Week six hypothesis testing I and II; Testing differences between means, variances, and proportions

A town official claims that the average vehicle in their area sells for more than the 40th percentile of your data set.
Using the data, you obtained in week 1, as well as the summary statistics you found for the original data set (excluding the super car outlier), run a hypothesis test to determine if the claim can be supported. 
Make sure you state all the important values, so your fellow classmates can use them to run a hypothesis test as well. 
Use the descriptive statistics you found during Week 2 — NOT the new SD you found during Week 4. 
Because again, we are using the original 10 sample data set NOT a new smaller sample size. 
Use alpha = .05 to test your claim.

(Note: You will want to use the function =PERCENTILE.INC  in Excel to find the 40th percentile of your data set. 
Hopefully, this Excel function looks familiar to you from Week 2.)

First, determine if you are using a z or t-test and explain why.
Then conduct a four-step hypothesis test including a sentence at the end justifying the support or lack of support for the claim and why you made that choice.

I encourage you to review the Week 6 Hypothesis Testing PDF at the bottom of the discussion. 
This will give you a step-by-step example of how to calculate and run a hypothesis test using Excel.
I DO NOT recommend doing this by hand. 
Let Excel do the heavy lifting for you.  
You can also use this PDF in the Quizzes section.

There were 5 additional PDFs that were created to help you with the Homework, Lessons, and Tests in Quizzes section. 
While they won’t be used to answer the questions in the discussion, they are just as useful and beneficial. 
I encourage you to review these ASAP! 
These PDFs are also located at the bottom of the discussion.

Once you have posted your initial discussion, you must reply to at least two other learner’s post.
Each post must be a different topic.
So, you will have your initial post from one topic, your first follow-up post from a different topic, and your second follow-up post from one of the other topics.
Of course, you are more than welcome to respond to more than two learners.

Instructions: Make sure you include your data set in your initial post as well. 
You must also respond to at least 2 other students. 

In your first peer response post, look at the hypothesis test results of one of your classmates and explain what a type 1 error would mean in a practical sense.
Looking at your classmate’s outcome, is a type 1 error likely or not?
What specific values indicated this?

In your second peer response post, using your classmate’s values, run another hypothesis test using this scenario: 
A town official claims that the average vehicle in their area Does Not sell for the 80th percentile of your data set. 
Conduct a four-step hypothesis test including a sentence at the end justifying the support or lack of support for the claim and why you made that choice. 
Note: this test will be different than the initial post, starting with the hypothesis scenario.
Use alpha = .05 to test your claim.

Week 6 Hypothesis Testing .pdf

Week 6 Hypothesis Testing Proportions 1-sample.pdf

Week 6 2-sample Hypothesis Testing and CI Proportions.pdf

Week 6 2-sample Hypothesis Testing and CI Unknown SD.pdf

Week 6 2-sample Hypothesis Testing and CI Known S.pdf

Week 6 2-sample Hypothesis Testing and CI matched or paired.pdf

My course tutor provides sample solutions to the discussion questions.

1 comment

  1. Hypothesis testing is a statistical technique in which a choice is made between a null hypothesis and an alternative hypothesis based on information in a sample.

    A hypothesis test assesses two mutually exclusive statements that give a population to define which statement might be best reinforced by the sample data. When we can conclude that a result is statistically significant, then our hypothesis test is the cause.

    The representation of any random sample and sample mean is generally muddied, because we’re looking at a sample rather than the entire population. Sampling error is the difference between a sample and the entire population. For any given random sample, the mean of the sample almost certainly doesn’t equal the true mean of the population due to sampling error.

    So, our goal is to decide whether our sample mean is considerably different from the null hypothesis mean. Hypothesis tests are useful in that they allow us to quantify the probability that our sample mean is unusual.

    Here are the parts of a hypothesis test …

    Null hypothesis, denoted H0, is the statement about the population parameter that is assumed to be true unless there is convincing evidence to the contrary.

    The alternative hypothesis, denoted Ha, is a statement about the population parameter that is contradictory to the null hypothesis and is accepted as true only if there is convincing evidence in favor of it.

    It will be an important part of our course!

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