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1. DEVRY BIS 155 iLab 8 (Week 7) Descriptive statistics,
formatting, graphs, and regression analysis NEW
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BIS 155 iLab 8 (Week 7) Descriptive statistics,
formatting, graphs, and regression analysis NEW
Scenario/Summary
Hopefully you will find this week's iLab activity fun and
useful. We'll be exploring the world of statistics from a
business perspective this week, allowing you to practice
your skills with descriptive statistics, formatting,
graphs, and regression analysis.
As discussed in the lesson, the value of statistics lies in
the ability to analyze data more effectively for the
purpose of improving decision making. You might have
heard the expression that "statistics never lie, and only
liars use statistics". There is an obvious truth in this

2. statement, in that, depending on the questions being
asked and the data collected, the statistics can skew
reality.
For example, it is true that as ice cream sales increase,
accidents at swimming pools increase. Does this mean
that the more ice cream that is sold, the more accidents
it causes (correlation/causation)? Of course not, but the
data, if not interpreted correctly, could lead to false
conclusions. It just so happens that both are correlated
to a rise in temperature in the summertime. The hotter
it is outside, the more kids flock to swimming pools,
leading to more accidents, and the more ice cream is
sold. So you see, although statistics are vital in the world
of decision making, you have to be wise, and ask the
right questions.
STEP 1: Getting Started—Worksheet Template
Please download this week's iLab file:
Week7_iLab_Statistics
Your first step should be to save and rename this file
according to the naming convention above.
It is recommended, as you work on this iLab, that you
save your work often.
STEP 2: Create a Documentation Page
This will be a similar documentation page that you have
used for all prior iLabs. Please refer to instructions in
iLab 1 for detailed instructions.

3. Be sure to place the documentation sheet as your first
sheet.
STEP 3: Descriptive Statistics
The Data_1971_2000 worksheet is already loaded with
data for you, which is the actual temperatures for all of
the U.S. states between 1971 and 2001. As you can see,
the data already contains the averagetemperature for
each state, in both Fahrenheit and Celsius, along with
the ranking of the states, in terms of warmest average
temperature (#1) to the lowest.
1. Freeze the top row, so that the column headers are
visible as you scroll through the data.
2. At the bottom of the page, you are asked to provide
the Count, Average, Median, Mode, Min, and Max for each
of the states for each of the data columns. The shaded
area at the end of the states is where these descriptive
statistics should be entered.
3. To the right of the data, starting at approximately
Texas (row 44), use the Data Analysis feature to display
the summary descriptive statistics for each temperature
and the rank. Be sure to shade and format your
descriptive statistics (similar to the shading in Step #2
above) so as to be able to read everything well.
As you read your results, you might note some
interesting results. First and foremost, note how the
statistics associated with the rankings are virtually
worthless, as they really don't provide any insight to the

4. data itself. This is a little of what I meant above when I
talked about some statistics are junk, and you have to be
careful in how you ask your questions and interrupt the
results.
STEP 4: Bar Chart and Summary Statistics
Using the BarChart worksheet, calculate the summary
statistics shown at the bottom of the data, for each of
Bottles, Cans, and Plastic.
Create a bar chart to the right of the data, with a title of
Marketing Campaign Results. You can choose the colors
that you want for each city's results, but make sure that
you show the Y-axis labels to the right and the X-axis
labels on the bottom, along with the word City as their
label.
STEP 5: Line Chart
Using the LineChart1 worksheet, calculate the average
income for the ages listed. Then create a line chart, with
a title of Average Income by Age, with appropriate labels
on the X and Y axis.
Your chart should be placed to the right of your data, on
the same sheet.
STEP 6: Average and Median, With Line Chart
This step is very similar to the previous worksheet,
except that there is an additional summary statistic and
you are working with multiple variables.
Calculate the average and median for both Income and
Rent. As you look at your results, do you notice the

5. difference between the results? Does this better explain
the difference between average and median for you?
To the right of the data, on the same sheet, produce a
line graph of the Income and Rent. Again, the color of the
lines is your choice. Use a chart heading of Average
Income/Rent by Age. Be sure to show your Income and
Rent labels to the right of the chart, and a label of Age on
the X axis and Amount ($000) on the Y axis.
STEP 7: Regression Analysis and Scatter Graph
The data here is very simple, and not really a good
example of using regression analysis, but the process
behind the exercise is the most important issue.
1. Create a scatter chart of the data, below the data, with
a title of Revenue Growth.
2. Draw a trendline associated with the data points. Be
sure to select the inclusion of the Equation and R-
squared values on the chart.
3. Perform a regression analysis on the data set.
Remember to identify which of the variables are
dependent (Y axis) and independent (X axis) so as not to
get confused on your input values. Place the regression
results, starting at cell I1, instead of using the default,
which is a separate sheet. Be sure to identify and
highlight on the regression data, the R-squared value,
the Intercepts point, and the slope of the line.
STEP 8: Sorting Statistics

6. The data listed here is the first and last five presidents
to have died of natural causes.
Many forget that simply sorting information in a specific
format can provide meaningful information. However,
before we begin the multiple sorting exercise, simply
complete the descriptive statistics at the bottom of this
group of presidents. There are multiple ways of
calculating the average age for the first and last five
presidents, but for this exercise, use the AVERAGEIF
function. Because there is not a MEDIANIF function, you
will have to do this one manually with your formula (not
with a calculator).
Your next step is to copy the data for each president,
excluding the ordinal column, to each of the other two
categories. For example, the column of President should
start at cell G3, and at cell L3. Once you have copied the
data, reorder the data, with the second group by Age at
Death, and the third set by Year of Death.
You will most likely find this information very
interesting. Some find it strange that the average age of
death of the first five and the last five presidents was
less than a year different, especially given all the
advances in medicine.
STEP 9: Regression Analysis
Scenario: The owner of the Original Greek Diner has
been advertising for the past year, and is now ready to
renew his contract. He needs to know if the advertising
has been effective, so your task is to take the prior year's
data and perform a regression analysis to determine the

7. correlation between advertising expenditures and
restaurant sales (revenues).
Using the GreekData sheet, prepare a scatter plot graph
with a title of Revenues (Y), placed to the right of the
data on the same sheet. The data must be presentable,
so you might want to use an increment of $500 for the X
axis. After creating the graph, which should include the
Equation and R-squared values, create a trend line. You
should notice that there appears to be a close
relationship between advertising spending and
revenues.
Your next task is to create the regression data on a
separate sheet, labeled Greek Regression. As a
reminder, regression analysis is located on the Data ->
Data Analysis menu. Be sure to highlight the R-squared
value in red, the Intercept value in blue, and the X
variable 1 in green.
Based on this data, and what you have learned about
regression thus far, what do you think the owner should
do?
Let me throw you a curve (pun intended). Suppose this
restaurant is located near a baseball park, and it just so
happens that the days the owner advertised, there were
baseball games playing on those nights. Would you now
have the same conclusions, or might you want to take
the time to collect more data?
Statistics are not perfect, but they can provide
immeasurable insight into data analysis. You just have
to ask the right questions.

8. Be sure to submit your work for this iLab to the Dropbox
basket labeled Week 7: iLab 8.