The sign of r corresponds to the direction of the relationship. The further away r is from zero, the stronger the linear relationship between the two variables. The Pearson correlation coefficient, r, can take on values between -1 and 1. A correlation analysis provides information on the strength and direction of the linear relationship between two variables, while a simple linear regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other. Results class for a dimension reduction regression.A correlation or simple linear regression analysis can determine if two numeric variables are significantly linearly related. Results class for Gaussian process regression models. RecursiveLSResults(model, params, filter_results)Ĭlass to hold results from fitting a recursive least squares model. Results for models estimated using regularization This class summarizes the fit of a linear regression model. Results class of the other linear models. Specific results class with some additional methods compared to the Sliced Average Variance Estimation (SAVE)įitting a linear regression model returns a results class. SlicedAverageVarianceEstimation(endog, exog, .) PrincipalHessianDirections(endog, exog, **kwargs) ProcessMLE(endog, exog, exog_scale, .)įit a Gaussian mean/variance regression model. RollingOLS(endog, exog)Īn implementation of ProcessCovariance using the Gaussian kernel. Yule_walker(x)Įstimate AR(p) parameters from a sequence using the Yule-Walker equations.Ĭompute Burg's AP(p) parameter estimator. Generalized Least Squares with AR covariance structure GLS : generalized least squares for arbitrary covariance \(\Sigma\) \(Y = X\beta + \mu\), where \(\mu\sim N\left(0,\Sigma\right).\)ĭepending on the properties of \(\Sigma\), we have currently four classes available: Standard Errors assume that the covariance matrix of the errors is correctly specified. Variable: GRADE R-squared: 0.416 Model: OLS Adj. summary ()) OLS Regression Results = Dep.
exog, prepend = False ) # Fit and summarize OLS model In : mod = sm. # Load modules and data In : import numpy as np In : import statsmodels.api as sm In : spector_data = sm.
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