PPT-Correlation and

Linear Regression Chapter 13 Learning Objectives LO131 Explain the purpose of correlation analysis LO132 Calculate a correlation coefficient to test and interpret the relationship between two variables. Chapter 10. Outline . Section 10-1 Introduction. Section 10-2 Scatter Plots. Section 10-3 Correlation. Section 10-4 Regression. Section 10-5 Coefficient of Determination and Standard Error of the Estimate. 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. Pb+Pb. collisions. Victor Roy. ITP Goethe . University Frankfurt. In collaboration with. L-G Pang, G-Y Qin, X-N Wang and G-L Ma. Plan of the talk. Motivation. Dynamical models . -3+1 D Ideal Hydro . Siwei. . Liu. 1,. Yang Zhou. 1. , Richard Palumbo. 2. , & Jane-Ling Wang. 1. 1. UC Davis; . 2. University of Rhode Island. Motivating Study. Physiological synchrony between romantic partners during nonverbal conditions. They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. 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?. How to compute?. How to interpret?. This week. 2. The relations between two variables. How the value of one variable changes when the value of another variable changes. A correlation coefficient is a numerical index to reflect the relationship between two variables.. Objective. : To look for relationships between two quantitative variables. Scatterplots. Scatterplots. . may be the most common and most effective display for data. . In a scatterplot, you can see patterns, trends, relationships, and even the occasional extraordinary value sitting apart from the others.. 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 . They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. Cross Correlation. A simple, yet powerful technique for detecting known patterns. Example applications include. Detecting preamble sequence in . WiFi. packets. Detecting cell tower ID based on known sequences. They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. Absolute vs. Relative . Age. Relative Age. Determining the age of an object in relation to other objects. “Estimate”. Index Fossils, Rock layers. Example. : A friend tells you . he . has 2 brothers – one that’s older than .