This article provides comprehensive regression analysis methods and Python code examples for various business scenarios including e-commerce, market research, healthcare, finance, social media, HR, education, hospitality, marketing, and logistics.
This article introduces the Python Statsmodels library, explains its key features such as linear regression, GLM, time‑series and robust methods, shows how to install it, load data with pandas, perform descriptive statistics, visualizations, hypothesis testing, and simple and multiple linear regression examples.
This article explains the concept of Granger causality, outlines the four possible test outcomes, and provides a step‑by‑step Python implementation using statsmodels to determine whether one time‑series statistically causes another.
This article explains the fundamentals of weakly stationary time series, defines mean, variance, autocovariance, and autocorrelation functions, introduces AR, MA, ARMA, and ARIMA models, discusses model identification using ACF/PACF, selection criteria like AIC/SBC, diagnostic testing, and provides Python statsmodels code examples for implementation.
This article demonstrates how to perform multiple linear regression using sklearn's LinearRegression and the statsmodels library in Python, covering both formula‑based and array‑based approaches, complete with example data, code snippets, and model evaluation details.
This article introduces key Python libraries—including NumPy, Pandas, Matplotlib, Statsmodels, Scikit‑Learn, and Keras—detailing their core functionalities for data handling, statistical modeling, and machine‑learning tasks, and provides concise usage insights for each package.
Learn to load the sunspots.csv dataset, fit an ARMA(4,2) model with statsmodels, evaluate it using AIC/BIC, visualize original and predicted values, and forecast the 1989 sunspot count, all with step‑by‑step Python code and plots.
This article demonstrates how to perform multiple linear regression using scikit‑learn's LinearRegression and the statsmodels library, covering data preparation, model fitting, code examples, and interpretation of statistical results for a cement heat‑release case study.
This article explains the concepts of odds and odds ratios, shows how logistic regression estimates them, and demonstrates practical model fitting and prediction using Python's statsmodels and scikit‑learn libraries with real‑world examples.
This article explains the theory behind two‑factor ANOVA, distinguishes cases with and without interaction, presents the mathematical model, and demonstrates a complete Python implementation using statsmodels, including data setup, model fitting, and interpretation of the ANOVA table.
This article explains the concept of one‑way ANOVA, walks through a real‑world example comparing four manufacturing processes, and demonstrates how to conduct the analysis in Python using statsmodels, interpreting the resulting F‑statistic and p‑value to assess significance.
This article provides a concise overview of key Python data‑analysis libraries—including NumPy, pandas, matplotlib, IPython/Jupyter, SciPy, scikit‑learn, and statsmodels—explaining their core features, typical use cases, and how they interoperate to form a powerful scientific computing ecosystem.
This article walks through constructing a multiple linear regression model for house price prediction using Python, covering data exploration, dummy variable creation, model fitting with statsmodels, diagnosing multicollinearity via VIF, and applying optimizations to improve predictive accuracy.
This article explains how to process a periodic time‑series, split it into training and test sets, smooth the data, decompose it with statsmodels' seasonal_decompose, forecast the trend using an ARIMA model, and evaluate the results with RMSE, providing a practical workflow for accurate forecasting.
