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linear regression

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Model Perspective
Model Perspective
Feb 12, 2025 · Fundamentals

Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting

Using a year‑long revenue dataset from an online‑education firm, this article examines how description, causal explanation, and statistical modeling together reveal patterns, uncover underlying drivers, and highlight the limits and uncertainties of forecasting future performance.

business analyticscausal inferencedata analysis
0 likes · 7 min read
Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting
IT Services Circle
IT Services Circle
Dec 31, 2024 · Artificial Intelligence

Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide

This article uses a dialogue format to introduce the fundamentals of linear regression, explain how loss functions such as mean squared error quantify prediction errors, and describe gradient descent as an iterative optimization technique for finding the best model parameters, illustrated with simple numeric examples and visual aids.

AI Basicsgradient descentlinear regression
0 likes · 13 min read
Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide
Model Perspective
Model Perspective
Nov 29, 2024 · Fundamentals

10 Essential Scientific Math Models and How to Fit Them with Origin

This article introduces ten widely used scientific mathematical models, explains their typical application scenarios, and provides step‑by‑step instructions for implementing each model with the Origin data‑analysis software, covering linear, exponential, kinetic, adsorption, enzymatic, and advanced fitting techniques.

Origin softwaredata fittinglinear regression
0 likes · 10 min read
10 Essential Scientific Math Models and How to Fit Them with Origin
Test Development Learning Exchange
Test Development Learning Exchange
Nov 23, 2024 · Artificial Intelligence

Evaluating Linear Regression Model Performance with K-Fold Cross-Validation in Python

This tutorial teaches how to evaluate a linear regression model's performance using K‑fold cross‑validation in Python, covering data loading, preparation, computation of MSE and R² metrics, and visualizing predictions with matplotlib, and interpreting the results.

MSEPythonR2
0 likes · 6 min read
Evaluating Linear Regression Model Performance with K-Fold Cross-Validation in Python
Model Perspective
Model Perspective
Nov 5, 2024 · Fundamentals

How to Build Accurate Sales Forecasts in Excel Using Linear and Multivariate Regression

Learn how to use Excel for mathematical modeling by walking through step‑by‑step case studies that build linear and multivariate regression models to forecast product sales, validate results, and explore additional Excel tools such as Solver and PivotTables.

ExcelMultivariate RegressionSales Forecasting
0 likes · 8 min read
How to Build Accurate Sales Forecasts in Excel Using Linear and Multivariate Regression
IT Services Circle
IT Services Circle
Sep 8, 2024 · Artificial Intelligence

10 Essential Plots for Linear Regression with Python Code Examples

This tutorial explains ten crucial visualizations for linear regression—scatter plot, trend line, residual plot, normal probability plot, learning curve, bias‑variance tradeoff, residuals vs fitted, partial regression, leverage, and Cook's distance—each illustrated with clear Python code using scikit‑learn, matplotlib, seaborn, and statsmodels.

Data VisualizationMatplotlibPython
0 likes · 21 min read
10 Essential Plots for Linear Regression with Python Code Examples
Python Programming Learning Circle
Python Programming Learning Circle
Apr 10, 2024 · Artificial Intelligence

Top 10 Machine Learning Algorithms Explained

This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental algorithms—including linear and logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, bagging with random forests, and boosting with AdaBoost—guiding beginners on how to choose the right model.

AIAlgorithmsSVM
0 likes · 14 min read
Top 10 Machine Learning Algorithms Explained
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 5, 2024 · Artificial Intelligence

Linear Regression Algorithm: Definition, Structure, Implementation, Cost Function, Gradient Descent, and Regularization

This article provides a comprehensive overview of linear regression, covering its definition, purpose, algorithmic steps, data preparation, feature scaling, parameter initialization, cost function computation, gradient descent optimization, visualization, normal equation solution, and regularization, accompanied by detailed Python code examples.

NumPyPythoncost function
0 likes · 19 min read
Linear Regression Algorithm: Definition, Structure, Implementation, Cost Function, Gradient Descent, and Regularization
Model Perspective
Model Perspective
Feb 1, 2024 · Fundamentals

Essential Guide to Statistical and Probabilistic Model Articles

This curated list gathers recent articles on statistical and probabilistic models, covering clustering analysis, various linear regression techniques, and causal analysis, providing convenient links for students and researchers to explore each topic in depth.

Clusteringcausal analysislinear regression
0 likes · 3 min read
Essential Guide to Statistical and Probabilistic Model Articles
Model Perspective
Model Perspective
Oct 25, 2023 · Operations

How Math Models Can Turn Your Coffee Shop into a Profit Machine

This article shows how forecasting, linear programming, EOQ inventory, pricing elasticity, and location‑selection models can be applied to a coffee shop to predict foot traffic, optimize menus, reduce waste, set optimal prices, and choose the best site, ultimately boosting profitability.

