Category: Statistics

Your work on the new student committee was a huge success! The director of new s

Your work on the new student committee was a huge success! The director of new student recruitment has requested that you continue your work on the committee. Specifically, the director would like you to distribute a small survey to the students who attended the weekend event, gauging their level of interest in studying at UMGC. The director is interested in obtaining demographic information from the prospective students, the academic program into which they would enroll, and their overall level of interest in attending UMGC. The survey questions and results are below:
Survey questions given to prospective students
What is your age?
Would you live in on-campus housing or off-campus housing?
Into which academic program would you enroll?
How likely are you to attend UMGC in the next year? (Rate: 1–4, 1 is not likely and 4 is very likely)
Student
Age
Housing
Academic Program
Likely to attend UMGC
1
18
Off campus
Political science
4
2
19
Off campus
History
1
3
17
On campus
Cybersecurity
2
4
30
Off campus
Nursing
4
5
18
On campus
History
3
6
21
On campus
Psychology
4
7
45
Off campus
Business
2
8
20
On campus
Business
3
9
18
On campus
Accounting
4
10
36
Off campus
Nursing
4
11
25
Off campus
History
2
12
29
Off campus
Sociology
2
13
31
Off campus
Spanish
2
14
19
On campus
Psychology
2

Your first task is to define the data resulting from each survey question as qualitative or quantitative. If the variable is qualitative, indicate if it is nominal or ordinal. If it is quantitative, indicate whether it is discrete or continuous and whether it is interval or ratio (see graphic below).

Next, create a table (a frequency distribution, stem and leaf plot, or a grouped frequency distribution) to organize the data from one of the variables. Include the table in your post. Does including the relative frequency or cumulative frequency make the table more meaningful? Why do you feel this table best organizes the data?
Then, consider how you might visually display the results as a graph (bar graph, Pareto chart, dot plot, line graph, histogram, pie chart, or box plot). Include the graph in your post. Why did you choose this graph? Explain why you believe this graph is the best choice to display the data.
Finally, find the mean, median, and mode for one of the variables. Which of these measures of central tendency do you think is the best choice for “average” and why? Find the range and standard deviation (measures of dispersion) for the variable. What would a narrower or wider deviation signify in the context of this data?
Your initial post to the discussion (covering the four tasks above) is due by 11:59 p.m. EST on Saturday.
Consider the graphs/charts and measures of central tendency and dispersion that your peers have chosen. Do they align with your choices? Discuss at least one benefit of your peers’ choices. Can you share a recommendation to improve their choices?

Read this week’s reading, ‘Descriptive Statistics’. Then, using the steps outlin

Read this week’s reading, ‘Descriptive Statistics’. Then, using the steps outlined in the reading and this week’s lecture as a guide, complete the same exercise using the data set “Album Sales” under the “Regression” folder (Open > Data Library > Regression > Album Sales).
Report the following descriptive statistics for the variables “sales,” “airplay,” and “adverts” in a single table: the mean
median
mode
standard deviation
variance
Copy and paste your output from JASP into a Word or Google Doc and save. Upload and submit your document
DOWNLOAD THE APP JASP
JASP
https://mfr.osf.io/render?url=https://osf.io/57y4s…

