OLS Regression Results
Dep. Variable:	Sentence	R-squared:	0.114
Model:	OLS	Adj. R-squared:	0.083
Method:	Least Squares	F-statistic:	3.679
Date:	Mon, 31 May 2021	Prob (F-statistic):	0.000466
Time:	10:11:24	Log-Likelihood:	-1162.1
No. Observations:	238	AIC:	2342.
Df Residuals:	229	BIC:	2373.
Df Model:	8
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
Intercept	38.0078	2.344	16.216	0.000	33.390	42.626
Avoidant	-5.9448	13.004	-0.457	0.648	-31.568	19.678
Dependent	-11.8896	26.008	-0.457	0.648	-63.136	39.357
Obsessive_Compulsive	33.9834	21.504	1.580	0.115	-8.387	76.354
Paranoid	-76.2419	18.939	-4.026	0.000	-113.559	-38.925
Schizotypal	-11.8896	26.008	-0.457	0.648	-63.136	39.357
Schizoid	18.8259	33.165	0.568	0.571	-46.523	84.174
Histrionic	-30.9323	37.981	-0.814	0.416	-105.769	43.904
Narcissistic	7.9555	26.963	0.295	0.768	-45.171	61.082
Borderline	58.1409	27.453	2.118	0.035	4.049	112.233
Antisocial	14.4292	11.640	1.240	0.216	-8.505	37.364

Omnibus:	69.475	Durbin-Watson:	2.110
Prob(Omnibus):	0.000	Jarque-Bera (JB):	141.778
Skew:	1.446	Prob(JB):	1.63e-31
Kurtosis:	5.435	Cond. No.	5.40e+18

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 5.46e-35. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.