StatCrunch: Conducting Hypothesis Tests for the Difference between Two Means with Raw Data Video Lecture | Statcrunch Tutorials for Beginners - Data & Analytics

In this video you will learn to conduct
hypothesis tests for the difference
between two means
with raw data using StatCrunch. The dataset that I'll be using is called
"Asking prices for four-bedroom homes in
Bryan/College Station, Texas"
This dataset compares four-bedroom homes
listed for sale in two adjoining cities;
Bryan, Texas and College Station, Texas. It
consists of a random sample of 15
homes listed for sale in Bryan, Texas
and 15 homes listed for sale in
College Station, Texas.
The square footage column contains the
square footage of each home
and location column lists city where
the home is located.
This video will cover how to compute a
two-sample t- hypothesis test
but a similar approach can be used for
two sample z- methods.
For this video the statistical question
I'm going to test is;
"Is there significant difference and
average size
of four-bedroom homes listed for sale in
the two cities?"
To do this, under the "Stat" menu,
I'll choose "T Stats", 'Two Sample",
'With Data". Under "sample 1" we'll select the
square footage column.
And in the corresponding "where" box
I'm going to enter "Location = "Bryan, TX""
to limit their homes and the first
sample to homes in Bryan, Texas.
Note: StatCrunch
is case sensitive so when you
type this and it needs to appear the
exact same way it does in the data table.
Under 'Sample 2", I'll again select the
square footage column
and in the corresponding "where" box I'll enter "Location = "College Station,
TX""
to limit their homes in the second
sample to College Station, Texas.
Under the 'Perform" menu, "hypothesis test" is
already chosen by default.
For this example we will leave the
null at 0 to compare the two means.
The alternative could be changed
to "less than" or "greater than" but I'll leave it at "not equal to".
Go ahead and click "compute".
The output table provides various
statistics from the test
including a test statistic and the
p-value.