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Statistics · Studypool

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Post ID: 3010397

Overview

For this assignment, you will work in groups. You are required to come up with a **work product in the form of a technical report **which is a summary of the inferences made from data and requires a synthesis of many of the concepts in this course.

The goal for this assignment can be framed as an overarching question: what can you learn about a population from a sample dataset using the LOs from this unit? For example, start with what you want to learn from your dataset, then think about which statistical tools to apply to address the questions. You can also work backward: think about which statistical tools could be applied to the dataset and what conclusions can be drawn from such analysis. Use the **Report Template **to prepare your final assignment as a technical report. For the statistical analysis, use **ipynb file **and each group will choose one unique dataset from available datasets to work. In particular,

  1. You will work in groups; each group member must summarize their contributions at the end of their submitted work.
  2. The goal of this assignment is to answer an overarching question: “what can you learn about a population from a sample dataset?”.
  3. Write a succinct report by synthesizing different statistical concepts of this course.
  4. You may use codes from previous assignments.
  5. Submission instructions:
  • The primary source is a well-formatted PDF:
  • Use above Report Template.
  • All extra calculations, narrative, symbolic equations may be added in an appendix.
  • There is no page-limit for the appendix which may contain information such that the reader could replicate your calculations but should be as succinct as possible.
  • The secondary sourceis the .ipynb file along with the .csv file submitted in a single zip folder.
  1. Make sure that your answers provide enough evidence in support of the following LOs in IDS-103 Statistical Intuition and Applications which will be assessed in the final assignment.

**Examples of Research Questions: **

Each group in the class will work on only one dataset and your instructor will ensure that two groups are not working on the same dataset. Choose one from the following datasets in your ipnyb file these are also indicated below with suggested research questions.

DataSet-1: Sleep Health and Lifestyle

  • Do males and females differ in stress levels? (or physical activity or BMI category)
  • Is there a statistically significant difference in the quality of sleep between doctors and nurses?
  • What are the 95% and 99% confidence intervals for the duration of sleep for the different occupations for males in the population, assuming random sampling?
  • Perform your hypothesis testing at a 5% significance level.

DataSet-2: Healthcare Insurance Expenses

  • Do insurance charges differ between males and females?
  • Is there a statistically significant difference in the insurance charges between smokers and nonsmokers?
  • What are the 95% and 99% confidence intervals for the BMI of individuals in the different regions in the population, assuming random sampling?
  • Perform your hypothesis testing at a 5% significance level.

DataSet-3: Online Education System – Review

  • Do males and females engaged in online education differ on their average study time?
  • Do males and females engaged in online education differ on their academic performance?
  • Is there a statistically significant difference in the proportion of satisfied students engaged in online education between urban and rural areas?
  • What are the 95% and 99% confidence intervals for each level of satisfaction in the population, assuming random sampling?
  • Perform your hypothesis testing at a 5% significance level.

DataSet-4: Online Shop Customer Sales

  • Is there a statistically significant difference in the [average] number of online purchases between males and females?
  • Is there a statistically significant difference in the number of purchases between males and females in the population?
  • What are the 95% and 99% confidence intervals for the total sales in the different payment methods?
  • Perform your hypothesis testing at a 5% significance level.

DataSet-5: Airline Passenger Satisfaction

  • Is there a statistically significant difference in the level of satisfaction with airline services between males and females?
  • Is there a statistically significant difference in the proportion of satisfied passengers between Economy and Business classes?
  • What are the 95% and 99% confidence intervals for the different flight services for females in the population, assuming random sampling?
  • Perform your hypothesis testing at a 5% significance level.

LO Guidelines

  1. You are required to make strong applications of the targeted LOs (#variables, #descriptivestats, #compprogramdesign, #visualizations, #distributions, #inferentialstats) that are relevant to the approach you have chosen.
  2. Make the most of these applications! A high score for any of these LOs would reflect a direct and thorough explanation, justifying each decision or calculation that you made and fully interpreting the result.

What submission must include?

  1. PDF of the report in which text is searchable
  2. ipynb file along with csv file

**Report Finalization: **

In approximately 250 words, each student in the group describe his/her specific role within the assignment. You need to cover the following points:

  • Detail the parts of the research where you made significant contributions.
  • Highlight any special skills, techniques, you applied to the project and their value.
  • Explain how your work contributed to the overall goals of the assignment.
  • Discuss any challenges you faced and describe how you helped overcome them.
  • Describe how you collaborated with others.

    • Appendix:

    You may provide here calculations, codes, snapshots, extra work (histograms, plots, analysis, technical details) which helped you to complete this report.

    • CompProgramDesign: Generate working programs in a computer language that can solve computational problems; find and fix bugs that appear in them.
    • Variables: Identify and classify the relevant variables of a system, problem, or model.
    • DescriptiveStats: Calculate and interpret descriptive statistics appropriately.
    • Correlation: Apply and interpret measures of correlation; distinguish correlation and causation.
    • Distributions: Identify different types of distributions and make inferences based on samples from distributions appropriately.
    • Visualizations: Interpret, analyze, and create data visualizations.
    • InferentialStats: Apply and interpret confidence intervals, statistical significance, and regression.