PPT-1 Lecture 15: Least Square Regression

Metric Embeddings COMS E69989 F15 Administrivia Plan PS2 Pick up after class 120gt144 auto extension Plan Least Squares Regression finish Metric Embeddings reductions for distances. Proposed is procedure based on adding small positive quantities to the diagonals of the normal equations to obtain estimates with Smaller mean square error The Science Citation Indexe SCI and the Social Sciences Citation Indexe SSCIa indi cate that 1 Weighted Least Squares as a Solution to Heteroskedasticity 5 3 Local Linear Regression 10 4 Exercises 15 1 Weighted Least Squares Instead of minimizing the residual sum of squares RSS 1 x 1 we could minimize the weighted sum of squares WSS 946 PalmistryIs a method of interpreting the shape of the hand and the lines of the palm, to project the character and possible life experiences of an individual. The science is divided into two broad ar Ordinary Least Squares – a regression estimation technique that calculates the Beta-hats -- estimated parameters or coefficients of the model – so as to minimize the sum of the squared residuals.. Design. Basics. Two potential outcomes . Yi(0) . and. Yi(1), . causal effect . Yi(1) − Yi(0), . binary treatment indicator . Wi. , . covariate. Xi, . and the observed outcome equal to:. At . Xi = c . 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. SIT095. The Collection and Analysis of Quantitative Data II. Week 7. Luke Sloan. About Me. Name: Dr Luke Sloan. Office: 0.56 . Glamorgan. Email: . SloanLS@cardiff.ac.uk. To see me: . please email first. Linear Regression. Section 3.2. Reference Text:. The Practice of Statistics. , Fourth Edition.. Starnes, Yates, Moore. Warm up/ quiz . Draw a quick sketch of three scatterplots:. Draw a plot with r . 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 . Chapter 3 – Exploring Data. Day 3. Regression Line. A straight line that describes how a . _________ . variable, . __. ,. . changes as an . ___________ variable. , . ___. ,. . changes. used to . __________ . 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 = . 4. 3. 2. 1. 0. In addition to level 3.0 and above and beyond what was taught in class, students may:. - Make connection with other concepts in math. - Make connection with other content areas..  . 3.2 Least Squares Regression Line. Correlation measures the strength and direction of a linear relationship between two variables.. How do we summarize the overall pattern of a linear relationship?. Draw a line!. 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.