Category: Statistics

describe the equation and write it as a formula nd have it written in overleaf:

describe the equation and write it as a formula nd have it written in overleaf:
Start with:
Given the scope of this paper, we examine Tukey’s Honestly Significant Difference (HSD) test and the Holm-Bonferroni method for multiple comparisons. Both methods are used to address the problem of Type I errors when performing multiple hypothesis tests, but they approach this issue in different ways.
Tukey’s HSD test is a post-hoc analysis performed after an ANOVA indicates significant differences among group means. It aims to identify which specific means are significantly different from each other. The test uses the studentized range distribution, which considers the maximum difference between group means to control the family-wise error rate (FWER). The HSD test calculates the critical value for the smallest significant difference between group means (denoted as ( text{hsd} )). The formula for the HSD test when group sizes are equal is ( text{hsd} = q_{alpha,A} sqrt{frac{text{MSS}(A)}{S}} ), where ( q_{alpha,A} ) is the critical value from the studentized range distribution, ( text{MSS}(A) ) is the mean square error from the ANOVA, and ( S ) is the number of observations per group. For unequal group sizes, the formula is adjusted to ( text{hsd} = q_{alpha,A} sqrt{frac{1}{2} text{MSS}(A) left( frac{1}{S_a} + frac{1}{S_{a’}} right)} ), where ( S_a ) and ( S_{a’} ) are the sizes of groups ( a ) and ( a’ ) respectively.
The Holm-Bonferroni method is a sequentially rejective procedure used to control the family-wise error rate in multiple hypothesis testing. It is an improvement over the classic Bonferroni correction, providing more power while still controlling the FWER. The Holm-Bonferroni procedure works by ranking all individual p-values from the multiple tests in ascending order: ( p_1 leq p_2 leq cdots leq p_m ). Each ( p_i ) is then compared to ( alpha / (m – i + 1) ), where ( m ) is the total number of hypotheses tested, and ( alpha ) is the desired overall significance level. The null hypothesis ( H_i ) is rejected for the smallest ( p_i ) that satisfies ( p_i leq alpha / (m – i + 1) ), and all subsequent null hypotheses ( H_j ) (for ( j > i )) that also satisfy this condition are rejected. Mathematically, the adjusted significance levels can be represented as ( alpha_i’ = frac{alpha}{m – i + 1} ), where ( alpha_i’ ) is the adjusted significance level for the ( i )-th smallest p-value.
Both Tukey’s HSD and the Holm-Bonferroni methods are essential tools for multiple comparisons, yet they are applied in different contexts. Tukey’s HSD is particularly useful when dealing with pairwise comparisons after an ANOVA, providing a conservative approach to controlling Type I errors. On the other hand, the Holm-Bonferroni method is more flexible and powerful, applicable to a broader range of multiple testing scenarios. These methods ensure the robustness of statistical inferences in the presence of multiple comparisons, thereby enhancing the reliability of research findings across various fields.
bonforrini: https://www.bmj.com/content/310/6973/170.short

describe the equation and write it as a formula nd have it written in overleaf:

describe the equation and write it as a formula nd have it written in overleaf:
Start with:
Given the scope of this paper, we examine Tukey’s Honestly Significant Difference (HSD) test and the Holm-Bonferroni method for multiple comparisons. Both methods are used to address the problem of Type I errors when performing multiple hypothesis tests, but they approach this issue in different ways.
Tukey’s HSD test is a post-hoc analysis performed after an ANOVA indicates significant differences among group means. It aims to identify which specific means are significantly different from each other. The test uses the studentized range distribution, which considers the maximum difference between group means to control the family-wise error rate (FWER). The HSD test calculates the critical value for the smallest significant difference between group means (denoted as ( text{hsd} )). The formula for the HSD test when group sizes are equal is ( text{hsd} = q_{alpha,A} sqrt{frac{text{MSS}(A)}{S}} ), where ( q_{alpha,A} ) is the critical value from the studentized range distribution, ( text{MSS}(A) ) is the mean square error from the ANOVA, and ( S ) is the number of observations per group. For unequal group sizes, the formula is adjusted to ( text{hsd} = q_{alpha,A} sqrt{frac{1}{2} text{MSS}(A) left( frac{1}{S_a} + frac{1}{S_{a’}} right)} ), where ( S_a ) and ( S_{a’} ) are the sizes of groups ( a ) and ( a’ ) respectively.
The Holm-Bonferroni method is a sequentially rejective procedure used to control the family-wise error rate in multiple hypothesis testing. It is an improvement over the classic Bonferroni correction, providing more power while still controlling the FWER. The Holm-Bonferroni procedure works by ranking all individual p-values from the multiple tests in ascending order: ( p_1 leq p_2 leq cdots leq p_m ). Each ( p_i ) is then compared to ( alpha / (m – i + 1) ), where ( m ) is the total number of hypotheses tested, and ( alpha ) is the desired overall significance level. The null hypothesis ( H_i ) is rejected for the smallest ( p_i ) that satisfies ( p_i leq alpha / (m – i + 1) ), and all subsequent null hypotheses ( H_j ) (for ( j > i )) that also satisfy this condition are rejected. Mathematically, the adjusted significance levels can be represented as ( alpha_i’ = frac{alpha}{m – i + 1} ), where ( alpha_i’ ) is the adjusted significance level for the ( i )-th smallest p-value.
Both Tukey’s HSD and the Holm-Bonferroni methods are essential tools for multiple comparisons, yet they are applied in different contexts. Tukey’s HSD is particularly useful when dealing with pairwise comparisons after an ANOVA, providing a conservative approach to controlling Type I errors. On the other hand, the Holm-Bonferroni method is more flexible and powerful, applicable to a broader range of multiple testing scenarios. These methods ensure the robustness of statistical inferences in the presence of multiple comparisons, thereby enhancing the reliability of research findings across various fields.
bonforrini: https://www.bmj.com/content/310/6973/170.short

