Easy Statistics: Linear and Non-Linear Regression | Economics Online Course by Udemy

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An easy introduction to Ordinary Least Squares, Logit and Probit regression and tips for regression modelling.

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Easy Statistics: Linear and Non-Linear Regression

Three courses combined. Linear andNon-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 andProbit 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. RegressionModellingThe 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 RegressionModelling – 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

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