I have 6 independent x variables and have used OLS to get a polynomial model to describe their relationship with my dependent y variable.
Here is what I get from statsmodels:
==============================================================================
Dep. Variable: p R-squared: 0.209
Model: OLS Adj. R-squared: 0.138
Method: Least Squares F-statistic: 2.949
Date: Sat, 09 Oct 2021 Prob (F-statistic): 0.0129
Time: 15:06:33 Log-Likelihood: -634.99
No. Observations: 74 AIC: 1284.
Df Residuals: 67 BIC: 1300.
Df Model: 6
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
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const 1.177e+04 1107.772 10.629 0.000 9563.438 1.4e+04
Plasma Temperature (K) 0.0025 0.003 0.843 0.402 -0.003 0.008
Kp*10 16.1861 27.491 0.589 0.558 -38.687 71.059
Dst -14.2056 14.523 -0.978 0.332 -43.195 14.783
Proton Density 83.2930 69.770 1.194 0.237 -55.969 222.555
f10.7 -15.9405 8.069 -1.975 0.052 -32.047 0.166
Ey -197.5733 292.431 -0.676 0.502 -781.268 386.121
==============================================================================
Omnibus: 6.537 Durbin-Watson: 2.089
Prob(Omnibus): 0.038 Jarque-Bera (JB): 6.412
Skew: 0.485 Prob(JB): 0.0405
Kurtosis: 4.067 Cond. No. 7.71e+05
==============================================================================
As someone with no experience with statistics, I looked up what all of these different parameters meant but am currently trying to determine how to get the most accurate polynomial using only some of the variables. How would I approach this? Is it simply running a case of running all of the combinations until I get the highest Adj. R-squared or lowest P>t?
Thanks!