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For this required peer review, you are going to perform the steps in a basic data analysis of our smartphone survey data. You are going to describe the quality of the dataset (including discussing if there are any bias or errors present), form hypotheses, perform some descriptive statistics calculations, determine confidence intervals, perform a regression analysis, and write a brief analysis report for your client. This is a big job, and it will not be easy, but is representative of the type of statistical analyses that many quantitative market researches do on a regular basis, so it will be good practice for you. A survey of use, perception, and buying behavior of smartphones was conducted in the first course of this specialization of the Coursera learners taking that course. For this assignment, you will perform descriptive, inferential, and causal evaluation of the survey data and develop your own hypotheses to test. 2. Identify bias that may exist in the data based on review of the survey and sampling methodology (you will write a brief description of sampling and non-sampling errors found in the deliverable section below). 3. Select 8 data elements that you will analyze as part of this assignment. Use the below rules to guide your selection: The data elements selected should be aligned with what you want to test with the data. Select two demographic elements that you would like to include in your analysis. From the remaining data elements, select two nominal, two ordinal, and two interval elements that you would like to include in your analysis. 4. Use Microsoft Excel, Google Sheets, or another spreadsheet software to perform descriptive analysis on the 8 elements you selected identifying total counts, central tendency, and degree of dispersion for each. If you get stuck, you can look up how those calculations can be performed in Excel here: 6. Look up the confidence intervals of the 8 elements you selected here: 7. Develop a null hypothesis and an alternative hypothesis related to one of your selected demographics and one of your selected attitudes towards phones. Find your data on the cross tabulation report from Qualtrics here: What are the degrees of freedom and chi square for the data? Which hypothesis is true in the data? Does the hypothesis make sense based on what you know of the market (is there potential for errors)? 8. Complete a regression analysis for two data elements. Write a null hypothesis and alternative hypothesis for the analysis. What is the independent variable and what is the dependent variable in your analysis? What is the adjusted R2 for the analysis? How much does variation in the independent variable explain variation in the dependent variable? What are the T-values in the analysis? Which hypothesis is true in the analysis? PART 2: DELIVERABLE Write an analysis report that describes the following: Summarize for a business stakeholder what you wanted to test in the data. Describe any bias that you found in the data due to the instrument or the sample. Identify the data elements you selected. Describe central tendencies and dispersion that illustrate aspects of what you wanted to test in part 1 or illustrate the bias you saw in part 1. Make sure to include this information for all 8 data elements selected. Report outcomes of your test of a null and alternative hypothesis between a demographic statistic and one of the other survey statistics that was in the Qualtrics cross-tab analysis. Include the hypotheses and which was supported. Report outcomes of your regression analysis with regards to the relationship of variance in the independent variable on the dependent variable and the test of the null or alternative hypothesis using the T-Variable. How to submit: Please submit your deliverable report as a single document in a PDF, Word Document (.doc, .docx), or Rich Text Format (RTF) file. Review criteria less You will be assessed upon the following criteria: A summary aimed at a stakeholder is provided A discussion of the quality of the dataset is included (bias and errors) Data is discussed in the form of descriptive statistics, including appropriate measures of central tendency and dispersion for all 8 elements chosen (2 demographic items, 2 nominal items, 2 ordinal items, and 2 interval items). Formulation of your hypotheses: i.e. they are testable and unambiguous, and are all related to the items chosen for analysis. A report of the regression analysis conducted is included and information includes: (1) identification of independent/dependent variables; (2) the percent variance in the dependent variable predicted by variance in the independent variable (the adjusted R^2), (3) the null hypothesis and alternative hypothesis are both identified; and (4) which hypothesis is supported based on the T-Values.

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