Download Math 224 – Elementary Statistics

STATISTICS – LAB #4
Statistical Concepts
 Normal Probability Distribution
 Cross Tabulations
 Box Plots
 Regression
 Probability
Dealing with Bivariate Data
 Open a new MINITAB worksheet.
 Open the class survey results that were entered into the MINITAB worksheet.
 Two qualitative variables – Create a Cross-Tabulation table for the variables
Gender and Race. Pull up Stat > Tables > Cross Tabulation and Chi Square
and set For rows: to gender and For columns: to race. Check the Counts
option and click OK. This data will display in the session window. Be sure to
print a copy of this table.
 One qualitative and one quantitative variable – Create side-by-side box plots for
the Height variable by Gender. Pull up Graph > Boxplot and select With
Groups. Set Graph variables: equal to Height and Categorical variables:
equal to Gender. Title (put your name in as the title) and print the graph.
 Two quantitative variables – The interest here is to predict Height by a person’s
Shoe size. Create a scatter plot including the least squares regression line along
with the regression equation. Pull up Stat > Regression > Fitted Line Plot and
set Y equal to Height and X equal to Shoe. Title (put your name in as the title)
the graph using Options and print out a copy.
Short Answer Writing Assignment
If applicable, answers should be in complete sentences. Include copies of all print
outs with this assignment.
1. How many females are in the class?
2. What percent of Caucasians in the class are female? Show your work.
3. If a student is randomly selected from this class, what is the probability that they are a
Caucasian or male? Show your work.
4. If a student is randomly selected from this class, what is the probability of selecting an
African-American given that you have selected a female? Show your work.
5. Looking at the side-by-side box plots what can you tell about the central tendency of each
group (compare the two groups)? Explain your reasoning.
6. Looking at the side-by-side box plots what can you tell about the variability within each
group (compare the two groups)? Explain your reasoning.
7. Looking at the side-by-side box plots what is the shape of the distribution for each group?
Explain your reasoning.
8. Without looking at data, do you feel that shoe size (by itself) would be a good predictor for
height? Explain.
9. Looking at the graph and the r 2 value, do you feel that shoe size is a good predictor for
height? Explain your reasoning for each.
10. Assuming that shoe size is a good predictor, what would the estimated height be for someone
who wears size 10 shoes?