This is what I have currently, but it is not working. Code import numpy as np import pandas as pd import scipy from scipy.stats import t # Setup Regression Model Class class RegressionModel(object):...

This is what I have currently, but it is not working.
Code

import numpy as np


import pandas as pd


import scipy


from scipy.stats import t




# Setup Regression Model Class


class RegressionModel(object):


def __init__(self, x, y, create_intercept, regression_type = "ols"):


# Convert to DataFrame (https://www.geeksforgeeks.org/python-pandas-dataframe/#Basics)


self.x = x


self.y = y


if create_intercept:


self.x['interept'] = [ 1 for i in self.y ]


self.create_intercept = create_intercept


self.regression_type = regression_type


self.results = None




def ols_regression(self):


beta_value = np.linalg.inv(self.x.T @ self.x) @ ( self.x.T @ self.y )


n, k = np.shape(self.x)


s_value = ((self.y.T @ self.y) - (self.y.T @ self.x @ beta_value))/ (n-k)


cov_value = s_value * np.linalg.inv(self.x.T @ self.x)


var_value = np.diag(cov_value)


stdErr = np.sqrt(var_value)


tstat = beta_value/stdErr


pval = t.sf(tstat, (n-k))




self.results = {}


i = 0


for j in self.x:


sub_results = {}


sub_results['Variable name'] = j


sub_results['coefficient value']=beta_value[ i ]


sub_results['standard error']=stdErr[ i ]


sub_results['t-statistic']=tstat[ i ]


sub_results['p-value']=pval[ i ]


i += 1


self.results.update({ j : sub_results })


return self.results




def summary(self):


return pd.DataFrame( data = self.results ).T




data = pd.read_csv("https://github.com/dustywhite7/Econ8320/raw/master/AssignmentData/assignment8Data.csv")


x = data[['sex','age','educ','white']]


y = data['incwage']


reg = RegressionModel(x, y, create_intercept=True)


output = reg.ols_regression()


reg.summary()

Mar 30, 2022
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