How different is the cost of living in United States? (I agree with the professor on the suggested research question because you can produce a lot of data collection on this. Now you can change this question to How different is the cost of living in United States for women or people of a specific age range (18- 24)?
And take look at how the cost of living fluctuates across the United States? Tell your readers on why this information is important and perhaps if you would like to move, maybe these places are options.
You have to research the cost of living of some states on the east, west, north, and south coast and graph that data. Use that same information and place it on a confidence interval (the bell curve chart), and I would add the mean, mode, and median of the data collection as well as further data collection.
After you finish that you can look at the data, notice any fluctuations or averages or any un expectations. In addition, you can talk about unknown variables that may impact the data of the cost of living such as how often individuals eat out, do they have kids, do they underlying medical conditions, salary restrictions, or does their education level impacts their ability to move up in the society.
You measure the change of the cost of living between different locations which can give you an index. Once you get the data that you need and format it on a bell curve ( I forgot how to do that) then it’s smooth sailing with the assignment.
Outline of the Final Project The goal of submitting an outline of the final project is to make sure that you are making satisfactory progress in completing your final project. If you are working in a group, all members are responsible to identify which techniques can be used to address any portion of your research question(s). For instance, if my research question is what are the characteristics that customers look for in selecting a coffee shop? Perhaps I want to check to see if there is a significant difference in customers’ rating between having a seating area or non-seating area or perhaps checking to see if customers prefer domestic rather than imported coffee. Now, the question will be whether to use a chi-square test or t-test. The answer will depend on the dataset you are working with. The following are the sections expected in your outline. 1. What dataset have you selected? Why? 2. What is your goal of your research paper? Write at least one research question. Research questions cannot be answer with a simple Yes or No. Please remember that if you are using one of the provided datasets, you are limited to address questions that can be answer using the available information. 3. Consider techniques we have covered so far (see list below). List the techniques you are planning to use and what are you trying to address by using that technique. Identify which variable(s) in your dataset you will using for each technique. Remember, all selected techniques should support your exploration of the dataset to answer your research question. There should be a bullet for each technique along with your explanations. Please be advised that some of this information will be included in the Exploratory data analysis and Methods section of your final paper. a) Use frequency distributions, crosstabulations (contingency tables), and graphs to describe categorical variables b) Conduct a chi-square test c) Use of exploratory data analysis to summarize quantitative variable d) Include a confidence interval to estimate a population mean e) Include a confidence interval to estimate a population proportion f) Identify an opportunity to answer a question using a t-test This outline will be a roadmap to follow to address your research question(s) and will result in much better organized final papers. It will give me the opportunity to check that you are selecting the right techniques for appropriate reasons. At any point, we can have an online meeting to address any questions or concerns you may have about the outline or the final project. Applied Data Analysis – Final Project The final project requires each group or individual to perform statistical analysis of the selected multivariate data set and to produce a written report detailing the analysis techniques and the findings of the project. The outline of the project follows: · Groups: Students are allowed to work in a group of up to three students. Groups are required to submit a memorandum detailing the contributions of each member. · Data Set: Students may use their own data set (from work or internship) or may obtain a data set from an external source. Groups may approach the instructor if they encounter difficulty finding a data set to use. The data set must have multiple variables (quantitative and qualitative) to perform various data analysis techniques covered in class. · You must have your data set pre-approved by the instructor before beginning the final project. It’s better to get the data set approved sooner rather than later, just in case the data set is denied. To get your project data approved, students need to write up a one-paragraph summary of the data set, including information about the size of the data set and the research questions you will try to answer. · Analyses: The data analysis methods utilized need to be compatible with the research goals and the data. Assumptions implicit to the methods need to be evaluated. If assumptions have been violated, it is OK to proceed with analysis, but you