Hello, Sylvain. What I wanna do is give you a brief overview of what you have to

Hello, Sylvain. What I wanna do is give you a brief overview of what you have to do for your week seven Lab. So in the week seven lab, the only spreadsheet we’re going to really need is your week six spreadsheet. So if you go to Modules and under week six and got the lesson here shall weeks, Week six spreadsheet. I’ve already downloaded it, so it looks like this. So this is the spreadsheet you should have downloaded for the lab. So I will download that. Another thing that I would download is the week seven Lab. So that looks like this. Now remember these lecture notes are for you to write all over them. Make any little documentation that you would like to help you do your lab. Ok. So in the week seven lab, you’re, the only spreadsheet you need is the week six spreadsheet, but you do need your week five lab data. So go ahead and take a moment to pull that. That should be the ten heights that your instructor gave you and then the ten heights that you gathered. Ok. So to get, I talk about how to get the weeks, six spreadsheets. So you have that. Now in order to kind of give an example of what you’re going to do in your lab. I want to go back and look at our data from lab five. Now remember the scenario for lab five. I’m a principal of a high school. I walk down the senior map hallway and I picked one class in one class had ten students in it. And I pooled their midterm exam math scores. And they’re listed here. If you remember from lab from last week that our mean, I’m just going to write it here. Feel free to fill in on your sheet. Our mean was a 76.9. Our standard deviation was 11.3964. And if remember our score that we were comparing was an 85. So I’m gonna write those down. Okay? So what I wanna do is I want to find a 95% confidence interval for the true mean midterm average of all the people in the class spraying it all. And then even trying to branch out further if we could to all the people in that or see years or whatever, what would the population we’re trying to represent. So if you open up your week six spreadsheet, it’s asking for competence level. And so our first one is 95. N is how many pieces of data we have. We only have ten. Are mean was 76.9, our standard deviation was 11.396 for population standard deviation, our answer is no, we don’t know, we didn’t have a population. They’re asking if this standard deviation is a population standard deviation or not. And it was not because we’ve pulled a sample. Okay. You would only type yes, here if you’ve actually pulled an entire population. Okay? And so then it gives us some great information here in yellow. But the one thing you’re going to take a look at are these the margin of error, you’re lower and your upper limit. So I’m going to take a screenshot because that’s the first thing that’s asking for. Take a screenshot or Dina and you feel free to do the same so that you have it. And then I’ll even, and then you can print these out and look at them while you’re doing it for lab. Let me make it very small. So it, since he got up into space. Okay. So there’s my 95% confidence at all. Now, give a practical interpretation of what this means. Well, what this means is that I can write, I am 95% confident. I am 95% confident that the true average of the mass mid-term exam is between 68.785.1 k. I am 95% confident that the true average, remember we only pull ten people. But if I were, if I wanted that to represent the entire average of the midterm exam of everybody who took it. It’s between a 68.785.1. I’m 95% sure that the average will be there. So what I mean by that is I would have to verify I would have to go and pull the midterm math exam of every single student who took it in that senior year at that school and average it out. And what I’m saying is a 95% sure that that average will fall between 60.785.1. A lot of students say, oh, I’m 95% confident that the average of my sample, now, you know the average of your sample, the average of your sample with 76.9. So don’t, when you’re making a practical interpretation, you’re not saying something about the sample. You’re seeing something about the true mean of the population. If that makes sense. So you’re basing it off of the sample that you have. Okay? Then what you’re gonna do is you’re going to do a 99% confidence interval. So all you have to do is go back to our spreadsheet and I, and when its height in, in blue, that I’m changing the confidence level from 0.952.99. So let’s change this to 0.99. Then I’m going to take a screenshot in your answers should match mine right now. We’re doing this together for the midterm math example. Okay? So here’s my screenshot. Now this is for a 99% confidence interval. So what is the practical interpretation of that? Again, let’s write this out. I am 99% confident that the true average of the midterm exam is between 65.288.6. So 60 by 0.288.6. And I’m getting that from the lower and the upper limit of my confidence interval. Now, want you to do is I want you to look at the margin of error. The margin of error for being 99% confident was 11.7, and the margin of error for being 95% confident was 8.15. What does that mean? Explain what that even means. What would the margin of error be larger or smaller? So in this case, and I want to tell you the answer for years, but for this case, it would be larger. And I want you to think about why that is. Look at the confidence intervals themselves. If you come back up here for the 95, mm, the 95 was between 68.785.1 and this is 65.288.6. It’s kind of hard. You want to see if we can look at them side-by-side even that would be great. But you should notice that the interval for the 99% confidence is wider. It’s a wider interval. Why is that? Think about that. Why is it, why is it bigger? Well, I want you to think about this. If you’re 99% confident, that means that you don’t want to be wrong, right? I mean, you never want to be wrong anytime. But 99% confidence means that you’re, you’re, you’re pretty sure, like you’re very, very sure that it’s going to fall between 65.20.6. That the more sure. How could you even me washer by opening up the interval? Does that make sense? So let me demonstrate here, maybe this will make more sense. Let’s say I was like, I am sure that the average amount of rain I’m going to get is two inches and six inches of rain, I’m sure but 95%. Well, I don’t want you to be 95%. I want you to be 99%. All of you want me to be that sure. I’m sure is between 015 Inches of rain. I don’t know where we’re living but 0.15. so what I did is I’ve widened my interval so that I could have the amount of inches of rain inside of my interval. You widen it, you include more numbers. Therefore, that makes you more confident. Okay? So that’s what happened here. So what we did just to recap, we took data from last week, okay? And we looked at the mean, the score, and the standard deviation, and we constructed a 9599% confidence interval. We talked about what they mean in context of the problem. Okay? We also looked at what happened to the interval itself as we become more confident and feel free to play around with this a little bit. What if you’re 80% confident? What if you’re 90% confident? So look what happens to your margin as you do that. Look what happens to the length of your interval when you change your level of confidence. That’s really great to bring in to your lab. So hopefully that explains what we’re gonna do in your actual lab. So let’s look at your actual lab. So what you’re going to be doing is you’re going to first, I want you to read articles about competence intervals. I would really like you to see how they apply, especially in the health sciences. And these articles will kind of inspire that thought. Okay? And then what you’re gonna do is you’re going to, and I would love the little summary. I would love a little summary starting off this lab of what you learned from these articles. Then the next step, using the data that you collected in week five. Now remember in week five, you had ten different peoples height plus the ten that you or your professor gave you. So you should have 20 numbers. I