PPT-Simple Linear Regression

1 Correlation indicates the magnitude and direction of the linear relationship between two variables Linear Regression variable Y criterion is predicted by variable X predictor using a linear equation. e Ax where is vector is a linear function of ie By where is then is a linear function of and By BA so matrix multiplication corresponds to composition of linear functions ie linear functions of linear functions of some variables Linear Equations Jennifer Kensler. Laboratory for Interdisciplinary Statistical Analysis. Collaboration. . From our website request a meeting for personalized statistical advice. Great advice right now:. Meet with LISA . 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 = . Model . the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed . data.. Formally, the model for multiple linear regression, given . 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. David J Corliss, PhD. Wayne State University. Physics and Astronomy / Public Outreach. Model Selection Flowchart. NON-LINEAR. LINEAR MIXED. NON-PARAMETRIC. Decision: Continuous or Discrete Outcome. PROC LOGISTIC. 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.. What. is . what. ? . Regression: One variable is considered dependent on the other(s). Correlation: No variables are considered dependent on the other(s). Multiple regression: More than one independent variable. Linear Regression Formula: . Used for prediction purposes for values beyond the region of the given data.. Equation: . and . are the means of x and y. is the standard deviation of x. is the covariance. : 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.. . 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 . Lecture Outline. 1. Simple Regression:. . Predictor variables Standard Errors. Evaluating Significance of Predictors . Hypothesis Testing. How well do we know . ?. How well do we know . ?. Multiple Linear Regression: . 1. 2. Office Hours. :. More office hours, schedule will be posted soon.. . On-line office hours are for everyone, please take advantage of them.. . Projects:. Project guidelines and project descriptions will be posted Thursday 9/25.. 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.