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(female narrator)
This is just a couple of tips
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on how to use the
significance test flow chart.
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As you go through each problem, this
flow chart can help you to determine
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what type of significance test you
should perform on that problem.
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The first thing you want to do is you
want to read the problem.
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When you're done reading the
problem, you need to ask yourself,
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"What was the data collected from
each member of the sample?"
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For instance, did you collect
a weight, or a height,
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or did you take their temperature,
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or did you count the number
of dogs that they had?
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That would be numerical data.
When you read the problem,
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did you see some key words like mean,
or average, or standard deviation.
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All of those things indicate that you
have a quantitative data problem.
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On the flip side of that, were there some
keywords like proportion, percent, rates,
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or was each member of the
sample asked a yes or no question,
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such as, "Have you had
a heart attack? Yes or no?"
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Maybe they were asked, "Are you
overweight? Yes or no?"
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"Do you support the
president? Yes or no?"
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So that type of a thing. That
would be qualitative data.
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Once you determine the type of data,
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then you follow the decision
tree down that side.
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For instance, if you have quantitative
data, then you would ask yourself,
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"Do I have two populations or
do I have one population?"
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Now remember, an easy way to tell
whether you have one or two populations
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is to look for the samples
that are given in the problem.
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If you're only given one sample,
then you only have one population.
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If you're given two samples,
then you have two populations.
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You have to be given the complete
information about both of those samples,
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like the sample mean and sample standard
deviation, for two separate samples
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in order to determine
you have two populations.
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Once you determine how
many populations that you have,
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if you have one population, you're going
to do the t-stat one sample.
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If you have two independent populations,
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then you're going to do the
t-stat two samples.
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Keep in mind that you need to determine
whether you're going to pull the variances
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or not pull the variances.
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Down here, we have the little
tip on how to decide that.
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It's just the ratio of the larger
sample standard deviation
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divided by the smaller sample
standard deviation,
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and looking to see if it's greater
than two, then you remove
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the pulled variances check mark,
and if it's less than two,
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then you keep the pulled
variances check mark.
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Now remember if you have
two dependent samples,
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the two samples are related to each other,
such as a before and an after,
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a pre and a post test. The two samples are
related because it's the same person
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taking the pre-test and the
same person taking the post-test,
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or maybe you're testing
two different kinds of tires,
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so you take a car and you
drive it with the first tire,
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and then you take the same car and you
drive it with the second tire.
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Those two samples are related because
it's the same car driving both times.
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If they're dependent samples,
then you have a choice
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of either finding the differences
between the two samples
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and doing a one sample t-test, or under
t-stats you can do the paired t-test.
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If you have more than two
populations, we only have one test
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where we had more than two populations
and that was from chapter 14,
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and that was where we had three, four,
five, six, as many populations
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as were needed, and that was to perform
the one-way ANOVA test.
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Now, if you have two quantitative
variables, like you were collecting
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the height of a person and
the weight of a person,
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and you were asked to decide if the
variables were dependent,
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or associated, or independent, if
you saw those words in there,
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but you have two quantitative
variables, a height and the weight,
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then that would be a
regression type problem,
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and you would perform that significance
test using the test on beta, the slope,
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using the regression simple linear
command in StatCrunch,
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so again that's for two
quantitative variables,
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and you're going to look for phrases
like "Determine if the variables
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are dependent, or
associated, or independent"
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Back over here on the qualitative
side, it's a little bit shorter
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on the qualitative side, but starting out
the same way we did on the quantitative
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side, we need to determine if we
have one or two populations.
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Again, if you have two populations then
you're going to have information
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for two samples. You're going to be
given the number of successes
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and the total sample size
for two different samples.
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If you do determine that
you have one population,
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then you would do a
proportion stat one sample.
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If you do determine that you
have two populations,
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then again you're going
to do a proportion stat,
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but you're going to do two samples.
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Our last test for qualitative data,
is if we have two variables.
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For examples, we might be looking at
gender and happiness.
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That would be two qualitative variables.
For gender, you would be male or female.
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For happiness, they might indicate very
happy, or happy, and not happy,
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so those are word answers to those.
"How happy are you?"
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Very happy, happy, not happy.
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Those are word answers so those are
definitely qualitative variables,
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and it was two variables.
We were collecting gender
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and we were collecting level of happiness
so we have two qualitative variables.
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In the problem you can also look to see
if it says the variables are dependent,
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associated, or independent.
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Also note, a little hint here, that when
you do have two qualitative variables,
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it's typically the data will be
shown in a contingency table,
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so all of these clues here
help you determine
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that you're going to perform a
chi-square independence test.
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So this is a little bit about how
to use this decision tree,
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or significance test flow chart.
Sometimes I call it a decision tree
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because it branches off,
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or sometimes I just call it a
significance test flow chart.
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But anyway, this is a little bit about
how to use this document.