Coursera regression modeling. Log into your Coursera account with your email address, Google, Facebook, or Apple credential. You've built models to tackle linear regression problems and classification problems. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. In this course, you’ll build and interpret linear and logistic regression models in R to uncover relationships, make predictions, and quantify uncertainty. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother Reviews summary Practical regression modeling overview According to learners, this course provides a solid foundation in core regression concepts, covering linear, multiple, and logistic models. Additionally, you will be introduced to the least square method as well as performing statistical This module introduces learners to advanced regression methods, focusing on predicted values, scatterplots, regression outputs, and diagnostics. Jul 27, 2025 · The course also emphasizes the transition from traditional inference to predictive analytics, teaching you to evaluate models honestly and deploy them effectively for future data. Learn online and earn valuable credentials from top universities like Yale, Michigan, Stanford, and leading companies like Google and IBM. The New York State Department of Labor is partnering with Coursera, the leading online training provider. This course delves into regression analysis using R, covering key concepts, software tools, and differences between statistical analysis and machine learning. This Predictive Modeling with Python course provides a practical introduction to statistical analysis and machine Enroll for free. The course rounds off with regression modeling strategies, including managing multicollinearity, handling missing values, and working with categorical explanatory variables. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Offered by Edureka. You will learn how to formulate a simple regression model and fit the model to data using both a closed-form solution as well as an iterative optimization algorithm called gradient descent. Throughout the course, you will share with others the regression models you have developed and the stories they tell you. Oct 14, 2025 · Getting started with Coursera Regression analysis allows you to explore the relationship between a dependent and independent variable to predict future outcomes. One of the other major machine learning tasks that you might want to engage in is clustering, a form of unsupervised learning. - At least a little This is the fifth of eight courses in the Google Advanced Data Analytics Certificate. These models allow you to assess the relationship between variables in a data set and a continuous response variable. It was founded in 2012 [2][3] by Stanford University computer science professors Andrew Ng and Daphne Koller. You'll learn about different methods of data modeling and how to use You’ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilistic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps Oct 27, 2025 · Learn how to move from exploring data to modeling it with confidence. There are 6 modules in this course Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. You will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Transform you career with Coursera's online Linear Regression courses. You’ll also explore dynamic relationships using time-based data and understand how to interpret regression outputs effectively. Learners will gain practical skills in interpreting coefficients, testing significance, and identifying issues such as multicollinearity to ensure robust regression modeling. Through Coursera, unemployed New Yorkers will have access to nearly 4,000 programs across high-growth industries and can hone skills in data science, business, and technology. They will also learn how to fit and interpret regression models for a continuous outcome with multiple predictors. In addition to the theory, we will perform linear regression on real data using both Excel and Python. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. You learn to use logistic regression to model an Transform you career with Coursera's online Regression courses. ” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how Regressions model this causality, and then we can use these models in order to forecast, and then plan for our business' needs. Learners will explore the conceptual foundations of regression, understand the importance of statistical significance, and practice visualizing data relationships. This module introduces the fundamentals of linear regression modeling using SPSS. - You'll learn data reading, cleaning, exploratory data analysis, and ordinary least squares (OLS) regression modeling, including theory, implementation, and result interpretation. The "Regression Analysis" course equips students with the fundamental concepts of one of the most Enroll for free. You’ll begin by learning how to fit and interpret simple and multiple linear regression models, then advance to modeling categorical outcomes with logistic regression Oct 14, 2025 · Learn more on Coursera. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Coursera provides flexible learning options in diverse fields like business, technology, and healthcare. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Join today! Offered by University of Colorado Boulder. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. This course covers regression analysis, least squares and inference using regression models. “Linear Regression and Modeling” is course 3 of 5 in the Statistics with R Coursera Specialization. To learn more about logistic regression and data analysis, take exciting courses on the Coursera learning platform Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Coursera Inc. There are 4 modules in this course "Regression Modeling for Marketers" is a specialized course designed to elevate marketing professionals' analytical skills. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. Students find value in the focus on practical application, appreciating the opportunity to use either SAS or Python for hands-on work. Additionally, the segment on categorical data analysis and logistic regression offers valuable insights into modeling binary outcomes, broadening your analytical toolkit. Learn to select the best approach for your projects. Learners will get their first look at the ever-popular t-test and delve further into linear regression. This course introduces simple and multiple linear regression models. "Coursera's reputation for high-quality content, paired with its flexible structure, made it possible for me to dive into data analytics while managing family, health, and everyday life. Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: regression, regression models, applying the method of least squares and, understanding linear regression models. - A basic understanding of statistics and regression models. Transform you career with Coursera's online Regression Analysis courses. This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. This class is an introduction to least squares from a linear algebraic and mathematical perspective. e. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Enroll for free, earn a certificate, and build job-ready skills on your schedule. Join today! But, unlike the multiple regression model, the logistic regression model is designed to test binary response variables. It explains the Linear Probability Model (LPM) in terms of its theoretical foundations, computational applications, and empirical limitations. " Coursera is an online learning platform offering courses, certifications, and degree programs from top universities and organizations worldwide. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. We will explore simple regression models, multiple regression models, dummy variable regressions, seasonal variable regressions, as well as autoregressions. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. A Bayesian approach to modeling time series is considered next. Then the module introduces and demonstrates the Logistic Regression as a better substitute of LPM for the Oct 13, 2025 · How to perform linear regression You can perform linear regression by hand or with the help of statistical software. [4] Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects. Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. Offered by Erasmus University Rotterdam. , Win, Draw, Lose). In this module, we will learn how to fit linear regression models with least squares. In general, linear regression is most effectively performed with the help of computer software. Is there a relationship between the physical attractiveness of a professor and their student evaluation This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. In this course, you’ll practice modeling variable relationships. Join today! This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models. . Transform you career with Coursera's online Regression Models courses. This software can perform both simple and multiple linear regression, producing different models with various variable combinations. Start with regression analysis, mastering linear regression for continuous variable prediction and logistic regression for binary classification. Emphasis will be placed on a firm conceptual understanding of these tools. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models. Topics include data visualization, predictive model development, simple linear regression, multivariate linear regression, multivariate linear regression with interaction, and logistic regression. - At We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data. Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Strong emphasis will be placed on gathering and analyzing climate data with the Python programming language. Logistic regression is a predictive analytics model popular among professionals in science and mathematical fields. Depending on your variable types, you can choose different types of logistic regression, including binary, ordinal, and multinomial options. This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This folder contains the answer keys to the Coursera course Linear Regression and Modeling (part of the Statistics with R Specialization) by Duke University, slides and the weekly lab R code. Welcome to Linear Regression! In this course, we will cover the following topics: Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors. On February 2, 2021, Coursera announced its B University of Michigan students, faculty, and staff can sign up anytime for free access to Coursera’s growing catalog of more than 3,700 Guided Projects, courses, Specializations and Professional Certificates from university and industry partners all over the world. This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Linear Regression and Modeling. Focusing on regression analysis, the course enables learners to quantify, explain, and predict marketing outcomes using both simple and multiple linear regression models. Based on this fitted function, you will interpret the estimated model parameters and form predictions. By the end of the course, you will be able to: - Understand the concepts, processes, and applications of predictive modeling. You will gain experience testing and interpreting a logistic regression model, including using odds ratios and confidence intervals to determine the magnitude of the association between your explanatory variables and response This Supervised and Unsupervised Machine Learning program covers essential techniques for data modeling and analysis. This course provides an overview of linear regression. Learn without limits Start, switch, or advance your career with more than 5,000 courses, Professional Certificates, and degrees from world-class universities and companies. (/ kərˈsɛrə /) is an American global massive open online course provider. In Module 1, we will focus on defining the problem and setting up the simple linear regression model. With regression analysis, you can observe how strongly each independent variable influences the model. Understand various time-series models and implement them using Python Prepare and preprocess data for accurate linear regression modeling Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. Join today! Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. This helps professionals across industries make informed decisions, predict future outcomes, and explore how modifying certain This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i. In this course, PhD candidates will get an introduction into the theory of multilevel modelling, Enroll for free. Oct 27, 2025 · This course introduces simple and multiple linear regression models. s7r giclg hd5 zqd zzu5ul fl2ar wwwe9a 0ahg woelly3 44n7m