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2 * Introduction & Thoughts Behind The Experiment - We wanted to chose an experiment that was easy to complete, organized, and some what entertaining. - Our original hypothesis, was that people who ate more Candy on a Regular Basis and had a stronger preference for Sweets rather than Chocolate would have more success and be able to correctly identify more skittles in one minute than those who do not.

3 The Task * 40 Subjects * Objective: * How many Skittles can the Subject identify in one minute based solely on their Taste Buds * Categorical Factors: * Gender * Male or Female * Candy Consumption * Frequent, Sometimes, Never * Candy Preference * Chocolate or Sweet * Hair Color

4 * Overall Quantitative Data The Data is right skewed and unimodal. The center is at the median of 2 skittles with an IQR of 2 skittles as well. The range was (0, 6) skittles. All data points are within the Upper & Lower Fence (UF=6) (LF=-2), thus there are no outliers. Mean: Standard Dev.: Min: 0 Q1: 1 Median: 2 Q3: 3 Max: 6

1 sample T Interval on the true average # of skittles Conditions Statement Formula Interval (a, b) Conclusion 5

6 * Quantitative Data by Gender The plots of our data broken down by gender shows that the both females and males had the same median at 2 skittles. The range for females was larger, which was (0,6) skittles, compared to the males which was (0,4) skittles. However they both had an IQR of 2 skittles. Finally, the males’ data appears to be more symmetrical, while the females’ data is clearly right skewed. Both are unimodal when we look at histograms of the data. Female: Mean Standard Dev Min- 0 Q1- 1 Median- 2 Q3- 3 Max- 6 Male: Mean Standard Dev Min- 0 Q1- 1 Median- 2 Q3- 3 Max- 4

2 sample t test on male vs. female average skittles Conditions Statement Hypotheses:Ho: µF = µMHa: µF ≠ µM Test statistic P-value Conclusion If reject, complete a confidence interval 7

88 * Quantitative Data by Candy Consumption SUMMARY STATS NEEDED FOR EACH VALUE OF THE VARIABLE

9 * Quantitative Data by Candy Consumption We chose to compare Candy Consumption to the data results to see if there was an association between people who ate Candy Frequently, Sometimes, or Never with the number of Skittles they correctly identified.  Our categorical variable that worked best with the data was if the subject ate candy on a regular basis (Frequently). The Frequently variable was more consistent overall than the Sometimes and Never variables  Frequently had a median at 3.5 skittles, which was the highest. Next was Sometimes with a median of 2 skittles, And never was the lowest with a median of ½ skittles.  The Sometimes variable had the largest range from (0,6) making it the most inconsistent, where as, the Never variable had a range from (0,1) and the Frequently variable had a range of (2, 5). The IQR of Frequently was …………  The shape of frequently was …….. (frequently, sometimes, or never)

X 2 GOF test on type of Candy Consumption NEVERSOMETIMESFREQ OBS EXP(uniform) Conditions Statement Hypotheses Test Statistic P-Value Conclusion 10 NOTE: If your categorical variable only has 2 values, instead of doing a Chi-Square GOF test, complete a 1 prop Z test to see if the % of one of the values is = 50%

11 * Simple Two-Way Table of Categorical Data

12 * Marginal Distribution for Gender Male: 52.5 % Female: 47.5%

13 * Marginal Distribution for Candy Preference Chocolate: 50% Sweet: 50%

* SEGMENTED BAR GRAPH 14 List the % in each category: Of the females: C = 57.89% S = 42.11% Of the males: C = 42.86% S = 57.14% CS F57.89%42.11% M42.86%57.14%

* X2 Test for Association * Observed Table:Expected Table * Conditions * Statement * Hypotheses * Test Statistic * P-value * Conclusion 15 (made in Excel & copied in)

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17 * Sources of Error and Bias

18 * Conclusion * Make a conclusion about each of the tests/intervals you did * Make a conclusion about each graph you made

* 19 EYEWEAR Glasses/ContactsNonetotal GENDERM F total304171