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    regression results

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    Starting with the data on the price of a related commodity for years 1986 to 2005 listed below, we have estimated the regression for the quantity demanded of a commodity (which we now label Q ̂X), on the price of the commodity (which we now label PX), consumer income (which we now label Y), and the price of the related commodity (PZ), and we obtained the following results.
    Year Pz($) 1986
    14 1987
    15 1988
    15 1989
    16 1990
    17
    Year Pz($) 1991
    18 1992
    17 1993
    18 1994
    19 1995
    20
    Year Pz($) 1996
    20 1997
    19 1998
    21 1999
    21 2000
    22
    Year Pz($) 2001
    23 2002
    23 2003
    24 2004
    25 2005
    25

    Q ̂x = 121.86 - 9.50Px + 0.04Y - 2.21Pz
    (5.12) (2.18) (-0.68)
    R2 = 0.9633 F = 167.33 D - W = 2.38

    Evaluate the above regression results in terms of the signs of the coefficients, the statistical significance of the coefficients and the explanatory power of the regression (R2). The number in parentheses below the estimated slope coefficients refer to the estimated t values. The rule of thumb for testing the significance of the coefficients is if the absolute t value is greater than 2, the coefficient is significant, which means the coefficient is significantly different from zero. For example, the absolute t value for Px is 5.12 which is greater than 2, therefore, the coefficient of Px, (-9.50) is significant. In other words, Px does affect Qx. If the price of the commodity X increases by $1, the quantity demanded (Qx) will decrease by 9.50 units. (c) X and Z are complementary or substitutes?

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    https://brainmass.com/economics/regression/regression-results-evaluating-308016

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    The estimated regression equation is given as:

    = 0.9633 F = 167.33
    Where = quantity demanded of the commodity X
    = Price of the commodity X
    Y = consumer income
    = Price of the commodity Z
    It is given that the regression equation is estimated by using a sample of size n = 20. Hence the given regression results can be interpreted as follows:
    1. The explanatory power of the model can be measured by using the R2 value. R2 = 0.9633 implies that the explanatory power of the model is 96.33%. This means 96.33% variations in the quantity demanded can be explained by the variations in the explanatory variables used in the model.
    2. The F value ...

    Solution Summary

    Evaluate the regression results in terms of the signs of the coefficients, the statistical significance of the coefficients and the explanatory power of the regression (R2).

    $2.19