Linear Programmingdemand forecastinginventory management
0 likes · 11 min read
How Math Models Can Turn Your Coffee Shop into a Profit Machine
Model Perspective
Model Perspective
Dec 2, 2022 · Fundamentals

Master Linear Regression in R: From Random Data to Insightful Models

This article explains the theory behind simple linear regression, demonstrates how to generate random data and fit a model using R's lm() function, and interprets the statistical output including coefficients, significance tests, and goodness‑of‑fit measures.

Rdata analysislinear regression
0 likes · 4 min read
Master Linear Regression in R: From Random Data to Insightful Models
Model Perspective
Model Perspective
Nov 20, 2022 · Fundamentals

How Simple Linear Regression Predicts Outcomes: Model, Assumptions, and Evaluation

This article explains the simple linear regression model, its six key assumptions, how to evaluate the fit using ANOVA and the coefficient of determination, and covers hypothesis testing and confidence intervals for regression coefficients with practical examples.

ANOVAconfidence intervalhypothesis testing
0 likes · 9 min read
How Simple Linear Regression Predicts Outcomes: Model, Assumptions, and Evaluation
Model Perspective
Model Perspective
Nov 8, 2022 · Artificial Intelligence

How to Solve Multiple Linear Regression with sklearn and statsmodels in Python

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.

Pythondata analysislinear regression
0 likes · 5 min read
How to Solve Multiple Linear Regression with sklearn and statsmodels in Python
Model Perspective
Model Perspective
Oct 11, 2022 · Artificial Intelligence

Unlocking Interpretable Machine Learning: From Linear Regression to EBM

This article surveys intrinsic interpretable machine‑learning models—from classic regression, additive models, and decision trees to modern approaches like Explainable Boosting Machines, GAMINet, RuleFit, and Falling Rule Lists—explaining their principles, parameter estimation, interpretability, advantages, and limitations.

explainable boosting machinegeneralized linear modelinterpretable machine learning
0 likes · 12 min read
Unlocking Interpretable Machine Learning: From Linear Regression to EBM
Model Perspective
Model Perspective
Oct 7, 2022 · Artificial Intelligence

Master Gradient Descent: From Intuition to Advanced Variants

This comprehensive guide explains the mathematical foundation, intuitive intuition, algorithmic steps, tuning strategies, and variants of gradient descent, comparing it with other optimization methods and illustrating its use in machine‑learning models such as linear regression.

AlgorithmOptimizationgradient descent
0 likes · 14 min read
Master Gradient Descent: From Intuition to Advanced Variants
Model Perspective
Model Perspective
Sep 19, 2022 · Artificial Intelligence

Master Bayesian Linear Regression with PyMC3: A Hands‑On Guide

This tutorial explains how to use PyMC3 for Bayesian linear regression, covering model definition, data simulation, MAP estimation, advanced MCMC sampling with NUTS, and posterior analysis, all illustrated with complete Python code examples.

Bayesian inferenceMCMCPyMC3
0 likes · 11 min read
Master Bayesian Linear Regression with PyMC3: A Hands‑On Guide
Model Perspective
Model Perspective
Sep 18, 2022 · Artificial Intelligence

How Bayesian Linear Regression Reveals Uncertainty in Model Parameters

This article explains Bayesian linear regression, describing its probabilistic treatment of weights, prior and posterior computation, MAP and numerical solutions, and how it enables uncertainty quantification, online learning, and model comparison through Bayes factors.

Bayesian inferenceMAP estimationMCMC
0 likes · 9 min read
How Bayesian Linear Regression Reveals Uncertainty in Model Parameters
Model Perspective
Model Perspective
Sep 13, 2022 · Fundamentals

Why Linear Regression Is Surprisingly Powerful for Causal Inference

This article explains how linear regression can be used to estimate average causal effects, handle bias, and draw valid conclusions from both randomized experiments and observational data, while illustrating the theory with concrete examples and visualizations.

average treatment effectcausal inferencelinear regression
0 likes · 16 min read
Why Linear Regression Is Surprisingly Powerful for Causal Inference
Model Perspective
Model Perspective
Sep 7, 2022 · Artificial Intelligence

How to Solve Multiple Linear Regression with sklearn and statsmodels in Python

This guide demonstrates how to perform multiple linear regression in Python using sklearn's LinearRegression and the statsmodels library, covering data preparation, model fitting, coefficient extraction, prediction, and detailed statistical diagnostics with example cement heat data.

Pythondata analysislinear regression
0 likes · 5 min read
How to Solve Multiple Linear Regression with sklearn and statsmodels in Python
Model Perspective
Model Perspective
Aug 25, 2022 · Artificial Intelligence

Mastering Regression: Key Assumptions, Metrics, and Model Evaluation

This article explains the fundamental assumptions of linear regression, compares linear and nonlinear models, discusses multicollinearity, outliers, regularization, heteroscedasticity, VIF, stepwise regression, and reviews essential evaluation metrics such as MAE, MSE, RMSE, R² and Adjusted R².

linear regressionmachine learningmetrics
0 likes · 12 min read
Mastering Regression: Key Assumptions, Metrics, and Model Evaluation