Chapter 21 Correlation. Correlation A measure of the strength of a linear relationship Although there are at least 6 methods for measuring correlation,

Slides:



Advertisements
Similar presentations
Correlation, Reliability and Regression Chapter 7.
Advertisements

Statistics Measures of Regression and Prediction Intervals.
Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.
Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.
Correlation & Regression Chapter 15. Correlation statistical technique that is used to measure and describe a relationship between two variables (X and.
Correlation Correlation is the relationship between two quantitative variables. Correlation coefficient (r) measures the strength of the linear relationship.
Chapter 4 Describing the Relation Between Two Variables
Section #1 Quiz 1 Stem and Leaf Plot (N=29) X|8 2| | |00 Mean=29.9; M=30; Mode= 29; s=6.38;
Correlation and Regression Analysis
BIVARIATE DATA: CORRELATION AND REGRESSION Two variables of interest: X, Y. GOAL: Quantify association between X and Y: correlation. Predict value of Y.
SIMPLE LINEAR REGRESSION
Chapter 3 Summarizing Descriptive Relationships ©.
Correlation A correlation exists between two variables when one of them is related to the other in some way. A scatterplot is a graph in which the paired.
Chapter Seven The Correlation Coefficient. Copyright © Houghton Mifflin Company. All rights reserved.Chapter More Statistical Notation Correlational.
S519: Evaluation of Information Systems Social Statistics Ch5: Correlation.
Lecture 17: Correlations – Describing Relationships Between Two Variables 2011, 11, 22.
Correlation and Regression. Relationships between variables Example: Suppose that you notice that the more you study for an exam, the better your score.
Statistics for the Behavioral Sciences (5th ed.) Gravetter & Wallnau
Chapter 4 Two-Variables Analysis 09/19-20/2013. Outline  Issue: How to identify the linear relationship between two variables?  Relationship: Scatter.
Correlation and Regression A BRIEF overview Correlation Coefficients l Continuous IV & DV l or dichotomous variables (code as 0-1) n mean interpreted.
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
STATISTICS ELEMENTARY C.M. Pascual
Correlation.
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
Chapter 15 Correlation and Regression
1 Chapter 9. Section 9-1 and 9-2. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION.
Learning Objective Chapter 14 Correlation and Regression Analysis CHAPTER fourteen Correlation and Regression Analysis Copyright © 2000 by John Wiley &
Introduction to Quantitative Data Analysis (continued) Reading on Quantitative Data Analysis: Baxter and Babbie, 2004, Chapter 12.
L 1 Chapter 12 Correlational Designs EDUC 640 Dr. William M. Bauer.
Stats/Methods I JEOPARDY. Jeopardy CorrelationRegressionZ-ScoresProbabilitySurprise $100 $200$200 $300 $500 $400 $300 $400 $300 $400 $500 $400.
Correlation is a statistical technique that describes the degree of relationship between two variables when you have bivariate data. A bivariate distribution.
Basic Statistics Correlation Var Relationships Associations.
Chapter 20 Linear Regression. What if… We believe that an important relation between two measures exists? For example, we ask 5 people about their salary.
© 2010 Pearson Prentice Hall. All rights reserved. CHAPTER 12 Statistics.
Figure 15-3 (p. 512) Examples of positive and negative relationships. (a) Beer sales are positively related to temperature. (b) Coffee sales are negatively.
Elementary Statistics Correlation and Regression.
Relationship between two variables Two quantitative variables: correlation and regression methods Two qualitative variables: contingency table methods.
Semester 2: Lecture 5 Quantitative Data Analysis: Bivariate Analysis 2 – Identifying Correlations using Parametric and Non-Parametric Tests Prepared by:
Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.
Describing Relationships Using Correlations. 2 More Statistical Notation Correlational analysis requires scores from two variables. X stands for the scores.
1 Chapter 10 Correlation. Positive and Negative Correlation 2.
Chapter Thirteen Copyright © 2006 John Wiley & Sons, Inc. Bivariate Correlation and Regression.
Chapter Bivariate Data (x,y) data pairs Plotted with Scatter plots x = explanatory variable; y = response Bivariate Normal Distribution – for.
Correlation. Correlation Analysis Correlations tell us to the degree that two variables are similar or associated with each other. It is a measure of.
Section 5.1: Correlation. Correlation Coefficient A quantitative assessment of the strength of a relationship between the x and y values in a set of (x,y)
9.1B – Computing the Correlation Coefficient by Hand
Chapter 3-Examining Relationships Scatterplots and Correlation Least-squares Regression.
Scatter Diagram of Bivariate Measurement Data. Bivariate Measurement Data Example of Bivariate Measurement:
Chapter Thirteen Bivariate Correlation and Regression Chapter Thirteen.
Psychology 202a Advanced Psychological Statistics October 22, 2015.
SOCW 671 #11 Correlation and Regression. Uses of Correlation To study the strength of a relationship To study the direction of a relationship Scattergrams.
Chapter 15: Correlation. Correlations: Measuring and Describing Relationships A correlation is a statistical method used to measure and describe the relationship.
Summarizing Data Graphical Methods. Histogram Stem-Leaf Diagram Grouped Freq Table Box-whisker Plot.
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Chapter 9 Scatter Plots and Data Analysis LESSON 1 SCATTER PLOTS AND ASSOCIATION.
GOAL: I CAN USE TECHNOLOGY TO COMPUTE AND INTERPRET THE CORRELATION COEFFICIENT OF A LINEAR FIT. (S-ID.8) Data Analysis Correlation Coefficient.
Chapter Correlation and Regression 1 of 84 9 © 2012 Pearson Education, Inc. All rights reserved.
Correlation & Linear Regression Using a TI-Nspire.
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
Chapter 2 Bivariate Data Scatterplots.   A scatterplot, which gives a visual display of the relationship between two variables.   In analysing the.
Spearman’s Rho Correlation
CHAPTER 10 & 13 Correlation and Regression
Scatterplots A way of displaying numeric data
Correlation and Regression
Ch. 11: Quantifying and Interpreting Relationships Among Variables
Correlation and Regression
Karl’s Pearson Correlation
Ch 4.1 & 4.2 Two dimensions concept
Correlation & Regression
Association Between Variables Measured At Ordinal Level
Presentation transcript:

