Quantitative Data Analysis Assistant

Certainly! Let's walk through the process of analyzing the dataset step by step. For this analysis, I'll assume that the dataset is in a tabular format (e.g., CSV or Excel) with columns for Age, Gender, Country, BMI, Smoking Status, and Alcohol Consumption. ### 1. Preprocessing: #### Load the Dataset: ```python import pandas as pd # Assuming the dataset is in a CSV file df = pd.read_csv('your_dataset.csv') ``` #### Check for Missing Values: ```python df.isnull().sum() ``` #### Summary Statistics: ```python df.describe() ``` ### 2. Exploratory Data Analysis (EDA): #### Univariate Analysis: ```python import seaborn as sns import matplotlib.pyplot as plt # Distribution of BMI sns.histplot(df['BMI'], kde=True) plt.title('Distribution of BMI') plt.show() # Count of observations by Country sns.countplot(y='Country', data=df) plt.title('Count of Observations by Country') plt.show() ``` #### Bivariate Analysis: ```python # BMI by Gender sns.boxplot(x='Gender', y='BMI', data=df) plt.title('BMI by Gender') plt.show() # BMI by Smoking Status sns.boxplot(x='Smoking Status', y='BMI', data=df) plt.title('BMI by Smoking Status') plt.show() ``` ### 3. Hypothesis Testing: #### Example: Is there a significant difference in BMI between smokers and non-smokers? ```python from scipy.stats import ttest_ind smokers = df[df['Smoking Status'] == 'Smoker']['BMI'] non_smokers = df[df['Smoking Status'] == 'Non-smoker']['BMI'] t_stat, p_value = ttest_ind(smokers, non_smokers) print(f'T-statistic: {t_stat}, p-value: {p_value}') ``` ### 4. Regression Analysis: #### Example: Predict BMI based on Age, Gender, Smoking Status, and Alcohol Consumption using linear regression. ```python from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error # Prepare data X = df[['Age', 'Gender', 'Smoking Status', 'Alcohol Consumption']] X = pd.get_dummies(X, drop_first=True) # Convert categorical variables to dummy/indicator variables y = df['BMI'] # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Build and train the model model = LinearRegression() model.fit(X_train, y_train) # Make predictions y_pred = model.predict(X_test) # Evaluate the model mse = mean_squared_error(y_test, y_pred) print(f'Mean Squared Error: {mse}') ``` ### 5. Data Visualization for Insights: #### Example: Scatter plot of BMI vs. Age colored by Country ```python plt.figure(figsize=(12, 8)) sns.scatterplot(x='Age', y='BMI', hue='Country', data=df) plt.title('BMI vs. Age by Country') plt.show() ``` ### Conclusion: This is a simplified walkthrough, and you might need to adapt the code based on the actual characteristics of your dataset. Additionally, the choice of analyses and visualizations can vary based on your specific research questions and goals.