1. Lab 1 Sample calculation of statistics Use Microsoft Excel Program Save data from either Food Chemistry or Experimental Foods go to Food Chemistry or Experimental Foods Lab Data pagesFood ChemistryExperimental Foods follow the save instructions at the top for the data you wish to save and then run save data for your day on your disk Run/Open data in Microsoft Excel

2. In Microsoft Excel, Select ‘Tools’ and then ‘Data Analysis’ Note: If you do not have ‘Data Analysis’, you may have to load this through ‘Add-Ins’ - Select ‘Add-Ins’ then select ‘Analysis Toolpak’

3. From ‘Data Analysis’ Select ‘ANOVA: Two-Factor Without Replication’

4. Fill in the information for ‘Input Range’, and ‘Output Range’ - Remember if you select your data with labels, the labels box must be checked.

5. Highlight the Data Make sure Highlighted Data is in the Input Range Box

6. Make sure if Labels from the data were Highlighted that the Labels box is checked - Alpha should always be 0.05.

7. Now you are ready to select the Output Range - This is where the ANOVA table will be generated

8. Make sure the cursor is in the Output data box - If it is not then you will loose the information in the Input Range box

9. Select the cell Make sure the cell also gets registered in the Output Range box - If nothing is in this box an error will occur.

10. The ANOVA begins at the cell selected for the Output Range

11. First, look at the P-value - If it is greater than 0.05 it is not significant - if it is less than 0.05 it is significant

12. Focus in on columns (treatments) P-values because these represent whether treatments were significant - In this case, columns are significantly different, thus treatments are different. What next?

13. Multiple Comparison or Least Significant Difference (lsd) place treatment means (averages) from high to low TreatmentsAverage Sample 693 7.3 Sample 054 7.3 Sample 893 4.6 Now you must calculate the lsd to determine which mean or means are significantly different from each other Averages for the 3 treatments Where:t is the critical t value from the t-table with df as the error df from ANOVA table EMS is error mean square from ANOVA table n is the number of samples for each mean (i.e. # panelists)

14. EMS df for t-table n

15. THE lsd CALCULATION You need to use the t-table for helping do this calculation. In the table, you use the 0.05 for 2-tails probabilityt-table For 12 df from df for EMS you get a t value of 2.179 EMS from ANOVA chart is 1.5 the value for n is 7 from the ANOVA chart. THUS:

16. TreatmentsAverage Sample 693 7.3b Sample 054 7.3b Sample 893 4.6a Now to determine which treatments are significantly different you subtract each treatment from all the others. Thus, 7.3-4.6 = 2.7; 7.3-4.6 = 2.7; 7.3-7.3 = 0. This difference if less than 1.43 is not significantly different, however, if greater than 1.43 it is significantly different. You then assign letters or lines demonstrating significant differences. Thus: You conclude that Sample 893 is significantly different from Samples 693 and 054, but that the latter Samples (693 and 054) are not different from each other