Hlth 300 biostatistics for public health practice raul cruz cano ph d
Download
1 / 21

HLTH 300 Biostatistics for Public Health Practice, Raul Cruz-Cano, Ph.D. - PowerPoint PPT Presentation


  • 119 Views
  • Uploaded on
  • Presentation posted in: General

Fox/Levin/Forde, Elementary Statistics in Social Research, 12e. Chapter 10: Correlation. HLTH 300 Biostatistics for Public Health Practice, Raul Cruz-Cano, Ph.D. 5/5/2014 , Spring 2014. Final Exam. Monday 5/19/2014 Time and Place of the class Chapters 9, 10 and 11

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha

Download Presentation

HLTH 300 Biostatistics for Public Health Practice, Raul Cruz-Cano, Ph.D.

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Hlth 300 biostatistics for public health practice raul cruz cano ph d

Fox/Levin/Forde, Elementary Statistics in Social Research, 12e

  • Chapter 10: Correlation

HLTH 300 Biostatistics for Public Health Practice,Raul Cruz-Cano, Ph.D.

5/5/2014, Spring 2014


Final exam
Final Exam 12e

  • 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


Differentiate between the strength and direction of a correlation

  • Learning Objectives 12e

    • After this lecture, you should be able to complete the following Learning Outcomes

  • 10.1

Differentiate between the strengthand direction of a correlation


Correlation

10.1 12e

Correlation

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


Hlth 300 biostatistics for public health practice raul cruz cano ph d

Figure 10.1


Hlth 300 biostatistics for public health practice raul cruz cano ph d

Figure 10.2


Identify a curvilinear correlation

  • Learning Objectives 12e

    • After this lecture, you should be able to complete the following Learning Outcomes

  • 10.2

Identify a curvilinear correlation


Curvilinear correlation

10.2 12e

Curvilinear Correlation

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



Discuss the characteristics of correlation coefficients

  • Learning Objectives care of this

    • After this lecture, you should be able to complete the following Learning Outcomes

  • 10.3

Discuss the characteristics of correlation coefficients


The correlation coefficient

10.3 care of this

The Correlation Coefficient

Numerically expresses both the direction and strength of a relationship between two variables

  • Ranges between -1.0 and + 1.0

Direction

  • Strength

  • 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


Calculate and test the significance of pearson s correlation coefficient r

  • Learning Objectives care of this

    • After this lecture, you should be able to complete the following Learning Outcomes

  • 10.4

Calculate and test the significance of Pearson’s correlation coefficient (r)


Pearson s correlation coefficient r

10.4 care of this

Pearson’s Correlation Coefficient (r)

Focuses on the product of the X and Y deviations from their respective means

  • Deviations Formula:

  • Computational Formula:


Testing the significance of pearson s r

10.4 care of this

Testing the Significance of Pearson’s r

The null hypothesis states that no correlation exists in the population (ρ = 0)

  • To test the significance of r, at 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


Exercises
Exercises care of this

Problem 6, 19, 21


Requirements for the use of pearson s r correlation coefficient

10 care of this.4

Requirements for the Use of Pearson’s r Correlation Coefficient

  • A Straight-Line Relationship

  • Interval Data

  • Random Sampling

  • Normally Distributed Characteristics


Calculate the partial correlation coefficient

  • Learning Objectives care of this

    • After this lecture, you should be able to complete the following Learning Outcomes

  • 10.5

Calculate the partial correlation coefficient


Partial correlation

10.5 care of this

Partial Correlation

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


Exercise
Exercise care of this

Problem 30


Homework
Homework care of this

Problems 18, 22 and 31

Add interpretation


Hlth 300 biostatistics for public health practice raul cruz cano ph d

CHAPTER SUMMARY care of this

  • Correlation allows researchers to determine the strength and direction of the relationship between two or more variables

10.1

  • In a curvilinear correlation, the relationship between two variables starts out positive and turns negative, or vice versa

10.2

  • The correlation coefficient numerically expresses the direction and strength of a linear relationship between two variables

10.3

  • Pearson’s correlation coefficient can be calculated for two interval-level variables

10.4

  • The partial correlation coefficient can be used to examine the relationship between two variables, after removing the common effect of a third variable

10.5


ad
  • Login