want you to discuss the Gad Yair method of collection was its systematic convenience. Cluster, cluster stratified simple random. What are some false? I know you highlighted this in week five, but now I want you to talk about what are some faults with the type of data collection, okay. What other types of data collections could you used instead? And how might that affected your survey? So this right here is the really important part. This is what I am trying to see what you have learned, okay? So it’s about telling me again what type of method you chose. Telling me what faults come with that method, some research might be involved. And then telling me another type of method that you would’ve liked to try if you could have and how that would affect your study. Okay. Then you’re going to tell me what the point estimate was, the point estimates, your mean. So in our case when we’re talking about the midterm, whereas my lecture notes here again, this right here, maybe I should write this down. The mean, the 76.9, that’s your point estimate. This is what we’re basing it off of. Okay? So the point estimate is the mean. Okay? So give a point estimate for the mean of the average height of all people at the place of your work. If that’s where you pull your sample and start by putting the 20 heights you are working with into the blue data. So we’ve done this already. You should already have your data in and you know what your mean is in your sample standard deviation. Then what you’re gonna do is you’re going to construct a 95% confidence interval for the true mean height of all the pupil, the place of your work, what is the interval? And provide a screenshot which we did today. Then you’re going to give me a practical interpretation what this means. So what you’re explaining is your 95% confident that the true mean height of all the people that you work with or the population that you have is between this and this, okay? Then you’re gonna put, of course going to post a screenshot. Then you’re going to change your confidence levels. With 99% confidence level, you’re going to take a screenshot and provide that as well. You’re going to compare the margins of error. So this is where you’re going to talk about what happened to your margin of error when we went from a 95 to 99% confidence interval, what happened to your interval when he went from 95 to 99% confidence interval? There should be a good paragraph explaining what happened, why it’s happening, and try to explain it in the context of the data and the lab. And that’s it. And then you’re just going to save your document and upload and that will submit your work. Please feel free to reach out if you have any questions or concerns.
Lab
Assignment
Required Resources
Read/review the following resources for this activity:
OpenStax Textbook: Chapter 8
Lesson
Chamberlain University Library
Week 7 Lab TemplateLinks to an external site.
Scenario/Summary
The highlight of this week’s lab is confidence intervals and the use of these intervals in the health sciences. There is a short reading that specifically relates confidence intervals to health sciences and then you are asked to demonstrate your knowledge of confidence intervals by applying them in a practical manner.
Prepare
Download the Week 7 Lab Lecture Notes.Links to an external site.
Follow along with he Week 7 Lab Video and fill out the Week 7 Lab Lecture Notes as you watch the video. Instructions
Steps to Complete the Week 7 Lab
Step 1: Find these articles in the Chamberlain Library. Once you click each link, you will be logged into the Library and then click on “PDF Full Text”.
First Article: Confidence Intervals, Part 1 Links to an external site.
Second Article: Confidence Intervals, Part 2 Links to an external site.
Step 2: Consider the use of confidence intervals in health sciences with these articles as inspiration and insights.
Step 3: Using the data you collected for the Week 5 Lab (heights of 10 different people that you work with plus the 10 heights provided by your instructor), discuss your method of collection for the values that you are using in your study (systematic, convenience, cluster, stratified, simple random). What are some faults with this type of data collection? What other types of data collection could you have used, and how might this have affected your study?
Step 4: Now use the Week 6 Spreadsheet to help you with calculations for the following questions/statements.
a) Give a point estimate (mean) for the average height of all people at the place where you work. Start by putting the 20 heights you are working with into the blue Data column of the spreadsheet. What is your point estimate, and what does this mean?
example of adding a point estimate to spreadsheet
b) Find a 95% confidence interval for the true mean height of all the people at your place of work. What is the interval? [see screenshot below]
c) Give a practical interpretation of the interval you found in part b, and explain carefully what the output means. (For example, you might say, “I am 95% confident that the true mean height of all of the people in my company is between 64 inches and 68 inches”).
d) Post a screenshot of your work from the t value Confidence Interval for µ from the Confidence Interval tab on the Week 6 Excel spreadsheet
Step 5: Now, change your confidence level to 99% for the same data, and post a screenshot of this table, as well.
Step 6: Compare the margins of error from the two screenshots. Would the margin of error be larger or smaller for the 99% CI? Explain your reasoning.
Step 7: Save the Week 7 Lab document with your answers and include your name in the title.
Step 8: Submit the document.
Requirements
The deliverable is a Word document with your answers to the questions posed below based on the article you find.
Required Software
Microsoft Word
Internet access to read articles
Grading
This activity will be graded based on the Week 7 Lab Rubric.
Outcomes
CO 8: Given a sample dataset, estimate and interpret the confidence intervals for population mean or proportion.
Due Date
By 11:59 p.m. MT on Sunday
Rubric
Week 7 Assignment: Lab
Week 7 Assignment: Lab
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeData Collection and Pitfalls Range
12 pts
Proficient Lab includes all of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
10 pts
Above Average Lab includes 3 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
8 pts
Average Lab includes 2 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
6 pts
Needs Improvement Lab includes 1 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
0 pts
No Effort
12 pts
This criterion is linked to a Learning OutcomeEstimate a Confidence Interval
15 pts
Proficient Lab includes all of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
12 pts
Above Average Lab includes 3 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
10 pts
Average Lab includes 2 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
8 pts
Needs Improvement Lab includes 1 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
0 pts
No Effort
15 pts
This criterion is linked to a Learning OutcomeInterpret a Confidence Interval
15 pts
Proficient Lab addresses all of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
12 pts
Above Average Lab addresses 2 out of 3 of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
10 pts
Average Lab addresses 1 out of 3 of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
8 pts
Needs Improvement Lab mentions but does not explain fully any of the following *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
0 pts
No Effort
15 pts
This criterion is linked to a Learning OutcomeGrammar and Formatting
8 pts
Proficient Lab is easy to read and presents material in a logical order with no grammatical errors.
6 pts
Above Average Lab is easy to read and presents material in a logical order. There are a few grammatical errors but they do not distract from readability.
5 pts
Average Lab is easy to read and has few grammatical errors, but it is not logically organized.
4 pts
Needs Improvement There are significant grammatical errors and organizational issues that distract from readability.
0 pts
No Effort
8 pts
Total Points: 50

Complete the following on the Data tab of the Pastas R Us data file:
Calculate “

Complete the following on the Data tab of the Pastas R Us data file:
Calculate “Annual Sales” for each restaurant. Annual Sales is the result of multiplying a restaurant’s “SqFt.” by “Sales/SqFt.” The first value has been provided for you.
Calculate the mean, standard deviation, skew, 5-number summary, and interquartile range (IQR) for each of the variables. The formulas and the first results have been provided for you.
Create a boxplot (sometimes referred to as a box and whisker chart) for the “Annual Sales” variable.
Create a histogram for the “Sales/SqFt” variable.
Respond to the following questions on the Questions tab of the Pastas R Us data file:
Does the annual sales boxplot look symmetric?
Would you prefer the IQR instead of the standard deviation to describe the dispersion of the annual sales variable? If so, why?
Does the histogram show that the sales per square foot distribution is symmetric?
If the sales per square foot distribution is not symmetric, what is the skew?
If there are any outliers, which one(s)? What is the “SqFt” area of the outlier(s)?
Is the outlier(s) smaller or larger than the average restaurant in the data? What can you conclude from this observation?
What measure of central tendency may be more appropriate to describe “Sales/SqFt”? Why?

1/ Explore the data – explore the dataset to find if there are any signs that e-

1/ Explore the data – explore the dataset to find if there are any signs that e-Car’s current pricing contains ‘pricing errors’, resulting in ‘leaving money on the table’.
2/ Run your analysis – Segment the data and use exploratory visualization to identify pockets of opportunities. Choose the most appropriate analytic technique. Identify opportunities.
3/ Tell your story – Present your findings and solution as a team. Maximum 10 slides, including executive summary, and appendix on methodology.

Read the Crocker (2003) article on test fairness. In your post, please include t

Read the Crocker (2003) article on test fairness. In your post, please include the following:
Define test fairness in your own words. Think of an example of an assessment that you have taken, given, or used on the job. What is one example of a study you might design to investigate the fairness of this test? What groups of examinees are you comparing and what is the possible bias effect you are trying to allay? Provide at least one verse of Biblical evidence that supports the study of test fairness.
On page 9, Crocker (2003) discuss consequential validity which refers to the myriad effects of giving or requiring a test. Do you think that consequential validity is a worthwhile endeavor? Does it matter what the context is (e.g., psychological vs. educational) or does it not matter? Use a peer-reviewed source or your textbook to support your response.
For each thread, students must support their assertions with at least one (1)
scholarly and at least one (1) scriiptural citation in current APA format. Acceptable sources
include: The Bible, the textbook, and other peer-reviewed articles and/or books.

