PPT-Correlation & Regression

Chapter 10 Outline Section 101 Introduction Section 102 Scatter Plots Section 103 Correlation Section 104 Regression Section 105 Coefficient of Determination and Standard Error of the Estimate. 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 . 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. 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 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.. Due 10/30/15. 42 Points. A regression line is a ________ line that describes how a __________ variable y changes as an ____________ variable x changes. You can use a regression line to predict the value of y for any value of x by substituting this x into the equation of the line.. 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. 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. . Andrea . Banino. & Punit . Shah . Samples . vs. Populations . Descriptive . vs. Inferential. William Sealy . Gosset. (‘Student’). Distributions, probabilities and P-values. Assumptions of t-tests. 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 . Correlation. A statistical way to measure the relationship between two sets of data.. Means that both things are observed at the same time.. Causation. Means that one thing will cause the other.. You can have correlation without causation. -2-Note that-1 -2-Note that-1r1otal Sum of Squares TSS of the data set asTSSni1Syi-y2note thatTSSSyygression SSR asRSSni1Syi-y2and note that sinceyiwill not be exactly on the regression lineTSSx0000RSSunless the poi 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..