must state the limitations of the results. · Report: Your report must be double-spaced, 12-point font size, and be at least 10 pages long (this includes your graphs and tables) using MS Word. Your project will be evaluated on the “quality” of your analysis, not the quantity. The reader should be able to understand your conclusions and know how you arrived at the conclusions. The following is one way to organize your paper, but feel free to do what works best for you, just make sure that the organization is easy to follow. · Abstract—State the main results of the paper (not the objectives) and possibly the methods used. It should be short; four or five sentences is probably enough. · Introduction—A brief (approximately one page) general description of the problem, including: · Why the problem is of interest—you might refer to previous studies using this, or similar data sets. · Information about the data set, e.g, the source of the data, how many observations, number of variables, etc. · A brief summary of the methods utilized—tell me what methods you have utilized, e.g., “We perform a t-test to examine the mean difference of sugar content among cereals across the shelf on which they are displayed.” Detailed information about the methods should be left for a later section. · The main results of your analysis. · Exploratory data analysis—The introduction describes the data set and gives the a brief taste of the results that will be presented later in the paper. This section should provide the reader with graphical and numerical summaries of the data, paying special attention to summaries that provide evidence for the results you have mentioned in the introduction. · Methods—The methods section should include an explanation/justification of the techniques used and address any assumptions made. Topics that should be covered in this section include: · If appropriate, explicitly define any techniques used in your analysis (e.g., t-tests, chi-square tests, ANOVA, etc.). Make sure to state why you think the methods are appropriate for the data. · Discuss and evaluate the assumptions of the method(s) used. If your data does not quite conform to the assumptions, make note of it, and discuss the implications. · State the hypotheses you are testing, both in terms of the applied problem, and in terms of the statistical techniques used; also state which testing procedure(s) you are using. For example, “We perform a hypothesis test to determine whether the mean difference of sugar content among cereals on two different shelves, H0: µ1 = µ2 Ha: µ1 ≠ µ2 · At minimum, your project should include the following analysis techniques: 1. Use frequency distributions, crosstabulations (contingency tables), and graphs to describe categorical variables 2. Conduct a chi-square test 3. Use of exploratory data analysis to summarize quantitative variables 4. Include a confidence interval to estimate a population mean 5. Include a confidence interval to estimate a population proportion 6. Identify an opportunity to answer a question using a t-test 7. Identify an opportunity to answer a question using ANOVA test 8. Conduct tests to check assumptions 9. Use a correlation matrix that shows the correlations between all pairs of quantitative data along with a scatterplot, correlation coefficient, and linear regression model for the two variables you explored in detail. · Detailed Results—This section expands the explanation of the results, and includes, where appropriate tables and figures providing evidence for the stated conclusions. You can also report any secondary results. · Discussion—Summarize the findings one last time, paying close attention to the limitations of the analysis. You can share thoughts with the reader about how you might expand the study, improve on the techniques you have used, and the long-term implications of the findings. · Grading: Your score on the final project will be comprised of the following: – Clarity, 20%. If I scratch my head and ask myself, “What the heck are they trying to say?” several times when reading the paper, then it’s probably not very clear. Delete long sentences with complex structure in favor of ones that are relatively short and easy to understand. · Appropriateness of the method(s), 25%. This is the most important part of the project. When writing this portion of the project, you should ask yourself, “Is there a better way to perform this analysis?”, and “Have we consider all assumptions?” · Ability to draw the correct conclusions, 25%. You have used the right method(s) for data analysis. Did you use that method correctly to draw conclusions? Are your conclusions well supposed by your results? · Thoroughness, 20%. Did the group perform a complete analysis? Was an adequate exploratory data analysis performed? Is there something in the data that the group failed to discuss? Were all assumptions discussed and evaluated? Were limitations of the method(s) discussed? Did the group examine the importance of each variable? · The wow factor, 10%. Extremely well-written papers will be rewarded. Did the group go beyond expectations? Is the paper extremely well-written? Did the group suggest ways to extend the work or how the analysis could be improved? · Submission: All project must be formatted and ready to be printed. Make sure that all tables and figures display within the page margins. You must submit your report as a Microsoft Word document. Please upload your final paper in Canvas.