Applying hypothesis test data collected (Statistics)

In summary, the student is trying to apply a hypothesis test to determine if there is a correlation between the number of years spent at their college and the number of services used. They obtained a sample of 50 students and calculated the mean number of services used for the whole sample (1.510638) and for each year (1.4, 1.25, 1.6, 1.75, and 1.5). The null hypothesis is that there is no effect of years spent at the college on services used, while the alternative hypothesis is that there is an effect. The student plans to use the standard deviation and mean to calculate a z-score and determine if there is a significant correlation. They are struggling with applying
  • #1
Dusty912
149
1

Homework Statement


So I am doing a project for statistics and wanted to apply a hypothesis test to see if there is a correlation between the number of years spent at my college and the number of services used. The services include library, recreational services, clubs, etc.. i sent out a survey to get data from students and obtained a sample of 50 students with a mean of 1.510638 services used for the whole sample size. The average for first year students is 1.4. The average for second year students is 1.25, the average for 3rd year students is 1.6, the average for 4th year students is 1.75 and the average for 5th year students is 1.5.

SO my null hypothesis would be : number of years spent at my college would have no effect on number of services used

AND my alternative hypothesis would be that number of years spent at my school has an effect on number of services used.

I think that there will be no correlation. Just wondering how to set this up and execute it so I can prove statistically what is going on here. Any help would be appreciated!

Homework Equations


standard deviation (σ)
mean
z=(μ-mean)/(σ/sqrt(n))

The Attempt at a Solution


I need help picking which variables to use. Once I have all of the variables defined the analysis is straight forward. But applying this knowledge to real world data that i have collected is what I am struggling with.

would I be getting my standard deviation from the averages for each year?
 
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  • #2
Title should say "Applying Hypothesis Test TO data Collected (Statistics)"
 
  • #3
The standard deviation for the sample is 0.74810893259146
While the average for the whole sample size is 1.510638

The standard deviation for the averages of each year is 0.1903943276466
while the average of the averages is 1.5
 
  • #4
Dusty912 said:

Homework Statement


So I am doing a project for statistics and wanted to apply a hypothesis test to see if there is a correlation between the number of years spent at my college and the number of services used. The services include library, recreational services, clubs, etc.. i sent out a survey to get data from students and obtained a sample of 50 students with a mean of 1.510638 services used for the whole sample size. The average for first year students is 1.4. The average for second year students is 1.25, the average for 3rd year students is 1.6, the average for 4th year students is 1.75 and the average for 5th year students is 1.5.

SO my null hypothesis would be : number of years spent at my college would have no effect on number of services used

AND my alternative hypothesis would be that number of years spent at my school has an effect on number of services used.

I think that there will be no correlation. Just wondering how to set this up and execute it so I can prove statistically what is going on here. Any help would be appreciated!

Homework Equations


standard deviation (σ)
mean
z=(μ-mean)/(σ/sqrt(n))

The Attempt at a Solution


I need help picking which variables to use. Once I have all of the variables defined the analysis is straight forward. But applying this knowledge to real world data that i have collected is what I am struggling with.

would I be getting my standard deviation from the averages for each year?

This looks like a standard problem in ANOVA---Google it.
 

1. What is the purpose of applying hypothesis testing to collected data?

The purpose of hypothesis testing is to determine whether or not there is a significant difference between two or more groups or variables in a given population. It helps to determine if the results observed in the collected data are due to chance or if they can be generalized to the larger population.

2. What are the steps involved in applying hypothesis testing?

The steps involved in applying hypothesis testing include:
1. Formulating a null hypothesis and an alternative hypothesis
2. Choosing an appropriate test statistic and determining the level of significance
3. Collecting and organizing the data
4. Calculating the test statistic and determining the p-value
5. Comparing the p-value to the level of significance
6. Making a decision to reject or fail to reject the null hypothesis based on the p-value and level of significance
7. Interpreting the results and drawing conclusions.

3. What are the different types of hypothesis tests that can be used?

There are several types of hypothesis tests that can be used, including:
1. One-sample t-test: used to compare the mean of a sample to a specific value
2. Two-sample t-test: used to compare the means of two independent samples
3. ANOVA (Analysis of Variance): used to compare the means of three or more independent samples
4. Chi-square test: used to determine if there is a significant association between two categorical variables
5. Regression analysis: used to determine the relationship between two or more variables
6. Correlation analysis: used to measure the strength and direction of the relationship between two continuous variables.

4. How do you interpret the results of a hypothesis test?

The results of a hypothesis test are typically interpreted by comparing the p-value to the chosen level of significance (usually 0.05 or 0.01). If the p-value is less than or equal to the level of significance, the results are considered statistically significant and the null hypothesis is rejected. If the p-value is greater than the level of significance, the results are not considered statistically significant and the null hypothesis cannot be rejected. It is important to also consider the effect size and practical significance when interpreting the results.

5. What are some common mistakes to avoid when applying hypothesis testing?

Some common mistakes to avoid when applying hypothesis testing include:
1. Using the wrong test for the type of data or research question
2. Failing to clearly define the null and alternative hypotheses
3. Using a sample that is not representative of the population
4. Not considering the assumptions and limitations of the chosen test
5. Misinterpreting the results without considering the effect size and practical significance
6. Drawing conclusions that are not supported by the data.

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