Smoking Habits Analysis Based on Demographic Factors-Performing Chi-Square Test on Dataset using Python-—This project focuses on an in-depth analysis of smoking behaviors with a focus on understanding the association between smoking habits and gender. The primary objective is to assess if smoking behavior is influenced by gender and other demographic factors such as age, income, and education level. This study includes a statistical hypothesis test, exploratory data analysis, feature reduction through Principal Component Analysis (PCA), and classification modeling. The findings provide insights into the demographic factors influencing smoking behaviors and highlight significant predictors for potential health interventions.
B. Chi-square Test- Statistical Analysis . Statistical Analysis, • Chi-Square Test for Independence: • Purpose: To determine if there is a significant association between gender and smoking status. • Hypothesis Setup: • Null Hypothesis (H0): Smoking behavior is independent of gender. • Alternative Hypothesis (H1): Smoking behavior is not independent of gender. • Test Execution: • Calculate the Chi-Square statistic by cross-tabulating gen der and smoking status. • Calculate the degrees of freedom and obtain the p-value. • Display results and visualize the Chi-Square distribution if possible. • Conclusion: • Based on the p-value, conclude if gender significantly affects smoking behavior. • Interpretation of whether smoking behavior varies mean ingfully across genders
INTRODUCTION
Background: Smoking is a global public health concern with well-documented risks. Demographic factors can influ ence smoking habits, with previous studies suggesting cor relations between smoking and factors such as gender, age, income, and education.
Objective: This project aims to evaluate the relationship between smoking behavior and demographic characteristics, specifically analyzing the dependence of smoking habits on gender. We will employ statistical testing and data analysis to assess these relationships and build a predictive model to classify smoking behavior.
Significance Insights from this study can help in creating targeted anti-smoking campaigns and healthcare initiatives by identifying demographic groups more prone to smoking.
What Is a Chi-Square Test?
The Chi-Square test is a statistical procedure for determin ing the difference between observed and expected data. This test can also be used to determine whether it correlates to the categorical variables in our data. It helps to find out whether a difference between two categorical variables is due to chance or a relationship between them. In this test:
1]Variance is double the times the number of degrees of freedom.
2]Mean distribution is equal to the number of degrees of freedom.
3]When the degree of freedom increases, the Chi-Square distribution curve becomes normal
II. DATA ANALYTICS EVALUATION A. Dataset Overview Data Source: The dataset is sourced from Kaggle’s Smoking Habits dataset.
Feature Description:
• Gender: Categorical variable (Male, Female).
• Age: Continuous variable representing the age of partic ipants.
• Income Level: Ordinal variable indicating income brack ets.
• Education Level: Categorical or ordinal variable repre senting educational attainment.
• Smoking Status: Binary variable (smoker or non smoker).
Data Preprocessing:
• Handling Missing Values: Outline techniques used, such as mean imputation for age or mode imputation for categorical features.
• Encoding: Convert categorical variables (gender, education) into numeric form using one-hot or label encoding.
• Normalization: Scale continuous variables to ensure com parability across features.
What Are Categorical Variables?
Categorical variables belong to a subset of variables that can be divided into discrete categories. Names or labels are the most common categories. These variables are also known as qualitative variables because they depict the variable’s quality or characteristics.
Categorical variables can be divided into two categories:
Nominal Variable: A nominal variable’s categories have no natural ordering. Example: Gender, Blood groups.
Ordinal Variable: A variable that allows the categories to be sorted is ordinal variables. Customer satisfaction (Excellent, Very Good, Good, Average, Bad, and so on) is an example.
A. Exploratory Data Analysis (EDA) Descriptive Statistics: Present detailed descriptive statis tics:
• Mean and Median: To understand the central tendency of continuous variables like age and income.
• Variance: To assess the spread or variability within each feature.
• *Skewness:*Measure the asymmetry of the distribution (to detect any skewed patterns).
•Kurtosis: To assess the ”tailedness” of the distribution (to identify any outliers or extreme values).
• Correlation Analysis: Generate a correlation matrix and heatmap for continuous features to visualize relationships.
• Discussion on any notable correlations, e.g., if age corre lates with income or if education level influences income.