PPT-Linear Correlation

Statistics amp Risk Management 1 Copyright Texas Education Agency 2012 All rights reserved 2 Copyright Texas Education Agency 2012 All rights reserved  Copyright and Terms of Service. 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 . 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. 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 (. Week 1. Data Relationships. Finding a relationship between variables is what we’re looking for when extracting data from sample populations. . Is education better or worst now than before?. Do students learn better with the use of technology in the classroom?. 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.. 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. 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.. Hypothesis: We believe that some of the Eagles’ stats will be linear and have a positive correlation (meaning that the best-fitting line should be increasing, or the variable . r. should be as close to 1 as possible and go through as many points as possible).. 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.. x. y. 1. 2. 2. -3. 3. 8. 4. 9. 5. -21. Make a scatter plot from the table.. Ages of Famous Personalities. Oprah Winfrey. Beyonce. Knowles. George Clooney. Ellen DeGeneres. Leonardo . DiCaprio. Miley. 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 – . 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 .