by admin | Nov 22, 2022 | Statistics
1. Create Your Dataset:
o Select a Population of Interest: The possibilities are endless. You could study sports teams, countries/states/counties, musical artists/albums, political figures, celebrities, CEOs, streaming services, national parks, etc. So long as you can find a variety of information about each observation using the Internet, you can study it.
o If Needed, Choose a Sampling Method and Select a Sample: Be specific about your population and decide whether you can study the whole population or if you’ll need to draw a sample. For example, if you are only interested in studying NFL quarterbacks who played in a regular-season game during the 2021 season, you might be able to create a dataset with of the whole population. If however, you are interested in all NFL quarterbacks who have ever played, you’ll need a way of selecting a sample (e.g., random sampling).
o Choose Variables to Collect: Whatever you choose to study, you’ll collect a variety of pieces of data about each. For example, if you were composing a dataset of NFL quarterbacks, you could record a variety of continuous (e.g., annual salary, average passing yards) and categorical (e.g., whether they are right- or left-handed) variables about each quarterback. Think ahead about what type of analyses you’d like to be able to perform. For example, if you want to look for correlations, you’ll need continuous variables. If you want to compare groups, you’ll need categorical variables that can be used for grouping. Locate the data using the Internet and organize it into a dataset using Microsoft Excel.
o Create a Codebook: Following the instructions in the tutorial and using appropriate statistical vocabulary, create a codebook describing each variable in your dataset. Include this as an appendix in your final deliverable for reference.
o Save your Dataset: Remember, you can’t save multiple worksheets using the comma separated values (.csv) file format. As such, save your dataset as a .csv file and also save your codebook in a separate, Excel workbook. Include your dataset as an appendix in your final deliverable.
2. Perform the Analysis using JASP: You’ll need to include your full set of JASP outputs as an appendix for your final deliverable. Be sure to save all JASP outputs as you perform them.
o Descriptive Statistics: Fully analyze the descriptive properties of your dataset. Produce frequency/descriptive statistics for each variable and look at the shape/spread/skew of the data. Investigate possible outliers.
o Inferential Statistics: Chapter 16 of your textbook reviews how to select analyses appropriate to the variables and describe the results of those findings. Use that chapter to select analyses appropriate to your research questions and variables. Perform inferential analyses using JASP and interpret the p-values in light of your research question and/or predictions.
3. Prepare Your Report: Using Microsoft Word, create a report of your project that includes the following sections.
1. Introduction: Summarizes the scope and purpose of your project. Explain why you chose the project and what you hope to learn from the data you compiled. Be sure to specify your main research questions. Summarize and cite the article you selected for the Week 5 Deliverable and use it to contextualize your topic.
2. Description of Sample & Data: Describe the sample and/or population studied. In detail, explain the rationale and sampling method used to obtain the data. Using accurate statistical vocabulary, describe each variable studied and a rationale for why it was included. Include an APA-formatted in-text citation and reference for each source (e.g., website, agency) from which data was obtained.
3. Descriptive Analysis: Include a descriptive statistics section similar to the descriptive statistics report you prepared during Week 3 but also integrating more recent concepts (e.g., confidence intervals, boxplots/outliers). Provide both detailed verbal interpretations and data visualizations to familiarize your reader with the dataset.
4. Inferential Analysis: Include an inferential statistics section reporting interesting and/or significant findings from your data. For each inferential analysis you perform, include the following: your predictions/hypotheses and rationale for your predictions, a justification of the appropriateness of the statistical test based on the characteristics of the variables analyzed and research question being tested, specifics of the analysis performed and variables involved (e.g., which variables were treated as outcomes/predictors), and verbal interpretation of results including applicable statistics. If possible, also include a data visualization of your findings (e.g., bar graph, scatterplot).
5. Discussion: Conclude with a one- to two-paragraph summary of your project with a focus on the findings and their application. Consider possible implications (e.g., for personal decision making or policy) of your project. Remember, your job, as the statistician, is not just to state the statistical findings but to interpret them for your reader. Think critically, as a statistician, when offering interpretations for trends in your data.
6. References & Appendices: At the end of your deliverable, include APA-formatted references to all websites and other resources from which data were obtained as well as any articles referenced. Also, include an appendix section and include your codebook, dataset, and full JASP Outputs. Note, the instructor needs these materials to assess the accuracy of your statistical decision making and verbal interpretations.
by admin | Nov 22, 2022 | Statistics
calculating the main data from the project comes from this article,
https://www.pewresearch.org/fact-tank/2022/10/05/more-americans-are-joining-the-cashless-economy/ I have completed questions 1 . there are links in the question to take you to the useable tool to get the answers. if not this site will help with the math involved and it does not need a login. https://www.utdanacenter.org/our-work/higher-education/curricular-resources-higher-education/dcmp-data-analysis-tools
by admin | Nov 22, 2022 | Statistics
Pleas find in the enclosed folder the assignment requirements.
You are required to submit the document file (typed or handwritten) containing the solution via moodle by October 12th at 3:00 p.m. Show in all cases the formulas you used (do not merely copy the final result from your calculator, but show clearly how the result has been obtained).
Please note that if you take pictures of your handwritten notes, they must be submitted as a single pdf file, containing all the pages in order. The filename must contain the surname(s) of the author(s) of the assignment.
Please be advised that a cut-off time has been set for this assignment and late submissions will not be accepted/graded.
by admin | Nov 21, 2022 | Statistics
Hello, please edit the answer according to the hints on the home page when you are doing the question. The screenshot is in the correct format as requested by my professor. Most of the answers can be found online, such as Chegg. But I want the questions that require text answers to be written in the correct form that my professor requires.
by admin | Nov 21, 2022 | Statistics
Use the Word document below and follow the instruction carefully to complete the assignment. Use this document to type in your answers and show the work/process/rationale supporting the answers. The Excel document contains the data sets that are also included in the Word document.
by admin | Nov 21, 2022 | Statistics
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriiptive statistics and graphs provided.
Directions
Using the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate Data Spreadsheet spreadsheet (both found in the Supporting Materials section) for your statistical analysis.
Note: Present your data in a clearly labeled table and using clearly labeled graphs.
Specifically, include the following in your report:
Introduction
Describe the report: Give a brief descriiption of the purpose of your report.
Define the question your report is trying to answer.
Explain when using linear regression is most appropriate.
When using linear regression, what would you expect the scatterplot to look like?
Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variables.
Data Collection
Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).
Identify your predictor and response variables.
Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.
Data Analysis
Histogram: Create a histogram for each of the two variables.
Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.
Interpret the graphs and statistics:
Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.
Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?
Develop Your Regression Model
Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.
Based on your scatterplot, explain if a regression model is appropriate.
Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.
Identify any possible outliers or influential points and discuss their effect on the correlation.
Discuss keeping or removing outlier data points and what impact your decision would have on your model.
Find r: Find the correlation coefficient (r).
Explain how the r value you calculated supports what you noticed in your scatterplot.
Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.
Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.
Interpret regression equation: Interpret the slope and intercept in context.
Strength of the equation: Provide and interpret R-squared.
Determine the strength of the linear regression equation you developed.
Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.
Conclusions
Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.
Did you see the results you expected, or was anything different from your expectations or experiences?
What changes could support different results, or help to solve a different problem?
Provide at least one question that would be interesting for follow-up research.
