Hi everyone,
I have some 26 variables (reduced to 13 for this post) that list the ownership of household assets and a variable for household income. I'm using the following codes for a PCA analysis:
global household_assets qn3_19_1-qn3_19_13
pca $household_assets, covariance comp(5)
screeplot, yline(1)
rotate
estat kmo // values are more than 0.5 so using PCA is justified
predict ha1 ha2 ha3 ha4 ha5, score
Now that I have the 5 components which explain about 88% of the variation, I'd like to know how can I use this information in a regression analysis. More specifically, and for the purpose of this post i'd appreciate if someone can guide me into how should I interpret the coefficients of the following model
Y (household income in Uganda shillings) = B0 + B1X1 + B2X2 + B3X3 + B4X4 + B5X5
a) what should be the interpretation of the coefficients?
b) what happens when B1,B2,B3,B4, B5 shows opposite signs?
Thanks
I have some 26 variables (reduced to 13 for this post) that list the ownership of household assets and a variable for household income. I'm using the following codes for a PCA analysis:
global household_assets qn3_19_1-qn3_19_13
pca $household_assets, covariance comp(5)
screeplot, yline(1)
rotate
estat kmo // values are more than 0.5 so using PCA is justified
predict ha1 ha2 ha3 ha4 ha5, score
Now that I have the 5 components which explain about 88% of the variation, I'd like to know how can I use this information in a regression analysis. More specifically, and for the purpose of this post i'd appreciate if someone can guide me into how should I interpret the coefficients of the following model
Y (household income in Uganda shillings) = B0 + B1X1 + B2X2 + B3X3 + B4X4 + B5X5
a) what should be the interpretation of the coefficients?
b) what happens when B1,B2,B3,B4, B5 shows opposite signs?
Thanks
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input byte(qn3_19_1 qn3_19_2 qn3_19_3 qn3_19_4 qn3_19_5 qn3_19_6 qn3_19_7 qn3_19_8 qn3_19_9 qn3_19_10 qn3_19_11 qn3_19_12) int qn3_19_13 float hincome 1 1 0 0 0 0 0 0 0 0 6 4 0 0 1 2 0 0 0 0 0 0 0 2 5 3 0 300000 0 1 0 0 0 0 0 0 0 0 7 3 0 300000 1 1 0 0 0 0 0 0 0 0 4 4 0 100000 0 1 0 0 0 0 0 0 0 0 7 4 0 0 0 2 0 0 0 0 0 0 0 1 4 2 0 30000 1 2 0 0 0 0 0 0 0 0 5 7 0 200000 1 2 0 0 0 0 0 0 0 6 7 5 0 0 1 2 0 0 0 0 0 0 0 2 4 5 0 120000 1 1 0 0 0 0 0 0 0 1 6 3 0 150000 1 2 0 0 0 0 0 0 0 2 7 5 5 100000 0 3 0 0 0 0 0 0 0 0 6 4 0 140000 1 2 0 0 1 0 0 0 0 0 5 7 2 150000 0 2 0 0 0 0 0 0 0 0 5 3 0 50000 1 1 0 0 0 0 0 0 0 2 6 4 0 50000 1 1 0 0 0 0 0 0 0 0 2 2 0 30000 1 1 0 0 0 0 0 0 0 2 6 4 0 100000 1 1 0 0 0 0 0 0 0 0 7 4 2 50000 1 1 0 0 0 0 0 0 0 0 2 2 0 300000 1 1 0 0 0 0 0 0 0 0 3 4 0 10000 0 0 0 0 0 0 0 0 0 1 2 2 0 0 0 1 0 0 0 0 0 0 0 0 4 4 0 0 1 1 0 0 0 0 0 0 0 0 8 3 0 0 0 1 0 0 0 0 0 0 0 0 3 2 0 150000 0 0 0 0 0 0 0 0 0 0 3 1 0 0 0 1 0 0 0 0 0 0 0 1 6 4 0 . 