PPT-Previous Lecture: Regression and Correlation

Introduction to Biostatistics and Bioinformatics Proteomics Informatics This Lecture Proteomics Informatics Learning Objectives Structure of m ass spectrometry data Protein identification. . 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. The Data. http://. core.ecu.edu/psyc/wuenschk/SPSS/SPSS-Data.htm. Corr_Regr. See . Correlation and Regression Analysis: . SPSS. Master’s Thesis, Mike Sage, 2015. Cyberloafing. = Age. , Conscientiousness. ... 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 . 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.. 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.. 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 . a measure of the extent to which two variables change together.. How well does A predict B?. The correlation may be positive, negative, or have no relationship.. Correlation. A . positive correlation . 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. Summary of the measure of the characteristics of individuals in groups.. A descriptive statistic talks about a single characteristics within a given group. Lots of descriptive statistics are summarizing lots of characteristics but all within a given group.. 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 – . -2-Note that-1 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..