StatCrunch: Computing Confidence Intervals for the Difference between Two Means with Summary Data Video Lecture | Statcrunch Tutorials for Beginners - Data & Analytics

In this video you will learn to compute
confidence intervals for the difference
between two means
using StatCrunch. In this example I
will compare randomly sampled
four bedroom homes listed for sale in
two adjoining cities;
Bryan , Texas and College Station, Texas. A
random sample of fifteen homes listed for
sale in Bryan, Texas
was found to have a mean square footage of
2,842 square feet and a sample
standard deviation of 831 square feet.
A random sample of fifteen homes in
College Station, Texas
was found to have a mean of 2,751 square
feet and a sample standard deviation 774
square feet.
This video will cover how to compute a
two-sample t
confidence interval, but a similar
approach can be used for two sample z-
methods
that are appropriate for situations with
larger sample sizes,
and/or known standard deviations. So for
this video the statistical question I'm
going to investigate is:
"Is there significant difference in
the average size
a four-bedroom homes listed for sale in
the two cities?"
To do so under the "Stat" menu,
I'll choose "T Stats",
"Two Sample", "With Summary",
since I don't have the actual data in
the data in the data table in
StatCrunch. Under "Sample 1",
I will enter 2,842 for the sample mean,
831 for the sample standard deviation
and "15" for "sample size".
Under "Sample 2",
we'll enter 2,751 for the same for mean,
774 for the sample standard deviation,
and 15
for sample size. Under "perform",
I'll choose "confidence interval". By default
StatCrunch has value
0.95 for the level which will produce a
95 percent confidence interval.
For this example I'll enter 0.99
to produce the 99 percent confidence
interval. I'll click compute.
The results show a 99 percent confidence
interval for the difference in the mean
square footage of
four bedroom homes, where L. Limit is the
lower limit
and U. Limit is the upper limit of the confidence interval.