Hello, Sylvain. What I wanna do is give you a brief overview of what you have to

Hello, Sylvain. What I wanna do is give you a brief overview of what you have to do for your week seven Lab. So in the week seven lab, the only spreadsheet we’re going to really need is your week six spreadsheet. So if you go to Modules and under week six and got the lesson here shall weeks, Week six spreadsheet. I’ve already downloaded it, so it looks like this. So this is the spreadsheet you should have downloaded for the lab. So I will download that. Another thing that I would download is the week seven Lab. So that looks like this. Now remember these lecture notes are for you to write all over them. Make any little documentation that you would like to help you do your lab. Ok. So in the week seven lab, you’re, the only spreadsheet you need is the week six spreadsheet, but you do need your week five lab data. So go ahead and take a moment to pull that. That should be the ten heights that your instructor gave you and then the ten heights that you gathered. Ok. So to get, I talk about how to get the weeks, six spreadsheets. So you have that. Now in order to kind of give an example of what you’re going to do in your lab. I want to go back and look at our data from lab five. Now remember the scenario for lab five. I’m a principal of a high school. I walk down the senior map hallway and I picked one class in one class had ten students in it. And I pooled their midterm exam math scores. And they’re listed here. If you remember from lab from last week that our mean, I’m just going to write it here. Feel free to fill in on your sheet. Our mean was a 76.9. Our standard deviation was 11.3964. And if remember our score that we were comparing was an 85. So I’m gonna write those down. Okay? So what I wanna do is I want to find a 95% confidence interval for the true mean midterm average of all the people in the class spraying it all. And then even trying to branch out further if we could to all the people in that or see years or whatever, what would the population we’re trying to represent. So if you open up your week six spreadsheet, it’s asking for competence level. And so our first one is 95. N is how many pieces of data we have. We only have ten. Are mean was 76.9, our standard deviation was 11.396 for population standard deviation, our answer is no, we don’t know, we didn’t have a population. They’re asking if this standard deviation is a population standard deviation or not. And it was not because we’ve pulled a sample. Okay. You would only type yes, here if you’ve actually pulled an entire population. Okay? And so then it gives us some great information here in yellow. But the one thing you’re going to take a look at are these the margin of error, you’re lower and your upper limit. So I’m going to take a screenshot because that’s the first thing that’s asking for. Take a screenshot or Dina and you feel free to do the same so that you have it. And then I’ll even, and then you can print these out and look at them while you’re doing it for lab. Let me make it very small. So it, since he got up into space. Okay. So there’s my 95% confidence at all. Now, give a practical interpretation of what this means. Well, what this means is that I can write, I am 95% confident. I am 95% confident that the true average of the mass mid-term exam is between 68.785.1 k. I am 95% confident that the true average, remember we only pull ten people. But if I were, if I wanted that to represent the entire average of the midterm exam of everybody who took it. It’s between a 68.785.1. I’m 95% sure that the average will be there. So what I mean by that is I would have to verify I would have to go and pull the midterm math exam of every single student who took it in that senior year at that school and average it out. And what I’m saying is a 95% sure that that average will fall between 60.785.1. A lot of students say, oh, I’m 95% confident that the average of my sample, now, you know the average of your sample, the average of your sample with 76.9. So don’t, when you’re making a practical interpretation, you’re not saying something about the sample. You’re seeing something about the true mean of the population. If that makes sense. So you’re basing it off of the sample that you have. Okay? Then what you’re gonna do is you’re going to do a 99% confidence interval. So all you have to do is go back to our spreadsheet and I, and when its height in, in blue, that I’m changing the confidence level from 0.952.99. So let’s change this to 0.99. Then I’m going to take a screenshot in your answers should match mine right now. We’re doing this together for the midterm math example. Okay? So here’s my screenshot. Now this is for a 99% confidence interval. So what is the practical interpretation of that? Again, let’s write this out. I am 99% confident that the true average of the midterm exam is between 65.288.6. So 60 by 0.288.6. And I’m getting that from the lower and the upper limit of my confidence interval. Now, want you to do is I want you to look at the margin of error. The margin of error for being 99% confident was 11.7, and the margin of error for being 95% confident was 8.15. What does that mean? Explain what that even means. What would the margin of error be larger or smaller? So in this case, and I want to tell you the answer for years, but for this case, it would be larger. And I want you to think about why that is. Look at the confidence intervals themselves. If you come back up here for the 95, mm, the 95 was between 68.785.1 and