please help
old. This data was obtained from the US Census Bureau and can be found in the USCounties_Uninsured_Under19 dataset. A snip-if of the full dataset is here. Percent E State County Age Group Uninsured 1 AK Aleutians East Bo... Under 19 Years O... 15.6 2 AK Aleutians West C... Under 19 Years O... 16.4 3 AK Anchorage Borou... Under 19 Years O... 7.9 4 AK Bethel Census Ar... Under 19 Years O... 7.8 Bristol Raw Rori ! Inder 10 Veare n) 13 Q What Data to Include . For this assignments, you should consider the following states: o MN: You should include MN o WI: You should include WI O One Additional State: Pick a third state that matches whose first letter matches the first letter of your last name. E.g. Malone could pick Missouri or Montana; someone with last name Klemp could pick Kentucky or Kansas, If your last name is Unger and there is no state that starts with U, then pick something close to U like West Virginia. Complete the following tasks and/or answer the following questions using your three states. All discussions should be in context, i.e. make references to the uninsured rate of children in these states.1. In JMP, select Analyze > Distribution, place Percent Uninsured in the Y, Columns box and State in the By box. Click OK. (3 pts) Provide a screen-shot of your JMP output. 2. Compare and contrast the quantiles across the three states. Discuss. (3 pts) 3. Compare and contrast the mean and standard deviation across these three states. Discuss. (3 pts) 4. Consider the value of N under Summary Statistics. What does this value represent? (2 pts) 5. Using the boxplots provided, identify, by county name, all outliers. Specify a Label tag on County to make this easier. (3 pts) 6. Use Analyze > Fit Y by X to create a CDF plot that can be used to investigate the entire spectrum of the uninsured rate across the three states. (3 pts) Copy/paste your CDF plot that compares the three states7. Write a couple sentences about what you learn from the CDF plot. (3 pts) Next, consider the percent uninsured for all counties across the United States. The following output was obtained under Analyze > Distribution and placing Percent Uninsured in the Y, column box. A El Percent Uninsured uu A Qantil A EISummaly Statistics M \""' " ' won-x. maximum 303 Mean 5.2? 99.5% 20.6 Std Dev 3.661 97.5% 15.5 Std Err Mean 0.065 90.0% 11.5 Upper 95% Mean 6.398 75.096 quartile 7.7 Lower9596 Mean 6.141 50.0% median 5.2 N 3141 25.0% quartile 3.7 10.0% 2.8 2.5% 22 24 28 32 0.3% 1.57 0.0% minimum 1.1 8. Notice that the mean, 6.27%, is somewhat larger than the median, 5.2%. Why would we expect the mean to be larger than than the median for this data? Briefly discuss. (3 pts) Next, consider the large number of outliers on the upper-end of this distribution. Recreate the above output, and from the red drop-down menu (next to Percent Uninsured), select Save > Standardized (this will save the Zscores for each observation into your spreadsheet). Answer the following using Z-Scores. 9. A county whose ZScore is greater than 2 would be considered a potential outlier (a bad outlie as their uninsured rate is much higher than typical). Which state has the most number of potential outliers on the upper end? Which state has the second most? (4 pts) 10. A county whose ZScore is less than -2 would also be considered a potential outlier (a good outlier as their uninsured rate is much lower than typical). Are there any counties that are identified as potential outliers on the lower end? Discuss. (3 pts)
