Hello! I'm trying to write a regression equation for the attached OLS regression results. I have this and I'm not 100% sure it's correct.

In [44]: # Run a reduced multiple regression model In [45]: data[ 'intercept' ]=1 model=sm. OLS (data[ 'Bandwidth_GB_Year' ], data[ [ 'Children' , 'Tenure' , 'Fixes' , 'Replacements' , ' intercept' ]]) . fit( ) print_model=model . summary ( ) print (print_model) OLS Regression Results Dep. Variable: Bandwidth_GB_Year R-squared: 0.984 Model : OLS Adj. R-squared: 0.984 Method: Least Squares F-statistic: 1. 537e+05 Date: Tue, 14 Dec 2021 Prob (F-statistic) : 0.00 Time : 00 : 44 : 44 Log-Likelihood: -70407. No. Observations: 10000 AIC: 1. 408e+05 Df Residuals: 9995 BIC: 1. 409e+05 Df Model: 4 Covariance Type: nonrobust coef std err t P> t [0 . 025 0.975] Children 31.1763 1. 288 24 . 211 0 .000 28 .652 33.700 Tenure 81 . 9518 0.105 783 . 845 0.000 81. 747 82 . 157 Fixes 1. 0728 3.129 0.343 0. 732 -5.061 7.206 Replacements -3.6585 3.149 -1. 162 0 . 245 -9. 831 2 . 514 intercept 506.7695 11. 949 42 . 413 0 . 000 483 . 348 530 . 191 Omnibus : 380.733 Durbin-Watson : 1.978 Prob (Omnibus ) : 0.000 Jarque-Bera (JB) : 295.369 Skew : 0.334 Prob (JB) : 7.27e-65 Kurtosis : 2.488 Cond. No. 191. Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified