1. Kano Model & Multivariate Statistics Dr. Surej P John

2. Example: Requirements Survey

3. Functional vs. Dysfunctional Comparison

4. Basic Attribute

5. Functional vs. Dysfunctional Comparison Performance Attribute

6. Functional vs. Dysfunctional Comparison Exciting Attribute

7. Evaluation Customer Requirements C.R.AEORITotalGrade 1361423O 25611123O 361411123I 4131023E 51112923A Customer Requirement is: A: Attractive R: Reverse E: Expected O: One Dimensional I: Indifferent

8. Multivariate Techniques: Statistical tool depends on the data type

9. Chi-Square Test Widely used for the analysis of nominal/ordinal scale data (categorical data) H o : The column variable is independent of the row variable H a : The column variable is not independent of the row variable

10. The  2 test:  2 =  (observed freq. - expected freq.) 2 / expected freq. Obtain a sample of nominal scale data and to infer if the population from which it came conforms to a certain theoretical distribution. Used to test Ho that the observations (not the variables) are independent of each other for the population. Based on the difference between the actual observed frequencies (not %) and the expected frequencies that would be obtained if the variables were truly independent.

11. The  2 test:  2 =  (observed freq. - expected freq.) 2 / expected freq. Used as a measure of how far a sample distribution deviates from a theoretical distribution H o : no difference between the observed and expected frequency (H a : they are different) If H o is true then both the difference and chi- square value will be SMALL If Ho is false then both measurements will be Large, H a will be accepted

12. Example In a questionnaire, 259 adults were asked what they thought about cutting air pollution by increasing tax on vehicle fuel. Ans: 113 people agreed with this idea but the rest disagreed. Test: Perform a Chi-square text to determine the probability of the results being obtained by chance.

13. Cross Tabulation or Contingency Tables: Further examination of the data on the opinion on increasing fuel to cut down air pollution (example 1): H o : the decision is independent of sex Males Females Agree 13 (a) 100 (b) Disagree 116 (c) 30 (d)

14. Therefore, reject Ho and accept H A that the decision is dependent of sex.

15. Linear Regression Analysis A simple mathematical expression to provide an estimate of one variable from another It is possible to predict the likely outcome of events given sufficient quantitative knowledge of the processes involved

16. System Quality Service Quality Trust Information Quality Customer satisfaction

17. Results

18. Multi collinearity in regression Multicollinearity exits if there is a high correlation between any two independent variables. Multicollinearity makes a significant variable insignificant by increasing its standard error. If the standard error goes up, the p value will increase more than 0.05 and we interpret the particular variable is insignificant, but it is not in reality.

19. Example- Multi collinearity