Alcohol Consumption and Panic Disorders, Agoraphobia

  Introduction In gapminder data, the association between electric consumption and Co2 Emission was trying to find out. Getting and Preparing Data Data Analysis The p-value is lower than 0.05 so we reject the null hypthothesis that there is definetly an association  between electric consumption and Co2 Emission. R-squared value is %3, which is pretty low . The low value indicates that we might find other explanatory variables to make our model stronger. Basic Linear Regression Model The regression function with their coefficients are " Co2 Emission = 3.482e+6 relectrciperperson + 1.58e+06" Codes import numpy import pandas as pandas import statsmodels.api import statsmodels.formula.api as smf import matplotlib.pyplot as plt from sklearn.preprocessing import Normalizer import seaborn # bug fix for display formats to avoid run time errors pandas.set_option('display.float_format', lambda x:'%.2f'%x) #call in data set data = pandas.read_csv('data/gapminder.cs...

Sample The sample is from the first wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) which surveyed in 2001 - 2002.   The data analytic sample for this study included participants 20-50 years old who were drinkers at that moment. There are 6147 data in this category. The sample is used for analysis of  to find panic attack correlation with alcohol consumption.  Original Purpose and Location of the Data Collection It is a survey designed to determine the magnitude of alcohol use and psychiatric disorders in the U.S. population. It is a representative sample of the non-institutionalized population 18 years and older.  Procedure The sample included many data concerning panic attack. Because panic attach is difficult to diagnose, panic attack related data would be helpful to find out the right correlations against alchohol consumption.   Measures Explanatory Variables Age Category  All these data will combine and fi...

  Introduction Nesarc dataset is used for alcohol consumption, related age category, and depression data. Getting Data Consumer:  Alcoholic Consumption Age :  Ages S2AQ4B :  How often drank coolers in the last month Majordeplife:  The people who experience depression Preparing Data In Nesarc dataset, four columns are needed for analysis.  Only current alholoc consumers included in data and a new column AGECATEGORY is created for age category 20 - 30, 30 - 40, 40 - 50. Data Analysis The age category value counts are liste above.  The pearson corelation is -0.007 which is almost 0 indicating that there is no correlation between alcoholic consumption and depression.  The age categories were divided into 3 different subgroups to check the correlations. Almost all subgroups have close to 0 indicating that there is no correlation between alcohol consumption and depression.

 CO2 Emissions Corelations Introduction I am trying to find out correlation between cumulative CO2 emission (metric tons) and oil Consumption per capita and also cumulative CO2 emission (metric tons) and residential electricity consumption, per person (kWh). Getting Data I have used Gapminder dataset to get the data. Correlations Diagrams Correlation Result In Pearson calculation, there is a slight association between cumulative CO2 emission (metric tons) and residential electricity consumption. That is 0,138. But there is no association  Oil Person which the result is close to 0 which is 0,04.