this is 65.288.6. It’s kind of hard. You want to see if we can look at them side-by-side even that would be great. But you should notice that the interval for the 99% confidence is wider. It’s a wider interval. Why is that? Think about that. Why is it, why is it bigger? Well, I want you to think about this. If you’re 99% confident, that means that you don’t want to be wrong, right? I mean, you never want to be wrong anytime. But 99% confidence means that you’re, you’re, you’re pretty sure, like you’re very, very sure that it’s going to fall between 65.20.6. That the more sure. How could you even me washer by opening up the interval? Does that make sense? So let me demonstrate here, maybe this will make more sense. Let’s say I was like, I am sure that the average amount of rain I’m going to get is two inches and six inches of rain, I’m sure but 95%. Well, I don’t want you to be 95%. I want you to be 99%. All of you want me to be that sure. I’m sure is between 015 Inches of rain. I don’t know where we’re living but 0.15. so what I did is I’ve widened my interval so that I could have the amount of inches of rain inside of my interval. You widen it, you include more numbers. Therefore, that makes you more confident. Okay? So that’s what happened here. So what we did just to recap, we took data from last week, okay? And we looked at the mean, the score, and the standard deviation, and we constructed a 9599% confidence interval. We talked about what they mean in context of the problem. Okay? We also looked at what happened to the interval itself as we become more confident and feel free to play around with this a little bit. What if you’re 80% confident? What if you’re 90% confident? So look what happens to your margin as you do that. Look what happens to the length of your interval when you change your level of confidence. That’s really great to bring in to your lab. So hopefully that explains what we’re gonna do in your actual lab. So let’s look at your actual lab. So what you’re going to be doing is you’re going to first, I want you to read articles about competence intervals. I would really like you to see how they apply, especially in the health sciences. And these articles will kind of inspire that thought. Okay? And then what you’re gonna do is you’re going to, and I would love the little summary. I would love a little summary starting off this lab of what you learned from these articles. Then the next step, using the data that you collected in week five. Now remember in week five, you had ten different peoples height plus the ten that you or your professor gave you. So you should have 20 numbers. I want you to discuss the Gad Yair method of collection was its systematic convenience. Cluster, cluster stratified simple random. What are some false? I know you highlighted this in week five, but now I want you to talk about what are some faults with the type of data collection, okay. What other types of data collections could you used instead? And how might that affected your survey? So this right here is the really important part. This is what I am trying to see what you have learned, okay? So it’s about telling me again what type of method you chose. Telling me what faults come with that method, some research might be involved. And then telling me another type of method that you would’ve liked to try if you could have and how that would affect your study. Okay. Then you’re going to tell me what the point estimate was, the point estimates, your mean. So in our case when we’re talking about the midterm, whereas my lecture notes here again, this right here, maybe I should write this down. The mean, the 76.9, that’s your point estimate. This is what we’re basing it off of. Okay? So the point estimate is the mean. Okay? So give a point estimate for the mean of the average height of all people at the place of your work. If that’s where you pull your sample and start by putting the 20 heights you are working with into the blue data. So we’ve done this already. You should already have your data in and you know what your mean is in your sample standard deviation. Then what you’re gonna do is you’re going to construct a 95% confidence interval for the true mean height of all the pupil, the place of your work, what is the interval? And provide a screenshot which we did today. Then you’re going to give me a practical interpretation what this means. So what you’re explaining is your 95% confident that the true mean height of all the people that you work with or the population that you have is between this and this, okay? Then you’re gonna put, of course going to post a screenshot. Then you’re going to change your confidence levels. With 99% confidence level, you’re going to take a screenshot and provide that as well. You’re going to compare the margins of error. So this is where you’re going to talk about what happened to your margin of error when we went from a 95 to 99% confidence interval, what happened to your interval when he went from 95 to 99% confidence interval? There should be a good paragraph explaining what happened, why it’s happening, and try to explain it in the context of the data and the lab. And that’s it. And then you’re just going to save your document and upload and that will submit your work. Please feel free to reach out if you have any questions or concerns.
Lab
Assignment
Required Resources
Read/review the following resources for this activity:
OpenStax Textbook: Chapter 8
Lesson
Chamberlain University Library
Week 7 Lab TemplateLinks to an external site.
Scenario/Summary
