Overview 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. My response variable, lifeexpectancy, is a quantitative response variable that measures the life expectancy of the people of Ghana. For the purposes of running the Lasso Regression Analysis, I added more variables
OVERVIEW My research work deals with Ghana, a country from the Gapminder dataset. What I found in my logistic regression analysis. Discussion of the results for the associations between all of my explanatory variables and my response variable The primary quantitative explanatory variable in my regression analysis is the Income Per Person
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
Exploring Statistical Analysis in the Context of Correlation Chosen Dataset I will be working with Data from the Gapminder dataset. This happens to be the same dataset I worked with under the Data Management and Visualization course assignments. As elaborated and discussed in under the Data Management and Visualization course assignments, I have chosen to focus on the country,
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