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Refer tо the tаble which summаrizes the results оf testing fоr а certain disease. If one of the results is randomly selected, what is the probability that it is a false positive (test indicates the person has the disease when in fact they don't have it)? Round to 3 decimal places as needed.
Tаsk: Cоngrаtulаtiоns! Yоu have been selected by Chicago Mayor Brandon Johnson to join serve as a senior advisor in data analysis. The Mayor has tasked you with making recommendations on how the Divvy bike-share infrastructure can be better designed to complement the “L” rail network for car-free commuting. The Mayor believes that Divvy and the L should function as complements, with bikes solving the “first-mile / last-mile” problem. But there is concern that the current distribution of bikes does not align with actual transit demand. The Mayor is excited to announce a new vision of a car-free city designed around the Divvy bike-share program, so excited that he has called a news conference to deliver that vision 125 minutes from now. Now he needs you to give him a vision to announce. He needs a clear, data-driven assessment of how Divvy can be redesigned/reimagined/rebalanced to better support L-based commuting. When you showed up for work today he passed you a post-it note that read "Evaluate how well Divvy bike-share usage aligns with CTA rail ridership and recommend how the system should be redesigned/reimagined/rebalanced to better support car-free commuting. -Brandon" There was no prescribed methodology, leaving you free to use any defensible analytical approach, but it should go without saying that your conclusions must be data-driven, policy-relevant, and actionable. Furthermore, Chicago is operating under tight fiscal constraints. That means you may not assume recommend the expansion of system capacity or large capital investments. You are constrained to geographically or temporally reallocating existing resources only. You may also propose operational or equipment changes (e.g., transitioning toward e-bikes or scooters), but any such recommendation must be grounded in data, include a clear justification, and not change the total number of bikes/scooters in the fleet. Instead, your analysis should help answer questions like: Which stations appear to have excess supply (low usage) versus excess demand (high turnover or imbalance)? Where should bikes be reallocated to better complement L-based commuting? Are there particularly needy neighborhoods where expanded Divvy access would provide a more acute benefit? Given the close proximity of the press conference, the Mayor does not have time for a long presentation. Your final product must be readable in under five minutes and must include at least THREE distinct proposed changes to the Divvy system. It is hard to convey a great deal of information in words in such a short amount of time, so it is best to use data visualizations. This is something the Mayor has explicitly asked for in the past, so to be safe your notebook must include at LEAST one visual aid per proposed system change. These could be maps, charts, graphs, or formatted tables. Whatever they are, these must be clearly labeled, easy to interpret, and directly tied to your conclusions. You are welcome to use any data you deem relevant to support your analysis, but it is likely that you would find the following datasets particularly useful: Divvy Bike Share Trip Data CTA “L” Station Ridership Link to some possibly useful files Deliverable: You have 120 minutes to submit a Jupyter notebook that functions as a professional policy memo. Your notebook must include markdown cells explaining specific recommendations for a redesigned/reimagined/rebalanced Divvy program, as well as a description of the data and methods you used to generate those recommendations. Code cells should be used to generate at least three visualizations that convey the primary findings of your analysis. Your notebook should read like a finished analytical report, not a rough draft. That means you should use clear section headings, write in well-structured sentences, remove unused code and irrelevant output, and ensure tables and figures are readable and properly labeled The goal is not to produce a perfect model. The goal is to demonstrate that you can: work with real-world data under time pressure identify meaningful patterns and produce a clear, defensible recommendation about how to improve a transportation system This is not just a coding exercise. You are being evaluated on your ability to produce a clear, concise, and professional analytical memo that communicates effectively and uses evidence appropriately to support a feasible policy recommendation. Grading Criteria: Clear and Concise Writing (20%): The memo must be well-organized, free of grammatical errors, and concise enough to be read in five minutes. Write in complete sentences and fully developed paragraphs. Use a clear, formal, professional tone. Avoid casual language, slang, or conversational phrasing. Do not write as if you are thinking out loud. Be direct, professional and precise. Every paragraph should make a clear point that is supported by your analysis. Each section should connect to the next and claims should follow directly from evidence. The reader should never have to guess what you are trying to say. Effective Visualizations (30%): Visual aids should be intuitive, well-designed, and critical in supporting your written analysis. Each visual must include titles, labels, and annotations where necessary. Use readable, properly formatted variable names, without underscores or raw variable names in final visuals. Include legends (where appropriate), readable scales, and appropriate units Visuals must be interpretable without additional explanation, though each visual must be explicitly referenced and explained in the text. Quantity and Depth of Insights (30%): Each recommendation must be data-driven and significant, allowing the Mayor to articulate a clear vision for the future of the Divvy bike-share program. Accurate Analysis (20%): All conclusions must be rigorously supported by the data. Misinterpretation of data or unsupported claims will affect the credibility of your analysis and your score for this category.