PPT-Regression Analysis Gnyana

Ranjan Parida AsstProf SPLSCUTM Example2 Deviation method from mean Regression equation for X on Y X x b xy Y ȳ Where bxy is the regression coefficient and . What did we use software & technology for?. Data Cleansing. Identifying . Patterns . and T. rends. Regression Analysis. Comp Selection. Comp Based Valuation. Ratio Analysis . Post Analysis (Looking for properties with high risk of appeal) . Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistics and Data Analysis. Part . 10 – Advanced Topics. Advanced topics. Nonlinear Least Squares. Nonlinear Models – ML Estimation . Introduction. Course Information. Your instructor: . Hyunseung. (pronounced Hun-Sung). Or HK (not Hong Kong . ). E-mail. : khyuns@wharton.upenn.edu . Lecture:. Time: Mon/Tues/Wed/. Thur. . at 10:45AM-12:15PM. An Application. Dr. Jerrell T. Stracener, . SAE Fellow. Leadership in Engineering. EMIS 7370/5370 STAT 5340 :. . . PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS. Systems Engineering Program. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Inference and Regression. Perfect Collinearity. Perfect Multicollinearity. If . X. does not have full rank, then at least one column can be written as a linear combination of the other columns.. REGRESSION ANALYSIS. Al . Muizzuddin. F. HISTORICAL ORIGIN OF THE TERM REGRESSION. The term . regression . was introduced by Francis Galton. .. In a famous paper. , Galton . found that, although there was a tendency for tall parents to . 1. 3.6 Hidden Extrapolation in Multiple Regression. In prediction, exercise care about potentially extrapolating beyond the region containing the original observations.. Figure 3.10. An example of extrapolation in multiple regression.. Stat-GB.3302.30, UB.0015.01. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction. . Professor William Greene; Economics and IOMS Departments. Dummy variables as an independent variable. Dummy variable trap. Importance of the "reference group". Using dummy variables to test for equal means. Dummy variables for . Multiple categories. Ordinal variables. Example Data Set. Y. X. 5. 20. 6. 23. 7. 27. 8. 33. 8. 31. 9. 35. 10. 43. 5. 19. 6. 25. 7. 29. 8. 31. Estimate two models. Model with y-intercept. Y = a b * X. Regression Statistics. Multiple R. 0.984. 9-. 1. 2. Objectives. Understand the basic types of data. Conduct basic statistical analyses in Excel. Generate descriptive statistics and other analyses using the Analysis . ToolPak. Use regression analysis to predict future values. Class 27 Example: Height and Weight Case: Colonial Broadcasting (HBS: 9-894-011) Heights and Weights of n=30 11-year-old girls CM Inches KG 135 53 26 146 57 33 153 60 55 154 61 50 139 55 32 131 52 25 INTRODUCTİON. . HISTORY. 1822-1911. : . Sir . Galton . "REGRESSION" WAS COINED. 1805. : . LENARDE. METHOD OF LEAST SQUARES. 1809. : . GAUSS. METHOD OF LEAST SQUARES. . HISTORY. 1857-1936. : . Karl Pearson. Obid. . A.Khakimov. OLS Estimation: Hetroscedasticity. If variance of residuals is constant then . Our equation collapses to original variance . Formula.. Consequences:. The regression coefficients are unbiased .