1. The first thing to do is to find a data set that interests you in the Statcrunch database. On the
homepage www.statcrunch.com you will find data sets in the Databox. What is important is that
you use a survey, NOT a population census. For example, there are data sets listed as
Thanksgiving Day Survey, Sample College Data, Depression and the Internet, Education versus
earnings, J-Pribe Auto Survey, etc. They are good to use. But many of the data sets such as New
York City Leading Causes Of Death or FIFA World Cup Results are data for the complete
population. Do not use these.
2. Present the observational data from the data set in pie charts, bar graphs, boxplots, stem and leaf
plots and histograms, etc. as well as in numerical summaries where appropriate:
a. Mean, median, range and standard deviation for any numerical data
b. Give a histogram for the frequency of data using carefully chosen bin size.
c. Side by side boxplots or histograms if you are comparing two (or more) types of data
such as male versus female or public versus private. Are there any outliers?
3. If you have two variables that you are comparing, such as engine size versus gas mileage, draw a
scatterplot of the data, find the correlation coefficient r and the least square regression line. Draw
any conclusions that the data and statistics seem to indicate.
4. After you have presented the observational data, proceed to the inferential statistics potion of your
report. Determine some research questions that you wish to ask. You should have some questions
that require confidence intervals, and some questions that require hypothesis tests. For example,
what is a good estimate for (CI) for the tuition at a private university and at a public university?
Or is there a connection between time on the internet and depression (a chi-square hypothesis
test)?
5. Try to use as many of the following as possible: one and two sample CI’s and HT’s for a
population proportion and the same for a population mean. Also, if the data presents the
opportunity, use a chi-squared test (GOF, independence or homogeneity) and/or ANOVA test.
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