Chapter 21 Correlation

Correlation A measure of the strength of a linear relationship Although there are at least 6 methods for measuring correlation, we are going to learn 2: –Pearson product-moment correlation coefficient (Pearson’s r) and –Spearman’s rho (r s )

Pearson’s r

Interpreting Pearson’s r r’s vary from -1 to = perfect positive linear relationship 0 = no linear relationship -1 = perfect negative linear relationship

Magnitude of the Relationship Absolute value of r: 0 < r <.25 Low correlation.25 < r <.50Moderate correlation.50 < r High correlation

Spearman’s Rank-Order Correlation “Pearson-on-the-Ranks” Rank each score with respect to the other scores of that variable Calculate the difference (D) between the ranks of each bivariate observation, or pair of scores Square the difference (D 2 )

Spearman’s Rank-Order Correlation Calculate r s using:

Review - Steps to Completing Regression (by hand) 1.Construct a data table (1 observation per row) 2. Compute each X i Y i, and ΣX i Y i 3.Compute n, ΣX i, ΣY i 4.Compute means (M X, M Y ) 5.Compute ΣX i 2, ΣY i 2, ((ΣX i ) 2, (ΣY i ) 2 ) 6.Compute the SS(X), SS(Y), and SP XY 7. Compute m (slope) and b (Y-intercept) 8. Find a point on the line: use a value of X on either end of the range, and compute the corresponding Y 9. Plot the point (M X, M Y ) and the point just found 10. Connect the points, label the line with the equation

Review - Steps to Computing a Pearson r 1.Construct a data table (1 observation per row) 2. Compute each X i Y i, and Σ X i Y i 3.Compute n, ΣX i, ΣY i 4.Compute means ( M X, M Y ) 5.Compute ΣX i 2, ΣY i 2, (ΣX i ) 2, (ΣY i ) 2 6.Compute the SS(X), SS(Y), SP XY 7. Compute

Review - Steps to Completing Spearman’s rho (r s ) 1.Rank each score with respect to the other scores of that variable (highest score gets highest rank of 1) 2.Calculate the difference (D i ) between the ranks of each bivariate observation, or pair of scores 3.Square the difference (D i 2 ) 4.Calculate

Interpreting Scatterplots

Correlation?

Not much

Other problems