Basic concepts of Regression
Über den Vortrag
Der Vortrag „Basic concepts of Regression“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Quantitative Analysis“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Basic concept of regression
- The million dollar question
- Introduction to regression analysis
- Types of regression models
- Population linear regression
- Sample regression function
- The error term (residual)
- OLS regression properties
- The least squares equation
- Assumptions underlining linear regressions
Dozent des Vortrages Basic concepts of Regression
Edu Pristine
Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
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Auszüge aus dem Begleitmaterial
... - Sample Regression Line, - Hypothesis Testing, - Explained and Unexplained Variation, - Residual AnalysisEstimating ...
... with one Regressor Expect around 6–8 questions ...
... analysis is to measure how the changes in one variable known as dependent variable can be explained by the changes in one or more other variables called the independent variables Linear relationship ...
... Dependent variable: the variable we wish to explain ...
... Random Error term, or residual Dependent Variable ...
... Estimated (or predicted) y value. Estimate of the regression slope. ...
... Wouldn’t it be good if we were able to reduce this error term? ...
... Are there any advantages of minimizing ...
... The simple regression line always passes through the mean of the y variable and the mean 0 ...
... More often than not it does not have a physical interpretation ...
... Error values u for given Xiare statistically independent, their covariance is zero. Once we fulfill these assumptions in Linear Regression, we are able to estimate the variance. ...
