I need to make a project about the data I have in excel
MG220-01 DATA ANALYSIS Spring 2022 Data Analysis Final Project - Due on Monday, May 2nd 2022 Instructions: For Homework # 1, you developed a general framework for a data analysis project to examine a question or problem that is interesting to you (and presumably, that you feel should be of interest to others). In this final assignment, you will apply the concepts, tools, techniques, and skills we’ve learned over the past several months to advance your initial project idea from its early and conceptual state to a more robust data-centered state. In short, you are taking a project you are interested in and continuing to build on this with the new knowledge you’ve acquired; hopefully you will continue building on this project in future classes as you learn new techniques and have greater access to more data that is relevant to your interests. Your final project will be a memo (see example memo formats on BB) that had the following main sections: 1. Introduction / Overview This section will set the stage for the reader; you will describe the background of the problem or issue you are examining, why you care about this, and why we should care. 2. Statement of Hypothesis(es) In this section, you take the abstract / conceptual problem explained in Section 1 above and convert it into concrete measures that your data analysis can explain, “prove”, or “disprove” with a certain level of confidence. [Please note: Truly proving or disproving something takes a lot more analysis that we can give right now; “establishing with confidence” some assertion might be the better way to describe what you are doing here.] The hypothesis or hypotheses you develop here (which relate directly back to the problem or issue described in # 1) will be tested in the next step with your data and analysis tool(s). 3. Data & Analysis Methodology In this section, you describe the data that you have gathered to test the hypothesis/es in # 2 above. This should include the source of the data (website, database, survey, etc.) so that anyone can recreate your results using the same data. Key variables from the dataset should also be described. Also – you should describe the analysis tools that you used to test the hypothesis(es) – such as ANOVA, 1-sample or 2-sample t-test, regression, etc. – and explain why these tools are the appropriate test for the stated hypothesis/es. Also – did you use Minitab or Excel? 4. Results Here you should include a printout / screen capture of the results (with the hypothesis, the results that matter, and the p-value(s) of the test(s) as applicable. Explain specifically what the results indicate, as related to your hypothesis/es. Do not assume the reader knows how to interpret a p-value or regression equation. 5. Discussion and Conclusion In this section, you quickly recap the problem and what you were testing, how you tested it, and what the testing revealed (the results). You could include here some thoughts on limitations of your analysis (sample size was only 25 subjects, or this sample only reflects DSU students). Suggestions for future analyses that account for these limitations might be included here. For example… I am interested in the topic of climate change, and I would like to satisfy my curiosity and crunch the data for myself and draw my own conclusions about what is really going on. In Part 1, I would build up this story and why I think this is important. In Part 2, I would establish a direct connection between the phenomenon being studied (climate change) and how parts of that phenomenon could be observed and measured (data captured over time on ocean temperatures, land temperatures, size or thickness of the polar icecap, rainfall amounts, etc.). I would formalize this/these connection(s) with a set of hypotheses that I can then test: e.g. Climate change will be evident in changing (higher?) average temperatures: Null Hypothesis (H0): Average land temperatures are constant from past to present Alternative Hypothesis (H1): Average land temperatures are different from past to present -or - e.g. Climate change will be evident in changing (lower?) rainfall amounts Null Hypothesis (H0): Average rainfall amounts have been constant from past to present Alternative Hypothesis (H1): Average rainfall amounts are different from past to present In Part 3, I would explain that I obtained a set of data (Solar Radiation file from NBER which includes weather data for US States and territories from 1991 – 2010) that can be used to test these hypotheses, and give the data source (give cites and web address if applicable, so reader can find and recreate your analysis). I would also explain the details of the dataset (number of observations, timeframe, key variables related to your hypotheses, etc.). Finally, describe the particular analysis method(s) that would be used to test your hypotheses. As we learned in class, if you are simply comparing the mean of the sample to a hypothesized mean, we use a 1-sample t-test. If comparing 2 separate samples (such as mean values from one year with another year), that would be a 2-sample t-test. If comparing multiple groups, ANOVA might be appropriate. And if trying to predict a value, or determine the influence of a particular variable on an outcome, regression might be the appropriate tool. You may decide a combination of tools is needed. Part 4 – Results – is pretty straightforward. You could just include the results and/or graphs from the various tests (copy these parts from Minitab or Excel and paste directly in your document, or create separate tables from the results). Make sure that you include the p-values, clusters, etc. that are relevant to getting your answer. What do these analyses and p-values say about your null and alternative hypotheses? Part 5 - Discussion – here is where you bring the technical analysis part back to the original question. I wanted to understand climate change more in depth, and my analysis of the NBER Solar Radiation dataset provided some interesting insights. First, these data suggest that while mean temperatures increased slightly between the decades, this was not a statistically significant change based on the p-value of xxxx. Also, the data from this sample showed that rainfall amounts were not statistically different, so I cannot conclude that rainfall decreased in the US and its territories from the 1990s to the 2000s. etc. etc. An interesting finding was that there appeared to be a significant jump in solar radiation received between 1997 and 1998 (see the graph on ANOVA for sunrad and year); further research will be conducted to determine the cause of this increase. Additionally, the average humidity seems to decrease, etc. etc. etc. You can also attach your data tables, graphs, charts, results, etc. as appendices or in the body of the document. The main thing to do is tell a story that makes intuitive sense, and is explained somehow by the data set you’ve chosen to analyze (whether your analysis supports your conclusion or not). This may be your first attempt at a data analysis project, so give it your best shot and let me know if you have any questions. GOOD LUCK and ASK FOR HELP IF NEEDED! 2 Sheet1 SeqstatedecadeyearpopulationrateP_allrateP_burglaryrateP_larcenyrateP_motorrateV_allrateV_assaultrateV_murderrateV_raperateV_robberytotP_alltotP_burglarytotP_larcenytotP_motortotV_alltotV_assaulttotV_murdertotV_rapetotV_robbery 1Alabama2000s200044471004059.7906.92864.8288486.2317.27.433.3128.21805394033112739912809216201410732914825702 2Alabama2000s200144689123876.8909.42685282.4438.2274.18.530.61251732534064211999212619195821225037913695584 3Alabama2000s200244788964027.8950.62767310.14452686.837.2133.11804004257812393213890199311200230316645962 4Alabama2000s200345037264046.4960.22754.1332.1429.2251.76.636.8134.11822414324512403914957193311133829916566038 5Alabama2000s200445253754029.39872732.4309.9427249.45.638.5133.51823404466612365014024193241128625417426042 6Alabama2000s200545483273900955.82656289433248.38.234.4141.71773934347312078013140196781129337415646447 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