1. My data set is a sample because these questions are applicable for the entire population of the world. However, this is just a sample of people in the world to understand basic information about them. If it were a population, the researcher would have to survey every person in thw world. This sample can be used to infer the results for a survey of the entire population.
One sample T hypothesis test:
μ : Mean of variable H_{0} : μ = 46 H_{A} : μ > 46 Hypothesis test results:

 variable: Age of participants
 null hypothesis: The mean equals 46
 alternative hypothesis:The mean is greater than 46
 t statistic = 0.64385753
 Conclusion: The t statistic of.0.64385753 is smaller than the critical value of t, 1.960.
Therefore, you fail to reject the null hypothesis that the sample mean is equal to 46.
2. Twoway table hypothesis test
Contingency table results:
Rows: Children Columns: Age
ChiSquare test:
ChiSquare suspect. 
 variables: Age of participants and Number of children that participants have.
 null hypothesis: Age and number of children are independent of one another.
 alternative hypothesis: Age and number of children are not independent of one another.
 Chi squared statistic = 90.110673
 Conclusion: The p value of .0003 is less than the significance level of .05. Therefore, you reject the null hypothesis that age and number of children are independent of one another.
3. Oneway ANOVA
Analysis of Variance results:
Data stored in separate columns. Column statistics
ANOVA table

 variables: Age, number of children, educational level, and income level.
 null hypothesis:The sample means of each set of data are all equal.
 alternative hypothesis: The sample means of each data set are different.
 F statistic = 794.81023
 Conclusion: The p value of less than .0001 is less than the significance level of .05. Therefore, you reject the null hypothesis that The sample means of each set of data are all equal.
Extra Credit:
1.
One sample Z hypothesis test:
μ : Mean of variable H_{0} : μ = 46 H_{A} : μ > 46 Standard deviation not specified. Hypothesis test results:

 variable: Age
 null hypothesis: the sample mean is equal to 46
 alternative hypothesis: The sample mean is greater than 46
 Z statistic = 0.64385753
 Conclusion: You would fail to reject the null hypothesis because the p value of 0.2598 is greater than the significance level of .05.
2.
Contingency table results:
Rows: Income Columns: Education
ChiSquare test:
ChiSquare suspect. 
 variables: Education level and income level
 null hypothesis: Education level and income level are independent of one another
 alternative hypothesis: Education level and income level are not independent of one another
 Chi squared statistic = 138.84036
 Conclusion: The p value of less than .0001 is less than the significance level of .05. Therefore, you reject the null hypothesis that education and income level are independent of one another.
3.
Analysis of Variance results:
Data stored in separate columns. Column statistics
ANOVA table

 variables: Age, number of children, and education level
 null hypothesis: The sample means of each set of data are all equal.
 alternative hypothesis:The sample means of each set of data are all different.
 F statistic = 978.64335
 Conclusion: The p value of less than .0001 is less than the significance level of .05. Therefore, you reject the null hypothesis that The sample means of each set of data are all equal.
4. I believe that the data set could be largely improved by giving people more options for their happiness level besides 1,2, or 3. I believe it would have been more beneficial to have surveyed this question with a scale of 110 or even a scale of 150. This would have made it much easier to conduct more statistical tests on. Also, if the data provided the numerical value of participants income rather than a level. If this was the case, I could have conducted more statistical tests in regards to the income of the participants. In addition, more graphs could have been used in several instances to better depict my data set.
Already a member? Sign in.
Dec 15, 2017
In hypothesis testing, we are testing the population mean.
Dec 14, 2017
No I am still working on it.
Dec 14, 2017
No I am still working on it.
Dec 14, 2017
not finished?