PPT-Regional regression equations

Regional Regression Equations provide estimates of flood frequencies at ungaged sites where we dont have peakflow data and computed flood frequencies Equations are developed for regions with similar hydrologic characteristics. 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. Regression and Forecasting Models . Part . 7 . – . Multiple Regression. Analysis. Model Assumptions. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Inference and Regression. Part . 9 – Linear Model Topics. Agenda. Variable Selection – Stepwise Regression. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 8 . – . Multicollinearity,. Diagnostics. Multiple Regression Models. 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 . Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. Chapter 3 – Exploring Data. Day 3. Regression Line. A straight line that describes how a . _________ . variable, . __. ,. . changes as an . ___________ variable. , . ___. ,. . changes. used to . __________ . Intro to PS Research Methods. Announcements. Final on . May 13. , 2 pm. Homework in on . Friday. (or before). Final homework out . Wednesday 21 . (probably). Overview. we often have theories involving . 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 = . Examples:. . 1). . . x 3 = 15. . 2) p – 5 = 20. 3) 2d = 16. 4). . = 24.  . Multi-Step Equations. Examples—. 5). 2p 15 = 29. 6). 14 – 3n = -10. 7). 27 = -9(y 5). 8). 7a – 3a 2a – a = 16. : A British biometrician, Sir Francis Galton, defined regression as ‘stepping back towards the average’. He found that the offspring of abnormally tall or short parents tends to regress or step back to average.. 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. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.