Sample or Population:
My dataset is a sample of 349 individuals, it is not a population because it does not take into acount the entire population rather that it takes a sample of some of the individuals in the population.
One sample T hypothesis test:
μ : Mean of variable H_{0} : μ = 18 H_{A} : μ ≠ 18 Hypothesis test results:

TTest:
The variable used in the test was soda intake (%). The null hypothesis (Ho) was sample mean=18, while the alternative hypothesis (Ha) was the sample mean ≠ 18.
After performing a Ttest because my dataset is a sample, the Tstat is 0.59874625. The Pvalue is 0.5497. Since the Pvalue is larger than the significance level (0.05) we fail to reject the null hypothesis.
Contingency table results:
Rows: Gender Columns: Soda
ChiSquare test:

Contingency Table:
The variables included in the contingency tables are gender and soda intake (%). While the null hypothesis states that there is no relationship between gender and soda intake, the alternative hypothesis states that the two factors are dependant and maintain a relationship.
The statistic, ChiSquared, came out to 13.169089. The Pvalue is 0.0218. Since the Pvalue is less than the significance level (0.05), we can conclude to reject the null hypothesis. Therefore, it can be concluded that there is a relationship between soda intake (%) and gender.
Analysis of Variance results:
Data stored in separate columns. Column statistics
ANOVA table

One Way ANOVA:
The variables included in the One Way ANOVA are age and total soda intake (%). The null hypothesis is that the sample mean of age and soda intake are equal while the alternative hypothesis is that the samples means of both age and soda intake are different.
Using the ANOVA statistic test, it was found that the Fstat was 47.019679. The Pvalue is less than 0.0001, and since this is less than the significance level (.05), the null hypothesis will be rejected, indicating that the sample means are different.
One sample Z hypothesis test:
μ : Mean of variable H_{0} : μ = 18 H_{A} : μ ≠ 18 Standard deviation not specified. Hypothesis test results:

Extra Credit #1:
The variable used in the test was soda intake (%). The null hypothesis (Ho) was sample mean=18, while the alternative hypothesis (Ha) was the sample mean ≠ 18.
After performing a Ztest, my data was extremely similar when compared to the Ttest calculated with the same variable. While my tstat remained at 0.59874625, my Pvalue slightly changed from 0.5497 for the Ttest and 0.5493 for my Ztest.
Although numerically different, the Pvalue is still greater than the significance level (0.05), and therefore we fail to reject the null hypothesis .
Contingency table results:
Rows: Diet Columns: Age
ChiSquare test:
ChiSquare suspect. 
Extra Credit #2:
The variables included in the contingency tables are age and diet. While the null hypothesis states that there is no relationship between age and diet, the alternative hypothesis states that the two factors are dependant and maintain a relationship.
The statistic, chisquared, came out to 31.014509, in which the Pvalue is less than .0001. Since the Pvalue is less than the significance level (.05), you can conclude to reject the null hypothesis. Therefore, it can be concluded that there is a relationship between age and diet.
Analysis of Variance results:
Responses: Age Factors: Diet Response statistics by factor
ANOVA table

Extra Credit #3:
(since my data only had 2 quantitative and 2 qualitative variables, I used both my quantitative variables for the first ANOVA test, and will use the response of Age with the factor of Diet)
The variables included in the One Way ANOVA are age and diet. The null hypothesis is that the sample means of age and diet are equal to one another while the alternative hypothesis is that the samples means of both age and diet are different to one another.
Using the ANOVA statistic test, it was found that the Fstat was 28.952904. The Pvalue was less than .0001, and since this is less than the significance level (0.05), the null hypothesis is rejected, indicating that the sample means are different.
Extra Credit #4:
While my dataset contains enough individuals who participated in the sample to accuratley display my data and make accurate conclusions regarding the soda intake and the ages, diet, and gender of those who do or do not intake soda and how much (%) there are more things I would like to be informed about regarding my sample. For example in a few instances under the diet variable, some individuals refused to answer yes or no. I would like to know for what reasons and if them answering yes or no , if it would effect the results and conclusions in any way. I would also like to witness that if more individuals were sampled would the distribution of my data become a more normal distribution, all though I know this to be true on a thoretical scale, I would like to witness it through physical data and graphs regarding my data set.
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Dec 14, 2017
In hypothesis testing, we are testing the population mean. Sample mean is determined once we have the data.