Using Python:

In this project you will do a simple analysis of three datasets containing height data that you will find in the datasets folder on the Canvas site. The first dataset (hopeheights.dat) is heights of college students at Hope College. The first column of this data is the gender of the subject (1 female, 2 male) and the second column is the height in inches. See pythonstart.py to see how to load a file with multiple columns. Alternatively, you can tell numpy.loadtxt which columns of the datafile you want to load. Google numpy.loadtxt to see how to do this. The second data set (BBhtwt.dat) contains heights (in inches) and weights (in lbs) of professional baseball players. The third data set (Bigheightwt.dat) is the heights (in inches) and weights (in lbs) of 25,000 adolescents and various ages and both genders. For the Hope College data, also calculate a mean and a standard deviation for each gender separately. You can use a for loop with an if statement to create a separate list for each gender. The append command will be useful here as well. Report your results in a data table with a caption. Next, make a normalized histogram for the height measurements in each data set and try to fit a Gaussian function to the histogram. For the Hope College data make three histograms, one for the entire data set, one for males only, and one for females only. Your histograms should be included as figures with captions.

Here are some of the first 5 lines from the data files:

hopeheights.dat	BBhtwt.dat height	BBhtwt.dat) weight	Bigheightwt.dat height	Bigheightwt.dat weight
1. 67 1. 67 1. 67 1. 60 1. 68	74 74 72 72 73	180 215 210 210 188	65.78331 71.51521 69.39874 68.2166 67.78781	112.9925 136.4873 153.0269 142.3354 144.2971