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## | Please refer to http://gking.harvard.edu/zelig for full |
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## | models support by Zelig. |
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## | Zelig project citations: |
## | Kosuke Imai, Gary King, and Olivia Lau. (2009). |
## | ``Zelig: Everyone's Statistical Software,'' |
## | http://gking.harvard.edu/zelig |
## | and |
## | Kosuke Imai, Gary King, and Olivia Lau. (2008). |
## | ``Toward A Common Framework for Statistical Analysis |
## | and Development,'' Journal of Computational and |
## | Graphical Statistics, Vol. 17, No. 4 (December) |
## | pp. 892-913. |
## | |
## | To cite individual Zelig models, please use the citation |
## | format printed with each model run and in the documentation. |
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## intersect, setdiff, setequal, union## Warning: package 'readstata13' was built under R version 3.1.3For the purpose of this assignment I am using the General Social Survey (GSS) data. I have started with the abridged version from last semester. I am going to cut it down further to include only the variables I am interested in studying. I am interested in looking at variables related to gun ownership. I am curious to see if age, political views, education, and/or racial attitudes have any relation to gun ownership.
However, because the variable for gun ownership is categorical, I will be looking at the relationship between other variables at first. For this assignment I will be looking to see if age and education are related to income. It may be important to know the relationship between these three variables when looking at how each of them relates to gun ownership.
## [1] "CASEID" "WORKBLKS" "RACDIF1" "RACMAR" "RACDIF2" "RACDIF3"
## [7] "HELPBLK" "HELPPOOR" "YEAR" "SEX" "AGE" "RACE"
## [13] "REALINC" "REALRINC" "EDUC" "DEGREE" "PRESTG80" "PAPRES80"
## [19] "MARITAL" "DIVORCE" "CHILDS" "RELIG" "WRKSLF" "UNEMP"
## [25] "REGION" "SIZE" "RACLIVE" "FEAR" "GUN" "POLVIEWS"
## [31] "FECHLD" "FEFAM"## [1] "WORKBLKS" "RACDIF1" "RACDIF2" "RACDIF3" "RACMAR" "HELPBLK"
## [7] "HELPPOOR" "YEAR" "SEX" "AGE" "RACE" "REALINC"
## [13] "EDUC" "DEGREE" "RELIG" "UNEMP" "REGION" "RACLIVE"
## [19] "FEAR" "GUN" "POLVIEWS"##
## <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td colspan="2">REALINC</td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">AGE</td><td>-75.426<sup>***</sup></td><td>55.033<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(7.517)</td><td>(7.150)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td></tr>
## <tr><td style="text-align:left">EDUC</td><td></td><td>3,395.803<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(38.543)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>34,779.270<sup>***</sup></td><td>-14,495.930<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(361.585)</td><td>(652.597)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>49,386</td><td>49,311</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.002</td><td>0.138</td></tr>
## <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.002</td><td>0.138</td></tr>
## <tr><td style="text-align:left">Residual Std. Error</td><td>28,480.760 (df = 49384)</td><td>26,473.610 (df = 49308)</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>100.675<sup>***</sup> (df = 1; 49384)</td><td>3,938.637<sup>***</sup> (df = 2; 49308)</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="2" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>##
## ===========================================================================
## Dependent variable:
## -------------------------------------------------------
## REALINC
## (1) (2)
## ---------------------------------------------------------------------------
## AGE -75.426*** 55.033***
## (7.517) (7.150)
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## EDUC 3,395.803***
## (38.543)
##
## Constant 34,779.270*** -14,495.930***
## (361.585) (652.597)
##
## ---------------------------------------------------------------------------
## Observations 49,386 49,311
## R2 0.002 0.138
## Adjusted R2 0.002 0.138
## Residual Std. Error 28,480.760 (df = 49384) 26,473.610 (df = 49308)
## F Statistic 100.675*** (df = 1; 49384) 3,938.637*** (df = 2; 49308)
## ===========================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01These two regressions show a significant relationship between age, education, and income. Regression one shows an inverse relationship in that younger people surveyed had higher income. However, once education is factored into the equation the second regression shows a more typical story. Regression two tells us that education and age have a positive relationship with income; older people with more education have higher income.