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    Regression and Time Series Analysis

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    The data in the table below represent the annual revenues(in billion of dollars)
    of McDonald's Corporation over the 31-year period from 1975 t0 2005.

    Year Coded Yr Revenues
    1975 0 1
    1976 1 1.2
    1977 2 1.4
    1978 3 1.7
    1979 4 1.9
    1980 5 2.2
    1981 6 2.5
    1982 7 2.8
    1983 8 3.1
    1984 9 3.4
    1985 10 3.8
    1986 11 4.2
    1987 12 4.9
    1988 13 5.6
    1989 14 6.1
    1990 15 6.8
    1991 16 6.7
    1992 17 7.1
    1993 18 7.4
    1994 19 8.3
    1995 20 9.8
    1996 21 10.7
    1997 22 11.4
    1998 23 12.4
    1999 24 13.3
    2000 25 14.2
    2001 26 14.9
    2002 27 15.4
    2003 28 17.1
    2004 29 19
    2005 30 20.5

    a) Calculate a three-year moving average to the data (add a column to the table)

    b) Using a smoothing coefficient of W = 0.75, exponentially smooth the series
    (add a column to the table, use data analysis to smooth)

    c) Plot the results from a) and b) with the time series.

    d) Compute a quadratic trend forecasting equation and plot the predicted result with the data against the coded years.

    e) Compute an exponential trend forcasting equation and plot the predicted results with the data against the coded years.

    f) Compute a second -order autoregressive model, test for the significance of the second-order
    autoregressive parameter.

    g) Compute a first-order autoregressive model, test for the significance of the first-oder
    autoregressive parameter, and plot the predicted results with the data against the coded years.

    Pr2 Simple Linear Regression.

    The owner of a chain of ice cream store would like to study
    the effect of atmosperic temperature on sales during the summer season.
    A sample of 21 consecutive days is selected, with the results stored in the table below

    Temperature, in degrees Sales, in thousand of $
    63 1.52
    70 1.68
    73 1.8
    75 2.05
    80 2.36
    82 2.25
    85 2.68
    88 2.9
    90 3.14
    91 3.06
    92 3.24
    75 1.92
    98 3.4
    100 3.28
    92 3.17
    87 2.83
    84 2.58
    88 2.86
    80 2.26
    82 2.14
    76 1.98

    a) Construct a scatter diagram

    b) Using Data Analysis/Regression, find the regression coefficients b0 and b1.

    c) Graph the regression line on the scatter diagram

    d) Interpret the meaning of b0 and b1 in this problem.

    e) Predict the sales per store for a day in which the temperature is 83F.

    f) determine the coefficient of determination and interpret its meaning.

    g) Graph the residuals in this model and determine
    the adequacy of the model (linearity).

    h) At the 0.05 level of significance, is there evidence
    of a linear relationship between sales and temperature?
    (based on t-test or p-value)

    Pr3. Anova one factor

    In order to test the strength of four brands of trash bags,
    one-pound weights were placed into bags, one at a time
    until the bag broke.
    A total of 40 bags, 10 of each brand, were used. The data in the table below
    gives the weight required to break the trash bag.

    KROGER GLAD HEFTY TUFFSTUFF
    34 32 33 26
    30 42 34 18
    40 34 32 20
    38 36 40 15
    36 32 40 20
    30 40 34 20
    30 36 36 17
    42 43 34 18
    36 30 32 19
    38 38 34 20

    a) Using Data analysis/Anova, test at the level of significance 0.05 for the evidence
    of the differences in the mean strength of the four brands of trash bags .

    b) If appropriate, determine which brands differ in mean strength

    Use Turkey-Kramer procedure.

    In Word:

    1. The names of the coefficients in the regression equation are __________________________.
    2. In the time series the independent variable is __________________________________________
    3. The exponential equation in autoregression model is transformed into____________________ by using ____________function.
    4. The average of odd number of values calculated for each consecutive cell is called ______________________________.
    5. In the autoregressive models the independent variables are________________________________________
    6. The multiple regression equation has one _________________ and several _____________________.
    7. The regression line fits the points on the scatter diagram that means __________________________________________________________________________________________________
    8. A column of the t-table is picked according to the __________________.
    9. The symbol used for order of the autoregressive model is ________.
    10. The goal of the ANOVA method is to test if ____________ among the________________ are significant.
    11. The explained variation is LARGE / SMALL when coefficient of determination is close to one.
    12. Periodic pattern on the time series with time period of 2 to10 years is called____________________.
    13. In the autoregressive model of the third-order the number of intercepts in the equation is__________________________
    14. In the simple regression method the coefficient of determination r^2 is calculated as _________________________________________________.
    15. The multiple regression models can be improved by adding _____________________ term.
    16. The Tukey-Kramer procedure calculates the _________________________ _____ and compares them with ______________________________________in Q tets.
    17. In the ANOVA method the number of factors may be __________________________________.
    18. The zero in the confidence interval for slope indicates that the relationship is STRONG / WEAK.
    19. No pattern on the plot of the residuals means that the regression model is appropriate to use. TRUE /FALSE.
    20. The contribution of a term to a regression model is significant if __________________ of the term is ____________________.

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    https://brainmass.com/statistics/correlation-and-regression-analysis/regression-time-series-analysis-596942

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    Solution Summary

    The detailed solution is provided for testing of interceptor, slope in regression analysis. Lag (r) test statistics value is calculated in order to decide the existence of auto-correlation and also moving averages, exponential smoothing and other time series techniques are used in this solution. Attached in Word and Excel.

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