Name
Date
Lab Data Analysis Guide - Correlations
During this lab exercise you have recorded many different biometrics of students in the class. You were
asked to test three different hypotheses and support them with the data you collected – a relationship
showing a positive correlation, a negative correlation and no correlation. To make sense of these data,
you have to be able to visualize the trends that appear when you compare the variable to each other.
Using Excel, I would like you to tell me the relationships between the various variables that you have
decided to measure.
You can do this in a variety of ways for any data set. If you are comparing discrete independent variables
(variable that fit into categories like male vs. female, live vs. dead, conscious vs. unconscious) you will
generally make these comparisons using a bar graph where each bar will represent a category. By
looking at the values (the bar heights) for each of these categories, comparisons can be made to see if
differences exist between the selected groups. Generally, the averages are taken for a column of
information to make the chart and to show the difference between groups. Directions on how to do this
in Excel are found below.
1.
To compare column averages, you first need to calculate them. From the original data set, cut
and paste your data into the number of columns that you wish to compare. Then, in an empty cell
at the bottom of a column, type:
=average(select the column you wish to take the average of by highlighting it with the cursor)
The average will appear under the column if you typed everything correctly. You can then drag this
formula (left click the bottom-right corner of a cell and drag it to where you want it) for each column.
2. To visually compare the averages for each column, you’ll want to make a bar graph. To do this:
Re-type the title of the column over the cell containing each average. Then, highlight the titles and
averages by dragging the cursor over all of the cells with the mouse. Once highlighted, click
“Insert – Chart – Column chart – 2d column”
This will give you a chart showing the averages and labels for each category. Please make sure the axes
for the charts are labeled. To do this, go to the top-right of the screen, select “chart tools” and click on
“chart elements” to add the titles for the x and y axes. These charts can then be used to compare the
values between the groups.
However, if you are comparing continuous variables (height vs. weight, breathing rate vs. pulse rate,
etc.) where the numbers you are measuring could be almost any value, you should compare them using
a scatterplot chart. This will allow you to include a “line of best fit” or a trendline that will show the
general relationship between the variables.
3. To compare continuous variables, you need to make a scatter-plot chart. Open a new sheet at the
bottom of the excel document and copy the columns of data you wish to compare. Highlight the
columns, click on “insert” and select “scatterplot”. This will result in a graph with data points
whose relationship can be compared between the two variables you measured. Right click on any
one of the data points and select “add trendline” and choose “linear”. This will place a “line of
best fit” through your data points to show you the general relationship between the categories.
Follow the directions above to add proper axes and other labels to your charts.
This shows a
positive
relationship
between
variables
This shows NO
relationship
between
variables
This shows a
negative
relationship
between
variables
These relationships can be used to infer how one might impact the other. Please remember that looking
at data this way only shows how they are related to each other, not that one CAUSES the other.
Correlation and causation are two completely different relationships. It is YOUR job to offer an
explanation that explains the relationships that you have found in your (or, in this case, the class) data.
To confirm your hypothesis, perform your comparison in Excel and include a data chart showing your
comparison. Then say whether you or not you supported your hypothesis and offer an explanation for
each outcome. You should also offer other procedures (tests) that could be done to increase your
understanding of what you learned or those that may support your hypothesis. For example…
Chronic smokers will have increased resting respiratory rates when compared to non-smokers. This is
because the decreased lung capacity of smokers will cause them to breathe faster to compensate for
lack of lung volume and therefore gas exchange.
25
Resting Respiration Rate
NonSmokers smokers
13
18
20
19
22
15
25
20
24
17
23
13
24
15
25
14
28
16
18
14
23
18
21
19
20
12
20
15
24
12
20
15
10
5
0
Smokers
Non-smokers
NonSmokers smokers
Average
22
15.8
These data support my hypothesis in that smokers have a higher average resting respiration rate than
non-smokers. This is because smokers have decreased lung capacity and therefore may need to increase
their respiration rates to compensate for this lack of gas exchange. In addition, smokers may have much
of the surface area of their lungs clogged with smoke particulates, preventing the diffusion of gases
through the epithelia of the lungs. Further testing of the lung capacity of smokers would be needed to
confirm this hypothesis. This could be compared by recording the total amount of air moved in and out
of the lungs in one breath between smokers and non-smokers by blowing up a balloon.
Please make sure that you perform at least three different comparisons between the dependent
variables and an independent variable and offer an explanation for each of the comparison you make.
This should be summarized with an end paragraph (like a Discussion section) at the end of the entire lab
report that logically synthesizes the comparisons that you chose to make. In other words, what story do
your results tell?
GRADING
Hypothesis for each variable comparison – 5 points each
Charts showing comparisons with appropriate labels – 10 points each
Explanation of results for each comparison – 10 points each
Summarizing paragraph that synthesizes the results of each comparison – 25 points