PPT-Regression Transformations for Normality and to Simplify Relationships

US Coal Mine Production 2011 Source wwweiagov Data Description Coal Mine Production and Labor Effort for all Mines Producing Over 100000 short tons of Coal in 2011 Units Mine n 691. a 12 22 a a mn is an arbitrary matrix Rescaling The simplest types of linear transformations are rescaling maps Consider the map on corresponding to the matrix 2 0 0 3 That is 7 2 0 0 3 00 brPage 2br Shears The next simplest type of linear transfo . 12. Correlation. and . linear. . regression. y = . ax. + b. The. . least. . squares. . method. of Carl Friedrich . Gauß. .. D. y. 2. OLRy. D. y. Covariance. Variance. C. orrelation. coefficient. Regression. slide . 1. Addendum. Testing assumptions of simple linear regression. 1. KNR 445. Regression. slide . 2. Now, how does one go about it?. The approach taken in this course will be to teach you to control . 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 = . U.S. Coal Mine Production – 2011. Source: www.eia.gov. Data Description. Coal Mine Production and Labor Effort for all Mines Producing Over 100,000 short tons of Coal in 2011. Units: Mine (n = 691). What is an association between variables?. Explanatory and response variables. Key characteristics of a data set. 1. Association between a pair of variables. Association:. Some values of one variable tend to occur more often with certain values of the other variable. Intro:. This section is going to focus on relationships among several variables for the same group of individuals. In these relationships, does one variable cause the other variable to change?. Explanatory Variable. Instructional Materials. http://. core.ecu.edu/psyc/wuenschk/PP/PP-MultReg.htm. aka. , . http://tinyurl.com/multreg4u. Introducing the General. Linear Models. As noted by the General, the GLM can be used to relate one set of things (. “Essentially. , all models are wrong, but some are . useful”. George E.P. Box. Your model . has to be. wrong…. … but that’s o.k.. if it’s illuminating!. Linear Model. Assumptions. Absence of. Examine the concepts of normality . and abnormality. What is normal . behaviour. ?. In pairs or small groups discuss examples of . behaviour. that is normal, and . behaviour. that is abnormal.. Examine the concepts of normality . Linear Function. Y = a + bX. Fixed and Random Variables. A FIXED variable is one for which you have every possible value of interest in your sample.. Example: Subject sex, female or male.. A RANDOM variable is one where the sample values are randomly obtained from the population of values.. Data Analysis & Computers II. Slide . 1. Assumption of normality. Assumption of normality. Transformations. Assumption of normality script. Practice problems. SW388R7. Data Analysis & Computers II. Section 3.2. Least-Squares Regression. Least-Squares Regression. MAKE predictions using regression lines, keeping in mind the dangers of extrapolation.. CALCULATE and interpret a residual.. INTERPRET the slope and . Presented by. Kelly Benson. Mouiad Al-Wahah. 1. Definitions we agreed upon... Def 1. . Diagnosis is the problem of trying to find what is wrong with some system based on knowledge about the design/structure of the system, possible malfunctions that can occur in the system and observations made of the behavior of the system..