Introduction to Regression Modeling

Over the past few days,  I delved into the realm of advanced statistical analyses, with a primary focus on regression modeling. This sophisticated technique empowered us to systematically quantify relationships between crucial variables, injecting a quantitative dimension into our previously qualitative observations. This analytical step marked a pivotal moment as we sought to unravel the intricate web of connections within our data.

OLS Regression Results                            
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Dep. Variable:      med_housing_price   R-squared:                       0.691
Model:                            OLS   Adj. R-squared:                  0.683
Method:                 Least Squares   F-statistic:                     90.56
Date:                Thu, 30 Nov 2023   Prob (F-statistic):           2.21e-21
Time:                        21:09:15   Log-Likelihood:                -1103.1
No. Observations:                  84   AIC:                             2212.
Df Residuals:                      81   BIC:                             2219.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
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                 coef    std err          t      P>|t|      [0.025      0.975]
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const       1.931e+05   5.58e+05      0.346      0.730   -9.18e+05     1.3e+06
unemp_rate   1.23e+07   2.19e+06      5.625      0.000    7.95e+06    1.66e+07
total_jobs    -1.4689      1.347     -1.091      0.279      -4.149       1.211
==============================================================================
Omnibus:                        3.255   Durbin-Watson:                   0.296
Prob(Omnibus):                  0.196   Jarque-Bera (JB):                2.849
Skew:                           0.354   Prob(JB):                        0.241
Kurtosis:                       2.441   Cond. No.                     5.90e+07
==============================================================================

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