The highlight of this week’s lab is confidence intervals and the use of these intervals in the health sciences. There is a short reading that specifically relates confidence intervals to health sciences and then you are asked to demonstrate your knowledge of confidence intervals by applying them in a practical manner.
Prepare
Download the Week 7 Lab Lecture Notes.Links to an external site.
Follow along with he Week 7 Lab Video and fill out the Week 7 Lab Lecture Notes as you watch the video. Instructions
Steps to Complete the Week 7 Lab
Step 1: Find these articles in the Chamberlain Library. Once you click each link, you will be logged into the Library and then click on “PDF Full Text”.
First Article: Confidence Intervals, Part 1 Links to an external site.
Second Article: Confidence Intervals, Part 2 Links to an external site.
Step 2: Consider the use of confidence intervals in health sciences with these articles as inspiration and insights.
Step 3: Using the data you collected for the Week 5 Lab (heights of 10 different people that you work with plus the 10 heights provided by your instructor), discuss your method of collection for the values that you are using in your study (systematic, convenience, cluster, stratified, simple random). What are some faults with this type of data collection? What other types of data collection could you have used, and how might this have affected your study?
Step 4: Now use the Week 6 Spreadsheet to help you with calculations for the following questions/statements.
a) Give a point estimate (mean) for the average height of all people at the place where you work. Start by putting the 20 heights you are working with into the blue Data column of the spreadsheet. What is your point estimate, and what does this mean?
example of adding a point estimate to spreadsheet
b) Find a 95% confidence interval for the true mean height of all the people at your place of work. What is the interval? [see screenshot below]
c) Give a practical interpretation of the interval you found in part b, and explain carefully what the output means. (For example, you might say, “I am 95% confident that the true mean height of all of the people in my company is between 64 inches and 68 inches”).
d) Post a screenshot of your work from the t value Confidence Interval for µ from the Confidence Interval tab on the Week 6 Excel spreadsheet
Step 5: Now, change your confidence level to 99% for the same data, and post a screenshot of this table, as well.
Step 6: Compare the margins of error from the two screenshots. Would the margin of error be larger or smaller for the 99% CI? Explain your reasoning.
Step 7: Save the Week 7 Lab document with your answers and include your name in the title.
Step 8: Submit the document.
Requirements
The deliverable is a Word document with your answers to the questions posed below based on the article you find.
Required Software
Microsoft Word
Internet access to read articles
Grading
This activity will be graded based on the Week 7 Lab Rubric.
Outcomes
CO 8: Given a sample dataset, estimate and interpret the confidence intervals for population mean or proportion.
Due Date
By 11:59 p.m. MT on Sunday
Rubric
Week 7 Assignment: Lab
Week 7 Assignment: Lab
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeData Collection and Pitfalls Range
12 pts
Proficient Lab includes all of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
10 pts
Above Average Lab includes 3 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
8 pts
Average Lab includes 2 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
6 pts
Needs Improvement Lab includes 1 out of 4 of the following: *20 data points/heights *method of collection *faults with method of collection used *a different method of collection
0 pts
No Effort
12 pts
This criterion is linked to a Learning OutcomeEstimate a Confidence Interval
15 pts
Proficient Lab includes all of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
12 pts
Above Average Lab includes 3 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
10 pts
Average Lab includes 2 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
8 pts
Needs Improvement Lab includes 1 out of 4 of the following: *point estimate *95% confidence interval *screenshot of Excel spreadsheet *practical interpretation on confidence interval
0 pts
No Effort
15 pts
This criterion is linked to a Learning OutcomeInterpret a Confidence Interval
15 pts
Proficient Lab addresses all of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
12 pts
Above Average Lab addresses 2 out of 3 of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
10 pts
Average Lab addresses 1 out of 3 of the following well *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
8 pts
Needs Improvement Lab mentions but does not explain fully any of the following *a 99% confidence interval *screenshot of Excel spreadsheet *comparison of margins of error for 99% and 95% *explanation of reasoning
0 pts
No Effort
15 pts
This criterion is linked to a Learning OutcomeGrammar and Formatting
8 pts
Proficient Lab is easy to read and presents material in a logical order with no grammatical errors.
6 pts
Above Average Lab is easy to read and presents material in a logical order. There are a few grammatical errors but they do not distract from readability.
5 pts
Average Lab is easy to read and has few grammatical errors, but it is not logically organized.
4 pts
Needs Improvement There are significant grammatical errors and organizational issues that distract from readability.
0 pts
No Effort
8 pts
Total Points: 50

