PPT-1 Correlation and Regression Analysis –

An Application Dr Jerrell T Stracener SAE Fellow Leadership in Engineering EMIS 73705370 STAT 5340 PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS Systems Engineering Program. 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 . To Accompany. Business Statistics: A Decision Making Approach, . 8th . Ed.. Chapter 14:. Introduction to Linear Regression and Correlation Analysis. By. Groebner, Shannon, Fry, & Smith. Prentice-Hall Publishing Company. ... beware. Definition. Var. (X+Y) = . Var. (X) + . Var. (Y) + 2·Cov(X,Y). The . correlation. between two random variables is a dimensionless number between 1 and -1.. Interpretation. Correlation measures the . Association and Prediction Using Correlation and Regression. Learning Objectives. Review information from Lecture 10. Understand the relationship between two interval/ratio variables using. Test for association between two variables using correlation and interpret the correlation coefficients. Linear Regression. Chapter 13. Learning Objectives. LO13-1. Explain the purpose of correlation analysis.. LO13-2. Calculate a correlation coefficient to test and interpret the . relationship . between two variables.. REGRESSION ANALYSIS. Al . Muizzuddin. F. HISTORICAL ORIGIN OF THE TERM REGRESSION. The term . regression . was introduced by Francis Galton. .. In a famous paper. , Galton . found that, although there was a tendency for tall parents to . 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.. 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. Correlation and regression are powerful tools, but have limitations.. Correlation and regression describe only linear relationship.. Correlation r and the least-squares regression are not resistant. . Var. (X Y) = . Var. (X) . Var. (Y) 2·Cov(X,Y). The . correlation. between two random variables is a dimensionless number between 1 and -1.. Interpretation. Correlation measures the . strength. of the . Correlation and Regression: The Basics. Finding the relationship between two variables . without being able to infer causal relationships. Correlation is a . statistical technique. used to determine the degree to which two variables are related. 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. Instructor: Prof. Wei Zhu. 11/21/2013. AMS 572 Group Project. Motivation & Introduction – Lizhou Nie. A Probabilistic Model for Simple Linear Regression – Long Wang. Fitting the Simple Linear Regression Model – . Fun facts about the regression line. Equation of regression line: . If we convert our X and Y scores to . z. x. and . z. y. , the regression line through the z-scores is:. Because the means of the z-scores are zero and the standard deviations are 1..