chapter4 - Chapter 4 Describing the Relation between Two...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
189 Chapter 4 Describing the Relation between Two Variables 4.1 Scatter Diagrams and Correlation 1. Univariate data measures the value of a single variable for each individual in the study. Bivariate data measures values of two variables for each individual. 2. A lurking variable is a variable that is related either to the response variable or to the explanatory variable, or both, but is not measured in the study. Examples will vary. One possibility: The number of firemen responding to a fire can be used to predict the amount of damage done. Both variables are related to the seriousness of the fire. 3. Two variables are positively associated if increases in the value of the explanatory variable tend to correspond to increases in the value of the response variable. 4. If 1 r = , there is a perfect positive linear relation between the variables. And the points of the scatter diagram will lie along a straight line with positive slope. 5. Since r measures only the strength and direction of linear relationships , obtaining 0 r = only means that there is no linear relation between the explanatory and response variable. 6. No, r is not a resistant measure. This is made apparent by considering the formula for the sample correlation coefficient. From the formula, we see that the value of r depends on the mean and standard deviation, both of which are not resistant. Therefore, r will also be sensitive to extreme values or outliers. Supporting examples will vary. 7. The linear correlation coefficient can only be calculated from bivariate quantitative data. The gender of a driver is a qualitative variable. 8. The correlation coefficient is a numerical measure of the strength and direction of the linear association between two quantitative variables, which we traditionally designate as x and y . It is calculated as a sum of products of the z -scores of the x - and y - components of each data point, that is 1 1 ii xy x y r ns s ⎛⎞ −− = ⎜⎟ ⎝⎠ . The correlation coefficient takes values between 1 and 1. For a positive linear relation, above average values of one variable (or positive z -scores) tend to be associated with above average values of the other variable (positive z -scores) and below average values of one (negative z -scores) with below average values of the other (negative z -scores), and so the products will mostly be positive, giving a positive value of r . Similarly, a negative value of r indicates a negative relation where above average values of one (positive z -scores) tend to be associated with below average values of the other (negative z -scores). The strength of the linear relation, either positive or negative, is measured by how close the value of r is to 1 or 1 . 9. This statement makes no sense because the linear correlation coefficient is unitless; it cannot be 0.93 bushel .
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Chapter 4 Describing the Relation between Two Variables 190 10. Correlation is simply a measure of association, it does not attempt to explain the association. A change in the explanatory variable is associated with a change in the response variable. Causation, on the other hand, means that a change in the explanatory
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 11/21/2011 for the course NGN 111 taught by Professor Ahmad during the Spring '11 term at American Dubai.

Page1 / 76

chapter4 - Chapter 4 Describing the Relation between Two...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online