INCOMEPRPBLCK
Mean 47053.78 0.113486
Median 46272.00 0.041444
Maximum 136529.0 0.981658
Minimum 15919.00 0.000000
Std. Dev. 13179.29 0.182416
Skewness 0.962831 2.700012
Kurtosis 7.551386 10.56841
JarqueBera 416.2135 1473.100
Probability 0.000000 0.000000
Sum 19244998 46.41594
Sum Sq. Dev. 7.09E+10 13.57651
Observations 409 409
The average of prpblck is .113 with standard deviation .182; the average of income is 47,053.78 with standard deviation 13,179.29. It is evident that prpblck is a proportion and that income is measured in dollars.
(ii)
Dependent Variable: PSODA
Method: Least Squares
Sample: 1 410
Included observations: 401
Excluded observations: 9
VariableCoefficientStd. ErrortStatisticProb.
PRPBLCK0.1149880.0260014.4225150.0000
INCOME1.60E063.62E074.4301300.0000
C0.9563200.01899250.353790.0000
Rsquared0.064220 Mean dependent var1.044863
Adjusted Rsquared0.059518 S.D. dependent var0.088798
S.E. of regression0.086115 Akaike info criterion2.058820 Sum squared resid2.951465 Schwarz criterion2.028940
Log likelihood415.7934 Fstatistic13.65691
DurbinWatson stat1.696180 Prob(Fstatistic)0.000002
If, say, prpblck increases by .10 (ten percentage points), the price of soda is estimated to increase by .0115 dollars, or about 1.2 cents, holding income constant. While this does not seem large, there are communities with no black population and others that are almost all black, in which case the difference in psoda is estimated to be almost 11.5 cents. I’d still say it’s pretty weak effect, but it’s not totally negligible, given that the price of soda is pretty low.
(iii)
Dependent Variable: PSODA
Method: Least Squares
Sample: 1 410
Included observations: 401
Excluded observations: 9
VariableCoefficientStd. ErrortStatisticProb.
PRPBLCK...
...
ECONOMETRICS



First of all, I would like to apologize for showing the results in Spanish, but I couldn’t find the way to change Gretl’s language. However, all the explanations are in English, so I hope there is no problem to understand the results.
Secondly, I would just inform you that the timeseries data that I have used is “U.S. macro data, 19502000” from Greene Sample folder in Gretl.
Before building the model…
I would try to explain the...
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This gives the total fit of , between 0 (chance)...
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cuthheq
50520
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dsdclve
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2041
3.9
mlohinc
50836
1622
3.1
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21
.0
a. Number of cases outside the range (Mean  2*SD, Mean + 2*SD).
According to the table shown in the next page, we can infer that Skewness and Kurtosis are higher than one in more than a case. This leads us to conclude that these variables are not normally distributed. However due to the large size of the sample we...
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After completing this chapter, you should be able to:
understand model building using multiple regression analysis
apply multiple regression analysis to business decisionmaking situations
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