Statistical Analysis Skill

Statistical analysis on a CSV dataset can be performed by applying descriptive statistics, correlation analysis, hypothesis testing, and regression analysis to derive meaningful insights. The process utilizes libraries like pandas, numpy, and scipy for robust computations.

Data normality testing evaluates distribution using the Shapiro-Wilk test and visualizes results with Q-Q plots. This distribution analysis assesses whether your dataset follows a normal distribution using scipy for statistical computations.

Linear regression modeling builds predictive models to evaluate outcomes using scikit-learn, calculating performance metrics like R-squared and RMSE. This regression analysis automates model evaluation for data science workflows.

Hypothesis testing supports t-tests, chi-square tests, and ANOVA to evaluate statistical significance between different data groups. These tests help validate hypotheses about variables such as marketing campaign effectiveness using scipy.

Correlation analysis calculates correlation matrices and specific coefficients with p-values, visualizing variable relationships through heatmaps. This leverages seaborn and pandas to identify correlations between metrics like satisfaction scores and purchasing behavior.

Automated statistical computations require pandas, numpy, scipy, scikit-learn, seaborn, and matplotlib. These dependencies provide the foundational environment for descriptive statistics, hypothesis testing, and data visualizations.