1. Why is a correlation the most appropriate statistic?
When it comes to the aim of this study between student anxiety and the hours studied it shows that these two have a nature and degree between the two variables. When it comes to being interested with the two it also shows that students having anxiety for taking an exam to the number of hours the student have to study for the exam I would say that correlation shows the statistic of the two.
2. What is the null and alternate hypothesis?
Null Hypothesis- Student anxiety for an exam does not have a significant correlation with the number of hours studied.
Alternate Hypothesis- Student anxiety for an exam has a significant correlation with the number of hours studied.
3. What is the correlation between student anxiety scores and number of study hours? Select alpha and interpret your findings. Make sure to note whether it is significant or not and what the effect size is.
The correlation between two variables X and Y is giving below in a formula
Decision Rule: Reject H0 if
|Study hours (X)||Student Anxiety Scores (Y)||()^2||()^2||(X – X bar)(Y – Y bar)|
We shall use significance level, ( = 0.05 to test the given hypothesis.
Sample Size, n = 10, therefore, degrees of freedom of t statistic = (n-2) = (10 – 2) = 8
Critical value of(from t-distribution table)
When it comes to the null hypothesis, the correlation shows that the student anxiety due to a exam and the hours for the students to study is not statistically significant. So therefore the effect size within the correlation is r=0.565
4. How would you interpret this?
When it comes to this study, I would say that there would be no evidence between the student anxiety within the exam and the number of hours the students would have to study for the exam.
5. What is the probability of a type I error? What does this mean?
When it comes to the probability it shows that it is 0.0885 of type I error, it shows rejecting the true null hypothesis. When it comes to the student anxiety and the hours for study the probability shows that there is relationship between them both. But from my study there is no significant relationship it still shows 0.0885
How would you use this same information but set it up in a way that allows you to conduct a t-test? An ANOVA?
When it comes to doing a conduct a t-test, dividing the number of students into two groups. When it comes to the two groups and the one that shows the lower number of study hours and the other group that has the higher study hours. Then when it comes to the two groups we will compute the between the two and see if the mean anxiety score is different with the groups. When it comes to ANOVA, the same test should be dome as well, but more than two groups will show example of the mean anxiety score.
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