Our Project
Results
Research
We didn't have any outstanding
outliers.
The percentage of variation
in our data that can be explained
by a linear relationship, was .64%
Shoe Size Vs. Height
We chose shoe size
for X.
The factors that could explain the
rest of the variation could have been;
abnormally tall with small feet, or
very short with large feet.
We chose the height for
the Y value.
Before and after applying
the central limit theorm, we
noticed that the mean remained the same but the standard deviation changed
It was the most convenient
to sample
The hypothesis we made about
the true population mean was to
test the claim that the mean was
less than 7.
The reason we chose that was to
find out if the mean of the shoes sizes
was more than 7.
We would use a linear regression line
for our scatter plot because it would
be close to the actual data points in the
sample.
After testing the claim, we found that there was not enough evidence to reject the claim
that the mean was greater than 7.
The hypothesis we made about the true population variance was that the true population was
We randomly sent a text to
about 50 girls and whoever
responded the fastest we used.
There was no linear
correlation, since we
sampled older girls there
was no smaller, younger girls.
We chose the X value to
complete the rest of our project.
It was the fastest way,
to just text then wait to find
33 girls to ask.
After creating our dot plot
to determine our type of distribution,
we found that we actually have a non
specific distribution.
Measure of center;
7.6.
Measure of Variance;
1.96.
Assets
details
map
Important
Details
Stockholm
(cc) photo by Metro Centric on Flickr
(cc) photo by jimmyharris on Flickr
Budapest
(cc) photo by Metro Centric on Flickr
San Francisco
(cc) photo by Franco Folini on Flickr
doodles
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