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
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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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