Stats project compiled

The topic of this project was an Exhale Study. We took multiple samples in different
techniques to get optimal variety in the categories of Gender and Forced Expiratory Volume in
Liters. We wanted to test if the different variables would fit into certain areas of limitation, and
determine if they were correct in doing so or not.
Total Population Data
Systematic Sample Data
Simple Random Sample Data
Total Data
For our term project, Anna and I chose to do the Exhale Study. The set of data only has two categorical
sections so we obtained our samples from the gender section. For the first sample we used Simple Random
by generating a group of 35 from Statcrunch. The second sample we generated a random number, and did
every tenth number after that for 30 numbers; systematic sampling.
Compared to the whole population it varies. The whole population has about even numbers of
males and females, with just a few more males. The systematic sample has more females, and the simple
random has 20% more males. Overall the groups of data are pretty similar.
Std. dev.
Forced Expiratory Volume-liters 654 2.6367798 0.8670591
Sample 1 (Simple Random)
Summary statistics:
Std. dev.
Sample 1- Simple Random Sampling 35 2.9432571 1.0850499
Sample 2 (Systematic)
Summary statistics:
n Mean Std. dev.
Sample 2- Systematic Sampling 30 3.3839 0.64097282
The data between the two sets is not significantly different. One difference that can be noted is the
information on the first samples boxplot is skewed right, and the second samples looks to be in the
middle, but is skewed more to the left. The second set of data has a normal bell shape. Overall, the
Population data and Sample 1 are similar to each other and the standard deviation of the Population is
similar to that of Sample 2.
I always had a hard time with math, and felt like I only needed to know the basics like
multiplication, division, addition, and subtraction to get by in life. After taking some college courses in
math though, I have realized the real-life applications. Statistics, and this project specifically, show that
you can be confident in your estimates without just guessing; there is an actual process that can give
you systematic conclusions.