1 1 0 0 0 0 0 0 0 0 5 2 0 . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 1 1 0 0 0 0 0 0 0 0 5 4 0 100000 1 1 0 0 0 0 0 0 0 2 7 2 0 0 1 1 0 0 0 0 0 0 0 2 5 3 0 . 0 1 0 0 0 0 0 0 0 0 2 3 0 0 0 2 0 0 0 0 0 0 0 0 4 1 0 200000 1 1 0 0 0 0 0 0 0 6 4 3 0 0 1 2 0 0 0 0 0 0 0 2 5 4 0 50000 0 1 0 0 0 0 0 0 0 1 4 5 0 100000 1 2 0 0 1 0 0 0 0 0 5 3 0 70000 1 0 0 0 0 0 0 0 0 0 4 6 0 50000 1 1 0 0 0 0 0 0 0 3 8 5 0 0 0 3 0 0 0 0 0 0 0 0 6 2 1 150000 0 2 0 0 0 0 0 0 0 2 7 6 0 . 0 1 0 0 0 0 0 0 0 0 5 4 0 0 0 2 0 0 0 0 0 0 0 1 5 2 0 150000 0 3 0 0 0 0 0 0 0 0 6 2 1 0 1 2 0 0 0 0 0 0 0 3 7 4 0 100000 1 2 0 0 1 0 0 0 0 3 7 3 0 85000 1 2 0 0 1 0 0 0 0 3 6 3 0 0 1 1 0 0 1 0 0 0 0 2 5 2 1 150000 1 2 0 0 0 0 0 0 0 3 6 5 0 200000 0 3 0 0 0 0 0 0 0 2 8 3 0 50000 0 2 0 0 0 0 0 0 0 0 6 10 0 15000 1 1 0 0 0 0 0 0 0 0 6 3 0 300000 1 2 0 1 1 0 0 0 0 4 6 5 4 200000 0 2 0 0 0 0 0 0 0 0 3 2 0 0 1 1 0 1 0 1 0 0 0 5 8 5 0 300000 3 3 0 0 0 0 0 0 0 0 5 3 0 150000 0 7 1 3 2 0 0 0 0 0 10 7 5 200000 1 2 0 0 0 0 0 0 0 0 6 3 1 0 1 1 0 0 0 0 0 0 0 0 5 3 0 0 0 0 0 0 0 0 0 0 0 0 7 6 0 80000 1 0 0 0 0 0 0 0 0 2 5 3 0 250000 1 1 0 0 0 0 0 0 0 0 6 8 0 100000 1 0 0 0 0 0 0 0 0 0 5 4 0 0 1 2 0 0 0 0 0 0 0 3 6 8 0 150000 1 2 0 0 0 0 0 0 0 0 5 3 0 200000 8 1 0 0 0 0 0 0 0 0 4 2 4 0 0 0 0 0 1 0 0 0 0 0 2 2 4 0 2 4 1 0 1 0 0 0 0 4 2 7 2 0 1 1 0 0 0 0 0 0 0 2 7 3 2 300000 0 0 0 0 0 0 0 0 0 0 4 1 2 180000 0 3 0 0 0 0 0 0 0 0 3 4 5 0 0 1 0 0 0 0 0 0 0 0 3 3 0 200000 1 2 0 1 1 0 0 0 0 0 5 4 5 90000 0 1 0 0 0 0 0 0 0 0 4 1 0 200000 1 2 0 0 0 0 0 0 0 0 4 4 2 0 0 0 0 0 0 0 0 0 0 3 3 3 0 80000 0 0 0 0 0 0 0 0 0 1 4 2 0 0 1 1 0 0 0 0 0 0 0 5 6 2 0 0 1 4 0 1 0 0 0 0 0 0 5 3 1 100000 0 1 0 0 0 0 0 0 0 5 6 2 2 100000 0 1 0 0 0 0 0 0 0 0 7 2 0 200000 0 1 0 0 0 0 0 0 0 0 4 2 1 99 0 0 0 0 0 0 0 0 0 0 3 2 0 100000 0 2 0 0 1 0 0 0 0 0 5 2 4 0 0 1 0 0 0 0 0 0 0 2 6 3 0 80000 1 3 0 0 0 0 0 0 0 0 8 6 5 40098 1 1 0 0 0 0 0 0 0 0 5 4 4 60000 0 3 0 0 0 0 0 0 0 0 5 6 5 300000 0 3 0 0 0 0 0 0 0 0 5 3 2 75000 1 2 0 0 0 0 0 0 0 0 5 2 3 60000 0 0 0 0 1 0 0 0 0 0 4 8 4 0 0 1 0 0 0 0 0 0 0 0 6 7 2 0 0 2 0 0 0 0 0 0 0 0 5 1 4 . 1 1 0 0 0 0 0 0 0 0 2 2 2 50000 1 2 0 0 0 0 0 0 0 0 3 2 0 0 0 1 0 0 0 0 0 0 0 0 3 1 0 0 1 0 0 0 0 0 0 0 0 0 2 1 0 100000 1 2 0 0 1 1 0 0 0 0 8 4 0 50000 0 2 0 0 0 0 0 0 0 1 4 1 0 0 end
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