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    Regression analysis

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    12.48 In the following regression, X = weekly pay, Y = income tax withheld, and n = 35 McDonald's employees.

    (a) Write the fitted regression equation.
    (b) State the degrees of freedom for a two tailed test for zero slope, and use Appendix D to find the critical value at ? = .05.
    (c) What is your conclusion about the slope?
    (d) Interpret the 95 percent confidence limits for the slope.
    (e) Verify that F = t2 for the slope.

    R2 0.202
    Std. Error 6.816
    n 35

    ANOVA table

    Source SS df MS F p-value
    Regression 387.6959 1 387.6959 8.35 .0068
    Residual 1,533.0614 33 46.4564

    Total 1,920.7573 34

    Regression output confidence interval

    variables coefficients std. Error t (df = 33) p-value 95% lower 95% upper

    Intercept 30.7963 6.4078 4.806 .0000 17.7595 43.8331
    Slope 0.0343 0.0119 2.889 .0068 0.0101 0.0584

    12.50
    In the following regression, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64 large banks.

    (a) Write the fitted regression equation.
    (b) State the degrees of freedom for a two tailed test for zero slope, and use Appendix D to find the critical value at ± = .05.
    (c) What is your conclusion about the slope?
    (d) Interpret the 95 percent confidence limits for the slope.
    (e) Verify that F = t2 for the slope.

    R2 0.519
    Std. Error 6.977
    n 64
    ANOVA table

    Source SS df MS F p-value

    Regression 3,260.0981 1 3,260.0981 66.97 1.90E-11
    Residual 3,018.3339 62 48.6828

    Total 6,278.4320 63

    Regression output confidence interval

    Variables coefficients std. error t (df = 62) p-value 95% lower 95% upper

    Intercept 6.5763 1.9254 3.416 .0011 2.7275 10.4252
    X1 0.0452 0.0055 8.183 1.90E-11 0.0342 0.0563

    13.30
    A researcher used stepwise regression to create regression models to predict BirthRate (births per 1,000) using five predictors: LifeExp (life expectancy in years), InfMort (infant mortality rate), Density (population density per square kilometer), GDPCap (Gross Domestic Product per capita), and Literate (literacy percent). Interpret these results.

    Regression Analysis?Stepwise Selection (best model of each size)

    153 observations
    BirthRate is the dependent variable

    p-values for the coefficients
    Nvar LifeExp InfMort Density GDPCap Literate s Adj R2 R2
    1 .0000 6.318 .722 .724
    2 .0000 .0000 5.334 .802 .805
    3 .0000 .0242 .0000 5.261 .807 .811
    4 .5764 .0000 .0311 .0000 5.273 .806 .812
    5 .5937 .0000 .6289 .0440 .0000 5.287 .805 .812

    13.32
    An expert witness in a case of alleged racial discrimination in a state university school of nursing introduced a regression of the determinants of Salary of each professor for each year during an 8-year period (n = 423) with the following results, with dependent variable Year (year in which the salary was observed) and predictors YearHire (year when the individual was hired), Race (1 if individual is black, 0 otherwise), and Rank (1 if individual is an assistant professor, 0 otherwise).
    Interpret these results.

    Variable Coefficient t p
    Intercept −3,816,521 −29.4 .000
    Year 1,948 29.8 .000
    YearHire −826 −5.5 .000
    Race −2,093 −4.3 .000
    Rank −6,438 −22.3 .000
    R2 = 0.811 R2 adj = 0.809 s = 3,318

    14.16
    (a) Plot the data on U.S. general aviation shipments.
    (b) Describe the pattern and discuss possible causes.
    (c) Would a fitted trend be helpful? Explain.
    (d) Make a similar graph for 1992-2003 only. Would a fitted trend be helpful in making a prediction for 2004?
    (e) Fit a trend model of your choice to the 1992-2003 data.
    (f) Make a forecast for 2004, using either the fitted trend model or a judgment forecast. Why is it best to ignore earlier years in this data set?

    U.S. Manufactured General Aviation Shipments, 1966-2003
    Year Planes Year Planes Year Planes Year Planes
    1966 15,587 1976 15,451 1986 1,495 1996 1,053
    1967 13,484 1977 16,904 1987 1,085 1997 1,482
    1968 13,556 1978 17,811 1988 1,143 1998 2,115
    1969 12,407 1979 17,048 1989 1,535 1999 2,421
    1970 7,277 1980 11,877 1990 1,134 2000 2,714
    1971 7,346 1981 9,457 1991 1,021 2001 2,538
    1972 9,774 1982 4,266 1992 856 2002 2,169
    1973 13,646 1983 2,691 1993 870 2003 2,090
    1974 14,166 1984 2,431 1994 881
    1975 14,056 1985 2,029 1995 1,028
    Source: U.S. Manufactured General Aviation Shipments, Statistical Databook 2003, General Aviation Manufacturers Association, used withpermission.

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    https://brainmass.com/statistics/regression-analysis/342610

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    Solution Summary

    The solution provides step by step method for the calculation of regression model. Formula for the calculation and Interpretations of the results are also included.

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