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*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.

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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

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*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: 2.07962Standard Dev.: 1.45791

Min: 0Q1: 1

Median: 2Q3: 3

Max: 6

1 sample T Interval on the true average # of skittles

•Conditions

•Statement

•Formula

•Interval (a, b)

•Conclusion

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*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- 2.10526Standard Dev.- 1.6632Min- 0Q1- 1Median- 2Q3- 3Max- 6

Male:Mean- 2.07692Standard Dev.- 1.45791Min- 0Q1- 1Median- 2Q3- 3Max- 4

2 sample t test on male vs. female average skittles

•Conditions

•Statement

•Hypotheses: Ho: µF = µM Ha: µF ≠ µM

•Test statistic

•P-value

•Conclusion

•If reject, complete a confidence interval

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*Quantitative Data by Candy Consumption

SUMMARY STATS

NEEDED FOR EACH

VALUE OF THE VARIABLE

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*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)

X2 GOF test on type of Candy Consumption

NEVER SOMETIMESFREQOBS 10 28 30EXP (uniform)

•Conditions

•Statement

•Hypotheses

•Test Statistic

•P-Value

•Conclusion

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• 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%

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*Simple Two-Way Table of Categorical Data

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*Marginal Distribution for Gender

Male: 52.5 %Female: 47.5%

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*Marginal Distribution for Candy Preference

Chocolate: 50%

Sweet: 50%

*SEGMENTED BAR GRAPH

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List the % in each

category:

Of the females:C = 57.89%S = 42.11%

Of the males:C = 42.86%S = 57.14%

  C S

F 57.89% 42.11%

M 42.86% 57.14%

*X2 Test for Association

*Observed Table: Expected Table

*Conditions

*Statement

*Hypotheses

*Test Statistic

*P-value

*Conclusion15

(made in Excel & copied in)

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*Sources of Error and Bias

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*Conclusion

* Make a conclusion about each of the tests/intervals you did

* Make a conclusion about each graph you made

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EYEWEAR

Glasses/Contacts None total

GENDER M 12 18 30

F 18 23 41

total 30 41 71