INSTRUCTIONS
EXAMPLE
DATA SETS
The Data Sets on the Data Set Tab will be used fo

INSTRUCTIONS
EXAMPLE
DATA SETS
The Data Sets on the Data Set Tab will be used for the Project Parts and the Semester Project. You will choose one Data Set to work with in Project Part 1 and create your own Systematic Sample. This sample will then be used in the following Project Parts assignments and the Semester Project as listed in those assignments. These Data Sets are only used in creating the Sample needed for those other assignments.
Each Set contains values from various states, male or female, and some other subcategories, just to ensure there were enough values with which to work.
When making your Systematic Sample, you will only work with the set you choose, if you need to start again from the beginning, do so. There is a detailed example and video on the Example Tab to help you create your sample. The first row and column of numbers in the Data Set are to help you keep count when making your sample.
Reference:
Publisher Centers for Disease Control and Prevention. (2021, April 21). U.S. chronic disease indicators (CDI). U.S. Chronic Disease Indicators (CDI) – CKAN. Retrieved February 25, 2022, from https://catalog.data.gov/dataset/u-s-chronic-disease-indicators-cdi
EXAMPLE of collecting the Systematic Sample
To create your Systematic Sample, you will use your Birth Month Number as your starting place in the set you choose, and your Birth Date as your nth value.
For example:
If Quinn was born on June 23rd.
They would use 6 for the value to start on and then pick every 23rd item from there.
If they had chosen the 2015 Average Annual Number Melanoma, mortality Data Set, the first number would be 101 (Column 6, Row 1), then the next value would be the 23rd one after that (Column 5, Row 3), which would be 11.
If Quinn does not get the 35 required but runs out of data, they will ‘wrap around’. So if the last number used was 49 (Column 2, Row 9), the next value to use would be 220 (Column 9, Row 2).
After you collect your sample, you do not need the Data Sets anymore. It is recommended that you download the Template for the Semester Project and enter the needed information as you do the work each week.
Remember to work with just one set of data.
2013 Average Annual Number Cancer of the colon and rectum (colorectal), mortality
2015 Average Annual Number Cancer of the colon and rectum (colorectal), mortality
2017 Average Annual Number Cancer of the colon and rectum (colorectal), mortality
2013 Average Annual Number Melanoma, mortality
2015 Average Annual Number Melanoma, mortality
2017 Average Annual Number Melanoma, mortality

#NAME?

– Share a web article containing an example of statistical inference or data distributions
– Relate the example to specific module concepts such as statistics vs parameters, variable types, graphical and numerical summaries, etc.
– Click theLink icon button to include an accessible, concise, descriptive linkLinks to an external site. to the web article
– Click theImages icon button to embed a graphic from the article
– Discuss why you find the article content interesting
– Write at least 2-3 sentences

