MAT 133 Milestone One Guidelines and Rubric Overview: The final project for this

MAT 133 Milestone One Guidelines and Rubric
Overview:
The final project for this course is the creation of a research study report. For the first milestone, you need to. select an appropriate study from the
Final Project Research Study Options document found in the Module One Reading and Resources. Then you will draft the first of three sections that will make up
your report.
Prompt: Draft the “Introduction” section of your research study report, which includes the following critical elements:
I. Identify the specific focus of the research. In other words, what was this study about?
II. Explain the purpose of the study. What was the study trying to achieve?
III. Describe the specific characteristics of the group being studied. What was the population? What was the sample size? What were its demographics?
Submit your Milestone One submission to the assignment page in Module Two. You will be graded based on the rubric information below.
 You will also be sharing your ideas for your introduction to the discussion board for feedback. Make sure to respond to your peers with thoughts and
information to help them improve their work for the final project submission (later in the course).
Rubric
Guidelines for Submission: Your draft of the “Introduction” section of your research study report should be 1 page in length (plus a cover page and references)
and must be written in APA format. Use double spacing, 12-point Times New Roman font, and one-inch margins. Cite all references in APA format.
Note: This rubric is tailored to this assignment and awards full points for “Proficient.” For the final project, you will need to demonstrate “Exemplary”
achievement to earn full points. To see how you will be graded on your final project, review the Final Project Guidelines and Rubric document (in the Assignment
Guidelines and Rubrics section of the course).

Prior to beginning work on this discussion forum, read Chapter 6 and Chapter 7 i

Prior to beginning work on this discussion forum, read Chapter 6 and Chapter 7 in the course text, Chapter 6 and pages 129 to 142 in Chapter 8 of the Jarman (2013) e-book, the Instructor Guidance for Week 4, and watch the ANOVA: Crash Course Statistics #33 (Links to an external site.) and ANOVA Part 2: Dealing with Intersectional Groups: Crash Course Statistics #34 (Links to an external site.)
videos. You may also wish to review the information about inferential statistics from Week 3. In this discussion, you will evaluate a research question and determine how that question might best be analyzed. To do this, you will need to explain statistical concepts and assess assumptions, limitations, and implications associated with statistical tests.A researcher wishes to study the effect of cognitive behavior therapy on young people diagnosed with depression. Consider and discuss the following questions as you respond:
Would you recommend using a z test, a t test, or an ANOVA for the analysis? Explain your answer.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
Would the hypothesis be directional or nondirectional?
Would the test be one or two tailed?
How would the null and alternative hypotheses be worded?
Resources:
ANOVA: Crash Course Statistics #33 – YouTube
ANOVA Part 2: Dealing with Intersectional Groups: Crash Course Statistics #34 – YouTube

Consider the 2020 Census. The purpose of a census is to gather information about

Consider the 2020 Census. The purpose of a census is to gather information about all people living in the United States. Once the data are collected, they can be used to describe and infer information about the population.
Respond to the following in a minimum of 175 words:
Given what you have learned so far in the course, what are some ways you could use your statistics knowledge to work with census data?
What limitations would you have if you were using the simple procedures addressed in Wks 1-4 of this course?
What additional statistical knowledge might be helpful to enhance your analysis?

Choose either topic #1 or topic #2 for your initial post. Respond to either for

Choose either topic #1 or topic #2 for your initial post. Respond to either for your peer reply post.
Topic 1:
One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression.
It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below.
Here we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capital consumption of margarine. Does this imply that all married couples in Maine should immediately stop using margarine in order to stave off divorce? Common sense tells us that is probably not true.
This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption but, it is not a causal link. The appearance of such a relation could merely be due to coincidence or perhaps another unseen factor.
What is one instance where you have seen correlation misinterpreted as causation? Please describe. This serves as your initial post to the discussion (if you choose topic #1) and is due by 11:59 pm EST on Saturday.
-OR-
Topic 2:
Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction.
For example, given this data on literacy and undernourishment, we can create a scatter plot which shows that there seems to be a relationship between the variables.
The graph implies that as literacy (x) increases, the percentage of people who are undernourished (y) decreases.
We can calculate a best fit line equation and use this to predict that the undernourishment rate we would expect in a country with a percentage literacy rate of 87% would be y = (-0.5539)(87)+55.621 or about 7.43 percent.
What is one instance where you think linear regression would be useful to you in your workplace or chosen major? Please describe including why and how it would be used. This serves as your initial post to the discussion (if you choose topic #2) and is due by 11:59 pm Eastern on Saturday.