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所在平台: Udemy |
课程主页: https://www.udemy.com/course/easy-statistics-linear-and-non-linear-regression/
课程评论：没有评论
Coursera 课程“轻松统计学：线性与非线性回归”概述： 本课程旨在用简洁易懂的方式，帮助学习者掌握统计学基础，特别是线性回归、非线性回归和回归建模。课程将深入讲解普通最小二乘法（OLS）、Logit 回归和 Probit 回归，并完全避免复杂的数学公式，侧重于实际应用和结果解释。 **课程内容亮点：** * **线性回归：** 探索普通最小二乘法（OLS）的工作原理，理解回归术语、OLS 假设，并能熟练解读和分析 OLS 回归输出，学习相关的技巧。 * **非线性回归：** 介绍非线性回归的概念，包含 Logit 和 Probit 模型，理解最大似然估计等工作机制，并能够解读非线性回归结果，掌握相关技巧。 * **回归建模：** 提供实用的回归建模技巧，涵盖函数形式、交互效应、时间序列数据处理、分类变量的使用及多重共线性、缺失数据等常见问题处理方法。 **主要学习目标：** * 掌握普通最小二乘法、Logit 和 Probit 回归的基本统计直觉。 * 熟悉回归术语和 OLS 的各项假设。 * 能够自信地解读和分析各种回归输出。 * 学习实用的统计分析技巧。 **授课方式：** 课程将通过动画图形展示统计概念，并通过 Stata 软件进行实际操作演示，帮助学习者更好地理解和应用所学知识。 **目标受众：** 本课程适合所有需要接触定量分析的学习者，无需任何先修知识。 **课程涵盖的具体主题包括：** * 不同类型的回归分析 * 相关性与因果关系 * 最小二乘法、R 方、Beta、标准误、t 统计量、p 值、置信区间 * 高斯-马尔可夫假设、偏差与效率、同方差性、多重共线性 * 函数形式、零条件均值、对数回归 * 最大似然估计、线性概率模型 * 潜在变量、边际效应、虚拟变量 * 拟合优度统计量、优势比 * Stata 软件在 Logit 和 Probit 模型中的应用 * 回归建模中的函数形式、交互效应、时间变量、分类变量处理等。 （请注意：课程提供了 Twitter 账号 @easystats3 用于获取促销代码和其他更新。）
Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3)Three courses combined. Linear and Non-Linear Regression and Regression Modelling.Learning and applying new statistical techniques can often be a daunting experience."Easy Statistics" is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.This course will focus on the concept of linear regression, non-linear regression and regression modelling. Specifically Ordinary Least Squares, Logit and Probit Regression.The first two parts will explain what regression is and how linear and non-liner regression works. It will examine how Ordinary Least Squares (OLS) works and how Logit and Probit models work. It will do this without any complicated equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts.No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis.The main learning outcomes are:To learn and understand the basic statistical intuition behind Ordinary Least SquaresTo be at ease with general regression terminology and the assumptions behind Ordinary Least SquaresTo be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least SquaresTo learn tips and tricks around linear regression analysisTo learn and understand the basic statistical intuition behind non-linear regressionTo learn and understand how Logit and Probit models workTo be able to comfortably interpret and analyze complicated regression output from Logit and Probit regressionTo learn tips and tricks around non-linear Regression analysisSpecific topics that will be covered are:What kinds of regression analysis existCorrelation versus causationParametric and non-parametric lines of best fitThe least squares methodR-squaredBeta's, standard errorsT-statistics, p-values and confidence intervalsBest Linear Unbiased EstimatorThe Gauss-Markov assumptionsBias versus efficiencyHomoskedasticityCollinearityFunctional form Zero conditional mean Regression in logsPractical model buildingUnderstanding regression outputPresenting regression outputWhat kinds of non-linear regression analysis existHow does non-linear regression work?Why is non-linear regression useful?What is Maximum Likelihood?The Linear Probability ModelLogit and Probit regressionLatent variablesMarginal effectsDummy variables in Logit and Probit regressionGoodness-of-fit statisticsOdd-ratios for Logit modelsPractical Logit and Probit model building in StataThe computer software Stata will be used to demonstrate practical examples. Regression ModellingThe third part provides useful practical tips for regression modelling. Understanding how regression analysis works is only half the battle. There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these sessions, we will examine some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them? Each topic has a practical demonstration in Stata. Themes include:Fundamental of Regression Modelling - What is the Philosophy?Functional Form - How to Model Non-Linear Relationships in a Linear RegressionInteraction Effects - How to Use and Interpret Interaction EffectsUsing Time - Exploring Dynamics Relationships with Time InformationCategorical Explanatory Variables - How to Code, Use and Interpret themDealing with Multicollinearity - Excluding and Transforming Collinear VariablesDealing with Missing Data - How to See the Unseen