Using Chapters 12+ of the book, provide a clear and concise answer to each quest

Using Chapters 12+ of the book, provide a clear and concise answer to each question below making sure to address each part of the question. Answer each question in sentence form using correct spelling and grammar. Then, submit the file (appropriately named) with your answers, including any graphs created, through the assignment link in this course space. Be sure that your answers are a different color font (ideally red) than the questions. Show your work and clearly label where each number is coming from. If you have to use a formula to get an answer, make sure to clearly write down the formula you are using. Credit is given both for right answers as well as based off of work shown/steps taken. 
What does the sign and the actual number tell you in a correlation?
Provide some details for the following examples based upon you answer above using a significance value of .05 (sign, number, and significance).
r = .03 (p>.05) 
r= .99 (p<.05)  r= -.8 (p<.05)  You are speaking with a friend who informs you that they have observed that when trees are taller in their neighborhood the birds at their house often “sing” more frequently. Your friend tells you that this must mean that taller trees result in birds singing more often. What might you tell your friend about this observation and their conclusion based upon what we know about correlations?   Below is a screenshot of a correlation between two quiz scores, Quiz 1 and Quiz 2. These are not real data (I made them for this example), but if they were what might you tell me about the results (.05 significance value)? Please also provide a brief description of the mean and standard deviation as well (under “descriptive statistics”). For an excellent example of how to interpret a correlation table see the following link created by ASK Brunel (2012/13): https://www.youtube.com/watch?v=pgQ9T_SdzoQ Suppose I provide you with the following information to calculate a correlation coefficient.  COVxy = 0.14 Sx * Sy = 0.22 Please calculate the correlation: Based on this correlation, provide an interpretation: Why might it not have been the best idea to immediately calculate a correlation such as this before doing anything else with the data?  After reading the below scenario, write out which statistical test you would use.  “A researcher prepares a set of 20 different photographs of Dakota’s dogs playing at the park, with 10 mounted on a white background and 10 mounted on red. One picture is identified as the test photograph and appears twice in the set, once on white and once on red. Each participant looks through the entire set of photographs and rates how amazing each dog photo is on a 10-point scale (0 = awesome and 10 = super amazing). Are the ratings for the test photograph significantly different when it is presented on a red background compared to a white background?” What statistical test should you use to answer this question and why? Below is a screenshot of a data set I made up in Excel:  The first step is you will need to open an Excel document and type in the data in the screenshot as shown. Then watch the attached link to a YouTube video. This will explain how to run a correlation in Excel. It is a straightforward function so likely will not be too confusing once you watch the video.  https://www.youtube.com/watch?v=K9zDgPO_P-g You do not need to enable anything/download anything to run a correlation other than just having Excel. Excel on Mac will work too. I am not sure if Numbers for Mac allows this to be ran by default, but it might. You are also welcome to just hand calculate it if you would like if you do not have the above programs/would rather just do this. Once you run a correlation, please write a one sentence description stating how to interpret the correlation (along with the actual number you got, you can round to the first decimal) if we were to find this correlation statistically significant. Your sentence should discuss the direction and magnitude, and then describe what this means in more general terms (i.e., in relation to the two variables you see in my screenshot, food dropped and dogs going outside to go potty).  Here is a screenshot of the data set:

1-R Springer
The null hypothesis, which acts as the default or underlying assump

1-R Springer
The null hypothesis, which acts as the default or underlying assumption, is the claim that there is no effect or difference. Usually, the researcher wants to test against it.
An alternative hypothesis is a claim that suggests there is a difference or an effect. That’s what the researcher hopes to demonstrate.
Selecting the Significance Level: The threshold for rejecting the null hypothesis is the significance level, which is commonly represented by alpha. The standard selections are 0.05, 0.01, and 0.10. It shows the likelihood of rejecting the null hypothesis in the event that it is true (Type I error).
Compute the Test Statistic: A test statistic is computed using the sample data.
The kind of test statistic (such as a z-score or t-score) that is used depends on the hypothesis and the type of data. The distance between the sample statistic and the null hypothesis can be determined with the help of this statistic.
Making the Decision – Use the p-value strategy or compare the test statistic to the critical value(s) from the relevant statistical distribution (depending on the selected significance level).
Reject H0 In the event that the p-value is smaller than α or the test statistic is within the critical range.
Fail to Reject H0-In the event that the p-value exceeds α or the test statistic does not fall inside the critical zone.
Developing hypotheses is the process’s most crucial step. The entire testing process is set up at this step.The test’s findings will be meaningless if the hypotheses are not properly formulated. Properly defining the null and alternative hypotheses ensures that the test is addressing the right question and that the results will be interpretable in the context of the research objectives.
Real-Life Example of Hypothesis Testing
Consider yourself the manager of quality control at a plant that makes lightbulbs. The light bulbs your manufacturer produces have an average lifespan of 1000 hours.
Null Hypothesis: A light bulb lasts for 1000 hours on average.
An alternate theory: A lightbulb does not have an average lifespan of 1000 hours.
You measure the lifespan of a random sample of light bulbs. 
Next, you run a hypothesis test to see if there is sufficient data in the sample to rule out the null hypothesis.
The simplest part of hypothesis formulation is usually formulating the problem in terms of null and alternative hypotheses, which makes it a basic process. I have no trouble comprehending the idea of comparing two statements.
I have trouble grasping significance levels and evaluating p-values. It can be confusing to think of a p-value as the likelihood of witnessing the data in the event that the null hypothesis is true.
2-A Then
1. Hypothesis Testing Steps
1. State the Hypotheses: Formulate the null (H₀) and alternative (H₁) hypotheses.
2. Set the Criteria for a Decision: Choose a significance level (α), typically 0.05.
3. Collect Data and Compute the Test Statistic: Gather data and calculate the relevant test statistic.
4. Make a Decision: Compare the test statistic to critical values and decide to reject or not reject H₀.
Most Important Step: Collect data and compute the test statistic, as the accuracy of the data is crucial for valid results.
2. Real Life Example
Testing a new drug’s effectiveness:
• H₀: The new drug is no more effective than the existing one.
• H₁: The new drug is more effective.
• Conduct a study, measure results, calculate the test statistic, and make a decision based on the p-value.
3. Easiest and Hardest Concepts
Easiest: Differentiating between null and alternative hypotheses.
Hardest: Understanding p-values and type I and type II errors due to their abstract nature.
4. Course Progress and Recommendations
Progress: The course is engaging and manageable, though some concepts need more time.
Recommendations for Classmates: Stay consistent and practice problem-solving.

