PPT-Simple Linear Regression & Correlation

Instructor Prof Wei Zhu 11212013 AMS 572 Group Project Motivation amp Introduction Lizhou Nie A Probabilistic Model for Simple Linear Regression Long Wang Fitting the Simple Linear Regression Model . The simple regression model formulas 4 Take aways 1 Introduction to linear regression Regression analysis is the art and science of fitting straight lines to patterns of data In a linear regression model the variable of interest the so called depend and regression. Scatter plots. A scatter plot is a graph that shows the relationship between the observations for two data series in two dimensions.. Scatter plots are formed by using the data from two different series to plot coordinates along the . Bivariate. Data . With Fathom. *. CFU 3102.5.10 Using technology with a set of contextual linear data to examine the line of best fit;. determine and interpret the correlation coefficient.. Andy Wilson – APSU – . 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 (. An Application. Dr. Jerrell T. Stracener, . SAE Fellow. Leadership in Engineering. EMIS 7370/5370 STAT 5340 :. . . PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS. Systems Engineering Program. Time and space records:. long jump, one hundred meters. are getting closer.. . (NG). Scatter. Correlation 0.58. Leaving out . obs. 9: 0.94. Rank correlation. Correlation between ranks is 0.67. Spearman correlation. 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.. 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 = . How to predict and how it can be used in the social and behavioral sciences. How to judge the accuracy of predictions. INTERCEPT and SLOPE functions. Multiple regression. This week. 2. Based on the correlation, you can predict the value of one variable from the value of another.. What is Correlation Analysis?. Testing the Significance of the Correlation Coefficient . Regression Analysis. The Standard Error of Estimate . Assumptions Underlying Linear Regression. Confidence and Prediction Intervals. ;. some. do’s . and. . don’ts. Hans Burgerhof. Medical. . S. tatistics. and . Decision. Making. Department. of . Epidemiology. UMCG. . Help! Statistics! Lunchtime Lectures. When?. Where?. What?. Prepared by T.O. . Antwi. -Asare . 2/2/2017. 1. Correlation and Regression . Correlation. Scatter Diagram,. Karl Pearson Coefficient of Correlation. Rank Correlation. Limits for Correlation Coefficient. 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.. Simple Linear Regression. April 17, 2018. Correlation . analysis* . M. easuring the degree . of association between two . continuous variables. , x and . y. We . have a . linear relationship. between x and y .