I have a dataset something like this:
A B C D E coef to A
1900 0.23 0.98 0.55 1 B 51
1530 0.47 0.76 0.46 1 C 62
2300 0.26 0.86 0.13 0 D 53
1430 0.24 0.66 0.76 0 E 55
2440 0.05 0.88 0.45 1
And when I form my model (with StatsModels Python) to predict A
I get E with a 100+ number for a coefficient, others around 51-56. E is a bivariable (0, 1) but not sure how I should transform it if I should at all. How should I handle this? Figure With E
OLS Regression Results
=======================================================================================
Dep. Variable: tuition R-squared (uncentered): 0.927
Model: OLS Adj. R-squared (uncentered): 0.927
Method: Least Squares F-statistic: 3659.
Date: Tue, 10 Dec 2019 Prob (F-statistic): 0.00
Time: 14:30:59 Log-Likelihood: -10794.
No. Observations: 1155 AIC: 2.160e+04
Df Residuals: 1151 BIC: 2.162e+04
Df Model: 4
Covariance Type: nonrobust
==================================================================================
coef std err t P>|t| [0.025 0.975]
----------------------------------------------------------------------------------
pcttop25 51.4044 4.607 11.157 0.000 42.365 60.444
graduat 57.3034 4.684 12.235 0.000 48.114 66.493
alumni 56.7086 8.162 6.948 0.000 40.695 72.723
public/private 3120.7245 192.468 16.214 0.000 2743.097 3498.352
==============================================================================
Omnibus: 16.130 Durbin-Watson: 1.461
Prob(Omnibus): 0.000 Jarque-Bera (JB): 23.647
Skew: 0.127 Prob(JB): 7.33e-06
Kurtosis: 3.653 Cond. No. 204.
==============================================================================
Without E
OLS Regression Results
=======================================================================================
Dep. Variable: tuition R-squared (uncentered): 0.910
Model: OLS Adj. R-squared (uncentered): 0.910
Method: Least Squares F-statistic: 3903.
Date: Tue, 10 Dec 2019 Prob (F-statistic): 0.00
Time: 14:23:06 Log-Likelihood: -10913.
No. Observations: 1155 AIC: 2.183e+04
Df Residuals: 1152 BIC: 2.185e+04
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
pcttop25 43.3553 5.075 8.544 0.000 33.399 53.312
graduat 85.6925 4.813 17.806 0.000 76.250 95.135
alumni 90.4125 8.744 10.340 0.000 73.256 107.569
==============================================================================
Omnibus: 16.562 Durbin-Watson: 1.479
Prob(Omnibus): 0.000 Jarque-Bera (JB): 17.476
Skew: 0.257 Prob(JB): 0.000160
Kurtosis: 3.314 Cond. No. 8.65
==============================================================================