Write an original problem that can be solved using the Fundamental Counting Prin

Write an original problem that can be solved using the Fundamental Counting Principle. Then solve the problem.

I need help solving 20 statistics questions.  Please all the work in the papers.

I need help solving 20 statistics questions.  Please all the work in the papers. If not it will not the accepted.

Application Activity 1
Instructions
For this application activity, you are goin

Application Activity 1
Instructions
For this application activity, you are going to create an infographic Links to an external site. providing statistics on an area of social concern in Robeson County, NC! You can earn up to 10 extra credit points!

STEP 1: CLICK HERE Links to an external site. to explore the U.S. Census data on Robeson County, NC!
STEP 2: Select 1 area of social concern to focus your infographic on: income & poverty, education, employment, housing, etc.
STEP 3: Find one community program/non-profit organization that focuses on improving the selected area of social concern. Make sure this program/non-profit organization is located in Robeson County, NC. Google is your friend!
STEP 4: CLICK HERE Links to an external site. to find a variety of templates of your infographic! Now start creating!

FAQs:

Q: What should you include in the infographic?
A: Your infographic must consist of five parts:
1 header section that includes your name, university, class, and semester. (5 points)
2 sections of statistics that you found on the U.S. Census site for Robeson County. Sections should focus on your selected area of concern. (30 points)
1 section that provides a summary of a community program/non-profit organization aimed at improving this area of social concern. (10 points)
1 section focused on your own interest in this area of social concern. Feel free to relate your own education and/or career path to the topic. (20 points)
1 section for references. You will need a reference for the U.S. Census site and the site for the community program/non-profit organization. Any citation style is fine. (10 points)

Q: How long is the infographic?
A: An infographic is a 1-page visual display of data. This project is not intended to be an exhaustive review of data, but rather offers a snapshot – which is what statisticians often do! Infographics are great ways to present statistics related to social issues you are passionate about – especially when you are trying to convince grant funders and policy makers to invest in your programs and/or recommendations!

Q: I have no idea how to create an infographic!! How do I do this?
A: Just think of this like a really cool flyer. You will pick from a selection of templates. Then, you just edit the words and pictures. Once you are finished, you download the infographic as a PDF and submit to Canvas. Viola! Easy peasy.

Q: How much does creating an infographic cost?
F: $0! You will create your infographic using Canva – a very well-known and popular site for making flyers, announcements, posters, and more! There is an option to try Canva pro to unlock some templates. Right now, there is a 30-day free trial you can try out if you would like to. If you choose this, please remember to cancel the pro subscriiption before the 30 days is up!

Attached is the excel spreadsheet
-You and the research team will write each of

Attached is the excel spreadsheet
-You and the research team will write each of the major components of a journal article including, but not limited to the Abstract, Introduction, Data Discussion, and Conclusion
-Prepare a 1-2 page summary (1.5 space) and/or outline of what information you would like for the research team to include in each component of the journal article. Also, under the Data Discussion section,  explain how you would like for them to analyze and report the data from the spreadsheet.