differences obtained are
Section 3:
The number of positive differences between the observed and the predicted counts.
Section 3:

Answer to Problem 73E
Solution: The four positive differences are
Explanation of Solution
Clearly, all the difference values are positive. Hence, all the 4 differences
The number of negative differences between the observed and the predicted counts.

Answer to Problem 73E
Solution: There are no negative differences.
Explanation of Solution
Clearly, none of the values are negative. Hence, 0 out of 4 differences are negative.
Section 4:
To find: Squares of the differences obtained in section 2 of part (d).
Section 4:

Answer to Problem 73E
Solution: The squares of the differences are:
Explanation of Solution
Calculation: The differences obtained in the section 2 of part (d) above are:
The squares of the differences obtained are calculated as follows:
The square of the difference
The square of the difference
The square of the difference
The square of the difference
Hence, the squares of the differences obtained are
Section 5:
To find: The sum of the squares of the differences obtained in Section 3 above.
Section 5:

Answer to Problem 73E
Solution: The sum of the differences is
Explanation of Solution
The sum of the differences
Hence, the sum of the differences is
(e)
To explain: The least-squares inference based on the calculations performed.
(e)

Answer to Problem 73E
Solution: The following least-square regression is a better measure of the relationship between the count and the time:
Explanation of Solution
The differences (residuals) are both positive and negative values, whereas, for the regression line,
all the differences (residuals) are high and positive values. Thus, the residual plots for the first regression line will lie both below and above the x-axis. Also, its predicted regression line will lie much near to the observed regression line. Whereas, the residual plots for the second regression line will lie only above the x-axis. Also, with such high and positive differences, the predicted regression line will lie far to the observed regression line. Hence, the first line better depicts the relationship between the count and the time because it gives a better approximate value of the response variable.
Want to see more full solutions like this?
Chapter 2 Solutions
Introduction to the Practice of Statistics
- John and Mike were offered mints. What is the probability that at least John or Mike would respond favorably? (Hint: Use the classical definition.) Question content area bottom Part 1 A.1/2 B.3/4 C.1/8 D.3/8arrow_forwardThe details of the clock sales at a supermarket for the past 6 weeks are shown in the table below. The time series appears to be relatively stable, without trend, seasonal, or cyclical effects. The simple moving average value of k is set at 2. What is the simple moving average root mean square error? Round to two decimal places. Week Units sold 1 88 2 44 3 54 4 65 5 72 6 85 Question content area bottom Part 1 A. 207.13 B. 20.12 C. 14.39 D. 0.21arrow_forwardThe details of the clock sales at a supermarket for the past 6 weeks are shown in the table below. The time series appears to be relatively stable, without trend, seasonal, or cyclical effects. The simple moving average value of k is set at 2. If the smoothing constant is assumed to be 0.7, and setting F1 and F2=A1, what is the exponential smoothing sales forecast for week 7? Round to the nearest whole number. Week Units sold 1 88 2 44 3 54 4 65 5 72 6 85 Question content area bottom Part 1 A. 80 clocks B. 60 clocks C. 70 clocks D. 50 clocksarrow_forward
- The details of the clock sales at a supermarket for the past 6 weeks are shown in the table below. The time series appears to be relatively stable, without trend, seasonal, or cyclical effects. The simple moving average value of k is set at 2. Calculate the value of the simple moving average mean absolute percentage error. Round to two decimal places. Week Units sold 1 88 2 44 3 54 4 65 5 72 6 85 Part 1 A. 14.39 B. 25.56 C. 23.45 D. 20.90arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forward
- The accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Develop line charts for each variable and identify the characteristics of the time series (that is, random, stationary, trend, seasonal, or cyclical). What is the line chart for the variable Fossil Fuels Production?arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forwardFor each of the time series, construct a line chart of the data and identify the characteristics of the time series (that is, random, stationary, trend, seasonal, or cyclical). Month PercentApr 1972 4.97May 1972 5.00Jun 1972 5.04Jul 1972 5.25Aug 1972 5.27Sep 1972 5.50Oct 1972 5.73Nov 1972 5.75Dec 1972 5.79Jan 1973 6.00Feb 1973 6.02Mar 1973 6.30Apr 1973 6.61May 1973 7.01Jun 1973 7.49Jul 1973 8.30Aug 1973 9.23Sep 1973 9.86Oct 1973 9.94Nov 1973 9.75Dec 1973 9.75Jan 1974 9.73Feb 1974 9.21Mar 1974 8.85Apr 1974 10.02May 1974 11.25Jun 1974 11.54Jul 1974 11.97Aug 1974 12.00Sep 1974 12.00Oct 1974 11.68Nov 1974 10.83Dec 1974 10.50Jan 1975 10.05Feb 1975 8.96Mar 1975 7.93Apr 1975 7.50May 1975 7.40Jun 1975 7.07Jul 1975 7.15Aug 1975 7.66Sep 1975 7.88Oct 1975 7.96Nov 1975 7.53Dec 1975 7.26Jan 1976 7.00Feb 1976 6.75Mar 1976 6.75Apr 1976 6.75May 1976…arrow_forward
- Hi, I need to make sure I have drafted a thorough analysis, so please answer the following questions. Based on the data in the attached image, develop a regression model to forecast the average sales of football magazines for each of the seven home games in the upcoming season (Year 10). That is, you should construct a single regression model and use it to estimate the average demand for the seven home games in Year 10. In addition to the variables provided, you may create new variables based on these variables or based on observations of your analysis. Be sure to provide a thorough analysis of your final model (residual diagnostics) and provide assessments of its accuracy. What insights are available based on your regression model?arrow_forwardI want to make sure that I included all possible variables and observations. There is a considerable amount of data in the images below, but not all of it may be useful for your purposes. Are there variables contained in the file that you would exclude from a forecast model to determine football magazine sales in Year 10? If so, why? Are there particular observations of football magazine sales from previous years that you would exclude from your forecasting model? If so, why?arrow_forwardStat questionsarrow_forward
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman





