PPT-Chapter 9: Regression

Alexander Swan amp Rafey Alvi Residuals Grouping No regression analysis is complete without a display of the residuals to check that the linear model is reasonable Residuals often reveal subtleties that were not clear from a plot of the original data. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model isavectorofparameterstobeestimatedand x isavectorofpredictors forthe thof observationstheerrors areassumedtobenormallyandindependentlydistributedwith mean 0 and constant variance The function relating the average value of the response to the pred Methods for Dummies. Isobel Weinberg & Alexandra . Westley. Student’s t-test. Are these two data sets significantly different from one another? . William Sealy Gossett. Are these two distributions different?. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistics and Data Analysis. Part . 6 – Regression Model-1. Conditional Mean . U.S. Gasoline Price. Monotonic but Non-Linear. The relationship between X and Y may be monotonic but not linear.. The linear model can be tweaked to take this into account by applying a monotonic transformation to Y, X, or both X and Y.. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models . Part . 7 . – . Multiple Regression. Analysis. Model Assumptions. SIT095. The Collection and Analysis of Quantitative Data II. Week 9. Luke Sloan. Introduction. Recap – Last Week. Workshop Feedback. Multinomial Logistic Regression in SPSS. Model Interpretation. In Class Exercise. Eric Feigelson. Classical regression model. ``The expectation (mean) of the dependent (response) variable Y for a given value of the independent variable X (or vector of variables . X. ) is equal to a specified mathematical function . 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. NBA 2013/14 Player Heights and Weights. Data Description / Model. Heights (X) and Weights (Y) for 505 NBA Players in 2013/14 Season. . Other Variables included in the Dataset: Age, Position. Simple Linear Regression Model: Y = . In linear regression, the assumed function is linear in the coefficients, for example, . .. Regression is nonlinear, when the function is a nonlinear in the coefficients (not x), e.g., . T. he most common use of nonlinear regression is for finding physical constants given measurements.. Fun facts about the regression line. Equation of regression line: . If we convert our X and Y scores to . z. x. and . z. y. , the regression line through the z-scores is:. Because the means of the z-scores are zero and the standard deviations are 1.. . Lecture compiled by. Dr. . Parminder. . Kaur. Assistant Professor. Department of Commerce. For . B.Com. (. Prog. ) II . Sem. . Sec A. SIMPLE . LINEAR . REGRESSION. DEFINITION OF . REGRESSION . 2. Dr. Alok Kumar. Logistic regression applications. Dr. Alok Kumar. 3. When is logistic regression suitable. Dr. Alok Kumar. 4. Question. Which of the following sentences are . TRUE.  about . Logistic Regression.