The following is an accessible version of the Python script output for OLS Regression. In the normal output, the items are arranged in two columns. Here, items are listed in one column for easier use. Each item has a label. For example, the label "Model" comes before the item "OLS". Items follow the same order as the normal Python script output. OLS Regression Results Dependent Variable: Quality Model: OLS Method: Least Squares Date: Fri, 16 Aug 2019 Time: 12:49:37 No. Observations: 18 Df Residuals: 15 Df Model: 2 Covariance Type: nonrubust R-squared: 0.978 Adj. R-squared: 0.975 F-statistic: 332.2 Prob (F-statistic): 3.80 e -13 Log-Likelihood: -21.142 AIC: 48.28 BIC: 50.95 Coef Std err T P>|t| [0.025 0.975] Intercept 0.5385 0.473 1.137 0.273 -0.471 1.547 Speed -1.9046 0.176 -10.834 0.000 -2.279 -1.530 Angle 4.0280 0.178 22.574 0.000 3.648 4.408 Omnibus: 4.358 Prob(Omnibus): 0.113 Skew: 0.082 Kurtosis: 1.637 Durbin-Watson: 2.121 Jarque-Bera (JB): 1.414 Prob(JB): 0.493 Cond. No: 14.4