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Created: Aug 26, 2016
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Week1 Report - jacob.moore24
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Section 1-1

The folloing are graphics generated from the data set on insurance coverage.  This first pie chart reprents all the data from the study.

The attached result is not available.

The next pie chart represents health insurance coverage for individuals with high income.

The attached result is not available.

And finally this pie chart represents health insurance coverage for indivicuals with low income.

The attached result is not available.

Section 1-2

In this graphic, we see generated two types of box plots comparing the times of relief reported by patients with respect to their gender.  In both data sets we see that the data is skewed to the right.  We can also see that men on avearge reported a longer releif time compared to women.  This observation is confirmed by the confidence interval displayed below the graph.  It is important to note that the data below only applies to the sample.  Hypothesis tests would be required to make inferences about our population.

The attached result is not available.

The attached result is not available.

Section 1-3

Below is a series of four box plots representing the levels of bacteria on physician's coats throughout a particular shift.  The second graphic shows the averages of each time with a 95% confidence interval.

The attached result is not available.

The attached result is not available.

The data shown is significant for an increase in average bacterial contamination throught the day.  Once again, conclusions can only be drawn at this point on the sample itself.  Of particular interest is the sharp increase from the start of day to before lunch.  This observation fits with my own personal expereinc of doctors doing the majority of rounding on their patients in the morning at the start of their day.

Section 1-4

*Part A

Below is a histogram describing the relationship in our data between age of the patient and whether or not the patient was intubated.  Of interest is patients who were either very young or very old were the most likely to be intubated.  Middle aged patients were intubated with relative lower frequencies.

The attached result is not available.

 *Part B

The attached result is not available.

In this section I am comparing the above graph with the histogram from part A (results 8 and 9).  The two graphs show different distributions in the patient's ages compared to whether they were intubated or had undergone a prior surgery.  When considering these results it is possible the the two variables could be confounding.  Stratification of the two variables should be considered for the purpose of creating inferences without bias.


For this section I was curious to see if intubation was a predictor of patient death after undergoing the resection procedure.  The following shows the respective proportions:

The attached result is not available.

As it turns out, none of the patients who were intubated died follwing the procedure.  The patients who were intubated did have increased risk of death.


Of final interest to me was to see if there was a relationship between age and the occurrence of past surgery.  Based on the histogram below, there does seem to be a relationship between age and previous surgery.  As expected, the older the patient the higher the incidence of undgoing previous surgery.

The attached result is not available.





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By michael.mccormick0254
Aug 29, 2016

Jacob on result #7 you interpreted the data as a increase in bacterial contamination throughout the day, and that you could only draw this conclusion about the sample.I believe the T confidence interval data allow us to make some generalizations to the population. It has been hard for me this first week of class to think of the sample and population as separate entities.
By nku.katie.waters
Aug 29, 2016

Hi Jacob,
Good work on your first report! A few comments:
1. For Problem 3, you were asked to create side-by-side boxplots for the variable "Bacterial Count" and use the "Group By" option to group by "Time of Day". (It is easier to compare four boxplots than it is to compare four histograms.)

2. For Problem 4, you are on the right track with the two-sample proportions test. However, there is a typo in your "Success = Yes" statement.

3. Pie charts, boxplots, histograms, and summary statistics are all descriptive statistics which means they apply to the sample only and we cannot use them to make generalizations about the entire population. When discussing these things, consider using phrases like "In this sample" or "the results seem to suggest" in your explanations of descriptive statistics. This helps avoid sounding like you are generalizing about the entire population. You should avoid using the word "shows" as it is a too strong.

4. It's important to realize that the confidence intervals DO allow us to generalize about the entire population (not just the sample). Please see the solutions for some interpretations of confidence intervals applied to problem 1-4. It is appropriate to interpret these more generally as applicable to the population of interest.

5. For Problem 1-3, a clever trick that you can do is order your boxplots (Start of Day, Before Lunch, After Lunch, End of Day), by selecting "Edit" -> "Orderings" -> "Add New Ordering". After you have created the ordering, you can recreate the boxplot and they should appear in order. Unfortunately this ordering does have to be recreated if want to use it again for a future session of StatCrunch.

Please review the solutions and let me know if you have any questions!

Always Learning