Statistical application and the interpretation of data is important in health ca

Statistical application and the interpretation of data is important in health care. Review the statistical concepts covered in this topic. In a 750-1,000-word paper, discuss the significance of statistical application in health care, including the following:
Describe the application of statistics in health care. Specifically discuss its significance to safety, health promotion, and leadership.
Provide an example of the application of statistics in health care by finding a journal article that illustrates its application to safety, health promotion, or leadership. Identify at least two statistical terms used within this article and provide definitions. Include the article on your reference page.
Describe the importance of standardized health care data as it pertains to ensuring integrity of research data.
In addition to your article, use two peer-reviewed, scholarly references other than those listed in the Topic 1 Resources. References should be published within the last 5-7 years.
Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Produce the following 5 plots using the data sets uploaded in the MATLAB tab
1.

Produce the following 5 plots using the data sets uploaded in the MATLAB tab
1. 2D line plot showing two datasets on the same plot taken from ‘acoustic 2d’ and ‘acoustic 2D
second plot’ data sets. Present the figure in your most preferred style.
2. A 3D data plot using data from ‘Acoustic 3D coursework’ – the choice of how you display this
3D data is up to you. The plot should clearly articulate the underlying scientific results, as
explained in the workshop section, but there are several sensible ways to do this, and so the
choice is yours.
3. A statistical analysis of a large data set, which then should be plotted as average of change
in amplitude with time vs time of flight, including a method of dispersion of your choice, for
data set ‘statistics 3’. Again, the choice of how to represent the data, especially regarding the
dispersion, is up to you. For this data set, include a one-line justification of the measurement
of the dispersion you decided to use.
4. Following the Matlab Subplot tutorial here you are required to investigate the impact of
altering the time step on the images produced. You should produce 4 plots (one for each of
x, y, z and xyz data), each of which shows 4 subplots for the four different time steps (all
labelled on your plot). Please use time steps of π/2, π/5, π/20 and π/1000. An example in the
the image below is given for the X data set is shown. For these plots you are free to choose
the style of plot which best suits the data and are encouraged to select an alternative colour
scheme which enhances the visualisation of the plot. Please ensure all plots are labelled
appropriately. For clarity, Plot 1: x data, 4 subplots of π/2, π/5, π/20 and π/1000;
plot 2: y data, 4 subplots of π/2, π/5, π/20 and π/1000; etc.
5. Submit a plot via the following steps:
• Download the coursework data (Nail Penetration Coursework).
• Import the data into MATLAB (you can use any method of your choice).
• Plot a colour map of the data, taking care to label it clearly, choose appropriate axis limits and
a suitable colour scale with a labelled colour bar (you can use any method of your choice to
format the figure).
• Find the locations of the data points that represent the trends shown by the arrows in the
Figure below (using the code covered in the workshop, or your own approach if you prefer).
• Plot the data points you have found for both trends on top of the colour map.
• Fit an appropriate model to the data points that you have found (using the code covered in the
workshop, or your own approach if you prefer).
• Plot the models for both trends on the same figure (that already contains the colour map and
the data points), taking care to choose clear formatting for all the data shown, and add a
legend.
• In the submission document include the equation and coefficients for your chosen model and
a justification (a sentence or two) for you chosen model (model = curve shape/equation).
Produce a .pdf containing all 5 figures (ideally good quality, i.e. high-resolution or a vector graphic)
for submission.

Produce the following 5 plots using the data sets uploaded in the MATLAB tab
1.

Produce the following 5 plots using the data sets uploaded in the MATLAB tab
1. 2D line plot showing two datasets on the same plot taken from ‘acoustic 2d’ and ‘acoustic 2D
second plot’ data sets. Present the figure in your most preferred style.
2. A 3D data plot using data from ‘Acoustic 3D coursework’ – the choice of how you display this
3D data is up to you. The plot should clearly articulate the underlying scientific results, as
explained in the workshop section, but there are several sensible ways to do this, and so the
choice is yours.
3. A statistical analysis of a large data set, which then should be plotted as average of change
in amplitude with time vs time of flight, including a method of dispersion of your choice, for
data set ‘statistics 3’. Again, the choice of how to represent the data, especially regarding the
dispersion, is up to you. For this data set, include a one-line justification of the measurement
of the dispersion you decided to use.
4. Following the Matlab Subplot tutorial here you are required to investigate the impact of
altering the time step on the images produced. You should produce 4 plots (one for each of
x, y, z and xyz data), each of which shows 4 subplots for the four different time steps (all
labelled on your plot). Please use time steps of π/2, π/5, π/20 and π/1000. An example in the
the image below is given for the X data set is shown. For these plots you are free to choose
the style of plot which best suits the data and are encouraged to select an alternative colour
scheme which enhances the visualisation of the plot. Please ensure all plots are labelled
appropriately. For clarity, Plot 1: x data, 4 subplots of π/2, π/5, π/20 and π/1000;
plot 2: y data, 4 subplots of π/2, π/5, π/20 and π/1000; etc.
5. Submit a plot via the following steps:
• Download the coursework data (Nail Penetration Coursework).
• Import the data into MATLAB (you can use any method of your choice).
• Plot a colour map of the data, taking care to label it clearly, choose appropriate axis limits and
a suitable colour scale with a labelled colour bar (you can use any method of your choice to
format the figure).
• Find the locations of the data points that represent the trends shown by the arrows in the
Figure below (using the code covered in the workshop, or your own approach if you prefer).
• Plot the data points you have found for both trends on top of the colour map.
• Fit an appropriate model to the data points that you have found (using the code covered in the
workshop, or your own approach if you prefer).
• Plot the models for both trends on the same figure (that already contains the colour map and
the data points), taking care to choose clear formatting for all the data shown, and add a
legend.
• In the submission document include the equation and coefficients for your chosen model and
a justification (a sentence or two) for you chosen model (model = curve shape/equation).
Produce a .pdf containing all 5 figures (ideally good quality, i.e. high-resolution or a vector graphic)
for submission.