© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey 07458 All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.

Slides:



Advertisements
Similar presentations
Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.
Advertisements

Prepared by Lloyd R. Jaisingh
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 2.1 Chapter 2.
DIVIDING INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
Overview of Lecture Parametric vs Non-Parametric Statistical Tests.
Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION
1 Contact details Colin Gray Room S16 (occasionally) address: Telephone: (27) 2233 Dont hesitate to get in touch.
1 Correlation and Simple Regression. 2 Introduction Interested in the relationships between variables. What will happen to one variable if another is.
Chapter 7 Sampling and Sampling Distributions
Chapter 4: Basic Estimation Techniques
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
9: Examining Relationships in Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush.
Psychology Practical (Year 2) PS2001 Correlation and other topics.
Chapter 18: The Chi-Square Statistic
Experimental Design and Analysis of Variance
1 Chapter 20: Statistical Tests for Ordinal Data.
Simple Linear Regression Analysis
Copyright © 2012 by Nelson Education Limited. Chapter 13 Association Between Variables Measured at the Interval-Ratio Level 13-1.
Correlation and Linear Regression
Multiple Regression and Model Building
Chapter 16: Correlation.
Describing Relationships Using Correlation and Regression
Correlation Chapter 9.
Chapter18 Determining and Interpreting Associations Among Variables.
Linear Regression and Correlation
SIMPLE LINEAR REGRESSION
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 6: Correlation.
Regression Chapter 10 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.
Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey All rights reserved. John W. Creswell Educational Research: Planning,
SIMPLE LINEAR REGRESSION
Chapter 9 For Explaining Psychological Statistics, 4th ed. by B. Cohen 1 What is a Perfect Positive Linear Correlation? –It occurs when everyone has the.
Correlation and Linear Regression
© 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.
SIMPLE LINEAR REGRESSION
Linear Regression and Correlation
CPE 619 Simple Linear Regression Models Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama.
Correlation.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Chapter 15 Correlation and Regression
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved Section 10-1 Review and Preview.
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved Lecture Slides Elementary Statistics Eleventh Edition and the Triola.
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
Hypothesis of Association: Correlation
Investigating the Relationship between Scores
Examining Relationships in Quantitative Research
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
Chapter 16 Data Analysis: Testing for Associations.
1 Chapter 10 Correlation. Positive and Negative Correlation 2.
Chapter 4 Summary Scatter diagrams of data pairs (x, y) are useful in helping us determine visually if there is any relation between x and y values and,
Correlation. Correlation is a measure of the strength of the relation between two or more variables. Any correlation coefficient has two parts – Valence:
Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.
Chapter 7 Calculation of Pearson Coefficient of Correlation, r and testing its significance.
1 Chapter 10 Correlation. 2  Finding that a relationship exists does not indicate much about the degree of association, or correlation, between two variables.
Correlation. u Definition u Formula Positive Correlation r =
Applied Quantitative Analysis and Practices LECTURE#10 By Dr. Osman Sadiq Paracha.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Lecture Slides Elementary Statistics Tenth Edition and the.
© 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.
Pearson’s Correlation The Pearson correlation coefficient is the most widely used for summarizing the relation ship between two variables that have a straight.
Determining and Interpreting Associations between Variables Cross-Tabs Chi-Square Correlation.
©2013, The McGraw-Hill Companies, Inc. All Rights Reserved Chapter 3 Investigating the Relationship of Scores.
CHS 221 Biostatistics Dr. wajed Hatamleh
Elementary Statistics
Making Use of Associations Tests
Warsaw Summer School 2017, OSU Study Abroad Program
Presentation transcript:

© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul Cruz-Cano, Ph.D. 5/5/2014, Spring 2014 Fox/Levin/Forde, Elementary Statistics in Social Research, 12e Chapter 10: Correlation 1

Final Exam Monday 5/19/2014 Time and Place of the class Chapters 9, 10 and 11 Same format as past two exams No re-submission of homework Summer SAS Course 2

Differentiate between the strength and direction of a correlation Learning Objectives After this lecture, you should be able to complete the following Learning Outcomes 10.1

4 Until now, we’ve examined the presence or absence of a relationship between two or more variables What about the strength and direction of this relationship? We refer to this as the correlation between variables Strength of Correlation This can be visualized using a scatter plot –Strength increases as the points more closely form an imaginary diagonal line across the center Direction of Correlation Correlations can be described as either positive or negative –Positive – both variables move in the same direction –Negative – the variables move in opposite directions Correlation

10.1 Figure 10.1

10.1 Figure 10.2

Identify a curvilinear correlation Learning Objectives After this lecture, you should be able to complete the following Learning Outcomes 10.2

8 A relationship between X and Y that begins as positive and becomes negative, or begins as negative and becomes positive Curvilinear Correlation

Figure 10.3 A non-linear transformation, e.g. square root, might take care of this

Discuss the characteristics of correlation coefficients Learning Objectives After this lecture, you should be able to complete the following Learning Outcomes 10.3

The Correlation Coefficient 10.3 DirectionStrength 11 The sign (either – or +) indicates the direction of the relationship Values close to zero indicate little or no correlation Values closer to -1 or +1, indicate stronger correlations Numerically expresses both the direction and strength of a relationship between two variables Ranges between -1.0 and + 1.0

Calculate and test the significance of Pearson’s correlation coefficient ( r ) Learning Objectives After this lecture, you should be able to complete the following Learning Outcomes 10.4

13 Focuses on the product of the X and Y deviations from their respective means –Deviations Formula: –Computational Formula: Pearson’s Correlation Coefficient (r)

The null hypothesis states that no correlation exists in the population (ρ = 0) To test the significance of r, a t ratio with degrees of freedom N – 2 must be calculated A simplified method for testing the significance of r Compare the calculated r to a critical value found in Table H in Appendix C Testing the Significance of Pearson’s r

15 Exercises Problem 6, 19, 21

Requirements for the Use of Pearson’s r Correlation Coefficient 10.4 A Straight-Line Relationship Interval Data Random Sampling Normally Distributed Characteristics

Calculate the partial correlation coefficient Learning Objectives After this lecture, you should be able to complete the following Learning Outcomes 10.5

18 The correlation between two variables, X and Y, after removing the common effects of a third variable, Z When testing the significance of a partial correlation, a slightly different t formula is used Partial Correlation

19 Exercise Problem 30

20 Homework Problems 18, 22 and 31 Add interpretation

© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved Correlation allows researchers to determine the strength and direction of the relationship between two or more variables In a curvilinear correlation, the relationship between two variables starts out positive and turns negative, or vice versa The correlation coefficient numerically expresses the direction and strength of a linear relationship between two variables Pearson’s correlation coefficient can be calculated for two interval-level variables The partial correlation coefficient can be used to examine the relationship between two variables, after removing the common effect of a third variable CHAPTER SUMMARY