Textbook Notes (368,123)
Canada (161,661)
Marketing (884)
MKT 500 (49)
Chapter 13

Chapter 13 – Relationships between variables

3 Pages
107 Views
Unlock Document

Department
Marketing
Course
MKT 500
Professor
Helene Moore
Semester
Fall

Description
Wk. 9 – Chapter 13 – Relationships between variables Lecture on: November 6, 2012 What is a relationship between two variables? - Relationship – consistent and systematic linkage between the levels or labels for two variables Boolean relationships and cross-tabulation analysis - Boolean relationship – one in which the presence of one’s variable’s label is systematically related to the presence of another variable’s label - Characterizing a Boolean relationship with a graph: o Stacked bar graph – two variables are shown simultaneously on the same bar graph – each bar in the stacked bar chart stands for 100% and it is divided proportionately by the amount of relationship that one variable shares with the other variable - Cross-tabulation analysis – analytical technique that assesses the statistical significance of Boolean or categorical variable relationships o Frequencies table – contains the raw counts of the various Boolean relationships found in the complete data set o Chi-squared analysis – examination of frequencies for two categorical variables in the cross-tabulation table to determine whether the variables have a significant relationship o Observed frequencies – raw counts o Expected frequencies – if there was no significant relationship  Expected frequency = (Cell column total*Cell row total)/Grand total 2 o Chi-squared = Sum of (observed – ixpected) /eipected i o How to interpret a significant cross-tabulation finding:  Set up cross-tab table with observed frequencies  Calculate expected frequencies  Calculate chi-square value  Determine critical chi-square using (#rows – 1)*(#columns – 1) = degrees of freedom (using this number, find it in the critical value table)  Evaluate whether or not the null hypothesis of no relationship is supported – when the computed value is greater than the table value, reject null hypothesis because there is a relationship Liner relationships and correlation analysis - Y= a+bx - Correlation coefficient – index number falling between the range of -1.0 and +1.0 o Communicates the strength and the direction of the linear relationship between two metric variables - Covariation – defined as the amount of change in one varia
More Less

Related notes for MKT 500

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit