Chapter 4 l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 14

CHAPTER 4 PowerPoint PPT Presentation


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

CHAPTER 4. CORRELATION. 4.2 How to measure relationships 4.2.1 Covariance Adverts Watched and Packets bought. Variance = ∑(x i – x) 2 N - 1. Cov (x,y) = ∑(x i – x) (yi – y) N - 1. Cross product deviation = 4.25 for this example.

Download Presentation

CHAPTER 4

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


Chapter 4 l.jpg

CHAPTER 4

CORRELATION


4 2 how to measure relationships 4 2 1 covariance adverts watched and packets bought l.jpg

4.2 How to measure relationships4.2.1 Covariance Adverts Watched and Packets bought

Variance = ∑(xi – x)2

N - 1

Cov (x,y) = ∑(xi – x) (yi – y)

N - 1

Cross product deviation

= 4.25 for this example

Figure 4.1 Graphical display of the difference between observed data and means of the two variables


4 2 2 standardisation and the correlation coefficient l.jpg

To overcome the problem of dependence on measurement scale, we convert covariance in to standard set of units.

By standardising we end up with a value that lies between -1 and +1.

+1 means variables are perfectly positively related.

This correlation coefficient can be called as the

Pearson product moment correlation coeffiecient or Pearson correlation coefficient

4.2.2. Standardisation and the correlation coefficient

r = Cov (x,y) = ∑(xi – x) (yi – y)

sx sy (N – 1) sx sy


4 4 graphing relationships the scatter plot 4 4 1 simple scatter plot load the file examanxiety sav l.jpg

4.4 Graphing Relationships: The scatter Plot4.4.1 Simple Scatter Plot : Load the file ExamAnxiety.sav

SPSS

Graphs-Interactive-Scatterplot

Figure 4.5


4 4 2 3d scatter plot l.jpg

4.4.2 3D Scatter plot

Figure 4.6 – 3D Scatter Plot


4 4 3 overlay scatter plot l.jpg

4.4.3 Overlay Scatter plot

SPSS

Graphs-Scatter

Figure 4.9


4 4 4 matrix scatter plot l.jpg

4.4.4 Matrix Scatter Plot

Figure 4.11


4 5 bivariate correlation l.jpg

After a prelimnary glance at the data, we can conduct the correlation analysis.

Access the File Advert.sav

SPSS Analyze –Correlate-Bivariate

4.5.1 Pearsons Correlation Coefficient

Reload the file ExamAnxiety.sav

4.5.2 A word of warning about interpretation: Causality

The third variable problem (There may be other measured or unmeasured variables affecting the results)

Direction of causality (The correlation coefficients say nothing about which variable causes the other to change)

4.5 Bivariate Correlation


4 5 3 using r 2 for interpretation l.jpg

We can go a step further by squaring r

The correlation coefficient squared (coefficient of determination) is a measure of the amount of variability in one variable that is explained by the other.

R2 = 19.4

Exam anxiety accounts for 19.4% of the variability in exam performance.

4.5.3 Using R2 for Interpretation


Slide10 l.jpg

4.5.4 Spearman´s correlation coefficient

4.5.5 Kendall´ tau (non parametric)


Partial correlation l.jpg

Partial Correlation

X2

X1

The shaded area of X1 as a portion of the area Y represents the partial correlation of X1 with Y given X2. This shaded area as a proportion of Y, denotes the incremental variance explained by X1, given that X2 is already in the equation.

Y

Part Correlation

X2

The unique predictive effect of the due to a single independent variable among a set of independent variables.

Y


Slide12 l.jpg

X1

X2

c

a = Variance of Y uniquely explained by X1

b = Variance of Y uniquely explained by X2

c = Variance of Y explained jointly by X1and X2

d = Variance of Y not explained by X1 or X2

a

b

d

Y


4 6 partial correlation l.jpg

4.6 Partial Correlation

Figure 4.17


4 6 3 semi partial correlation l.jpg

4.6.3 Semi Partial Correlation

Figure 4.20


  • Login