Develop two personas for your individual design project. Use the information and samples on page 406 of the course textbook to guide you. The personas should look visually pleasing and professional. Each persona should take up one page in a word file. Submit your word file using this assignment link prior to the deadline.

Question #C.2 page 309 Chapter 7

A sportswriter wants to know how strongly the residents of Indianapolis, Indiana, support the local minor league baseball team, the Indianapolis Indians. He stands outside the stadium before a game and interviews the first 30 people who enter the stadium. Suppose that the newspaper asks you to comment on the approach taken by this sportswriter in performing the survey. How would you respond? 

Question #C.3 page 309 Chapter 7

C.3. A large corporation has 4520 male and 567 female employees. The organization’s equal employment opportunity officer wants to poll the opinions of a random sample of employees. To give adequate attention to the opinions of female employees, exactly how should the EEO officer sample from the given population? Be specific. 

Question #C.4 page 309 Chapter 7

C.4. Suppose that you want to estimate the mean monthly gross income of all households in your local community. You decide to estimate this population parameter by calling 150 randomly selected residents and asking each individual to report the household’s monthly income. Assume that you use the local phone directory as the frame in selecting the households to be included in your sample. What are some possible sources of error that might arise in your effort to estimate the population mean? 

Question #C.5 page 309 Chapter 7

C.5. Provide an example of when you might want to take a stratified random sample instead of a simple random sample, and explain what the advantages of a stratified sample might be. C.6. Provide an example of when you might want.

Question #C.2 Page 353 Chapter 8

When, if ever, is it appropriate to use the standard normal distribution as a substitute for the t distribution with n − 1 degrees of freedom in estimating a population mean?

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I’m Attaching text book for the reference, Questions are from this text book.

Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment.

* **Note:** if you having trouble displaying the maps try running `jupyter nbextension enable –py gmaps` in your environment and retry.

* Create a heat map that displays the humidity for every city from the part I of the homework.


* Narrow down the DataFrame to find your ideal weather condition. For example:

* A max temperature lower than 80 degrees but higher than 70.

* Wind speed less than 10 mph.

* Zero cloudiness.

* Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal.

* **Note:** Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number.

* Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates.

* Plot the hotels on top of the humidity heatmap with each pin containing the **Hotel Name**, **City**, and **Country**.


As final considerations:

* Create a new GitHub repository for this project called `API-Challenge` (note the kebab-case). **Do not add to an existing repo**

* You must complete your analysis using a Jupyter notebook.

* You must use the Matplotlib or Pandas plotting libraries.

* For Part I, you must include a written description of three observable trends based on the data.

* You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels.

* For max intensity in the heat map, try setting it to the highest humidity found in the data set.

## Hints and Considerations

* The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook.

* You may want to start this assignment by refreshing yourself on the [geographic coordinate system](http://desktop.arcgis.com/en/arcmap/10.3/guide-books/map-projections/about-geographic-coordinate-systems.htm).

* Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome.

* A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: [citipy Python library](https://pypi.python.org/pypi/citipy). Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors — spending hours investigating their entire code — when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you!

* Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so?

* In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes).

* Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters.

* Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities.

* Lastly, remember — this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

Clemenson Imaging LLC monitors increased revenue from the use of CT scan equipment. You analyze the number of patients and procedures by technician and location.

[Student Learning Outcomes 6.3, 6.4, 6.5, 6.6, 6.7]

File Needed: ClemensonImaging-06.xlsx (Available from the Start file link.)

Completed Project File Name: [your name]-ClemensonImaging-06.xlsx

Skills Covered in This Project

• Calculate the net present value of a purchase.
• Use TRANSPOSE to rearrange labels into a column.
• Concatenate cells to display full names.
• Use SUMIFS to summarize data.
• Calculate procedure times.
• Format times with fractions.

1. Open the ClemensonImaging-06 workbook and click the Enable Editing button. The file will be renamed automatically to include your name.
2. Determine the net present value of a new equipment purchase.
   1. Click the Financials sheet tab and select cell H5.
   2. Use NPV with a Rate argument of 4.25%.
   3. Select cells D7:D13 for the Value1 argument and click OK. This is the same as entering each value argument separately.
   4. Edit the formula to add both costs (cells D4 and D5) at the end of the formula. The net present value is $268,921.79.
3. Use TRANSPOSE and CONCAT to display technician names.
   1. Click the Technicians sheet tab. The names are in rows.
   2. Select cells A4:A10, seven rows in one column.
   3. Select TRANSPOSE from the Lookup & Reference category and select cells A1:G1 for the Array argument.
   4. Press Ctrl+Shift+Enter to complete the array formula.
   5. Repeat the TRANSPOSE task for the first names in cells B4:B10.
   6. Select cell D4 and create a CONCAT formula to display the name in first name, last name order (Figure 6-111).Figure 6-111 CONCAT formula to display names
   7. Copy the formula in cell D4 to cells D5:D10.
   8. Click the Summary sheet tab, select cell A5, and create a 3D reference to cell D4 on the Technicians sheet.
   9. Copy the formula and preserve the borders.Figure 6-112 SUMIFS with absolute and relative references
      Figure 6-113 Change time format to display fractions
4. Use SUMIFS to total number of patients by procedure and technician.
   1. Click the Summary sheet tab and select cell C5.
   2. Use the SUMIFS function with an absolute reference to cells $D$5:$D$41 on the Procedures sheet as the Sum_range argument.
      • The Criteria_range1 argument is an absolute reference to the image type column on the Procedures sheet, cells $E$5:$E$41.
      • The Criteria1 argument is a relative reference to cell B5 on the Summary sheet.
      • The Criteria_range2 argument is an absolute reference to the technician names column on the Procedures sheet.
   3. Select cell A5 for the Criteria2 argument (Figure 6-112).
   4. Copy the formula in cell C5 to cells C6:C11 and preserve the borders.
5. Use SUMIFS to total number of patients by category and location in cells C14:C15.
6. Look for and correct format inconsistencies.
7. Calculate procedure times.
   1. Click the Times sheet tab and select cell F6.
   2. Build a formula to subtract the start time from the end time and multiply those results by 24. The result is shown in hours.
   3. Copy the formula to row 41.
   4. Select cells F6:F41 and open the Format Cells dialog box. On the Number tab, choose Fraction with a Type of Up to two digits (Figure 6-113).
8. Save and close the workbook (Figure 6-114).
9. Upload and save your project file.
10. Submit project for grading.