1. 1 Analysis of variance (ANOVA) Week 11 lecture 1

2. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 2 Recap When do we need to do hypothesis testing? What’s the logic behind hypothesis testing? What is tested in hypothesis testing? How do you report the result of hypothesis testing? What is the level of significance? What is the most important statistics you should look at when making conclusion from hypothesis testing?

3. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 3 Parametric and nonparametric statistics Parametric statistics  Based on certain assumptions on the data The data are in an interval or ratio scale The data fall in a normal distribution Nonparametric statistics  No such assumptions are made

4. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 4 One-way ANOVA means statistically Assesses whether the means of two or more groups are statistically different from each other Dependent variable is interval or ratio scale Independent variable has two or more levels Approximately normal distribution of the measure in the two groups is assumed F-score  Between-sample variation/within-sample variation

5. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 5 One-way ANOVA in excel You want to compare the property sales prices in Sydney, NSW. Independent random samples of 10 properties were selected from each of 4 different neighborhoods (A,B,C,D) What’s the null hypothesis two-tailed Test gives two-tailed p-value

6. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 6 Two-way ANOVA Analyzing data from factorial design One interval/ratio dependent variable Two independent variables Access the main and interaction effect of independent variables

7. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 7 2 factor design without replication Example: cache comparison workloadTwo cachesOne cachesNo caches ASM54.055.0106.0 TECO60.0 123.0 SIEVE43.0 120.0 DHRYSTONE49.052.0111.0 SORT49.050.0108.0

8. Thursday, May 27, 2004 ISYS3015 Analytical methods for IS professionals School of IT, University of Sydney 8 2 factor design with replication Cache sizeMemory size 4M8M 1 K15, 18, 1245, 48,51 2K25, 28, 1975,75,81 Example: 2 2 3 factorial design The memory-cache experiments were repeated three times each. The result is shown below (from week 9 lecture 2)