Testing a Basic Linear Regression Model – Data Analysis and Intrepretation
Testing a Basic Linear Regression Model Background My research work deals with Ghana, a country from the Gapminder dataset as has already been discussed from the beginning and progression through this course. 1) Program Code and Output
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# -*- coding: utf-8 -*- """ Created on Sat Feb 13 12:28:07 2016 @author: Bernard """ import pandas import matplotlib.pyplot as plt import statsmodels.formula.api as smf import seaborn data = pandas.read_csv('gapminder_ghana_updated.csv') data["incomeperperson"] = data["incomeperperson"].convert_objects(convert_numeric=True) data['lifeexpectancy'] = data['lifeexpectancy'].convert_objects(convert_numeric=True) # listwise deletion of missing values dataSub = data[['incomeperperson', 'lifeexpectancy']].dropna() scat1 = seaborn.regplot(x="incomeperperson", y="lifeexpectancy", scatter=True, data=dataSub) plt.xlabel('Income Per Person') plt.ylabel('Life Expectancy') plt.title ('Scatterplot for the Association Between Income Per Person and Life Expectancy of the People Of Ghana') print(scat1) # center quantitative Explanatory variable for regression analysis dataSub['incomeperperson_c'] = (dataSub['incomeperperson'] - dataSub['incomeperperson'].mean()) print("Describe the centered quantitative Explanatory variable") ds0 = dataSub["incomeperperson_c"].describe() print(ds0) # printing mean print("Mean for centered quantitative explanatory variable: incomeperperson_c") ds1 = dataSub.groupby('incomeperperson_c').mean() print (ds1) print("Standard deviation for centered quantitative explanatory variable:incomeperperson_c") sd1 = dataSub.groupby('incomeperperson_c').std() print (sd1) print("Mean for quantitative explanatory variable: incomeperperson") ds2 = dataSub.groupby('incomeperperson').mean() print (ds2) print("Checking values in incomeperperson_c") print(dataSub["incomeperperson_c"]) #Value counts print("Counts for incomeperperson_c") inc_c_Count = dataSub["incomeperperson_c"].value_counts(sort = False ,dropna=False) #dropna displays missen values print(inc_c_Count) print ("OLS regression model for the association between Income Per Person and Life Expectancy of the People of Ghana") reg1 = smf.ols('lifeexpectancy ~ incomeperperson_c', data=dataSub).fit() print (reg1.summary()) |
##################### OUTPUT BEGIN ##################### Axes(0.125,0.125;0.775×0.775) Describe the centered…