This document provides instructions for completing iLab 8 activities in BIS 155. The activities include descriptive statistics, formatting, graphs, and regression analysis using temperature, marketing, income, and other sample data. Students are instructed to calculate descriptive statistics, create different graph types like bar charts and line graphs, perform regression analysis to examine relationships between variables, and sort data in various ways. The document emphasizes that while statistics are useful, they must be interpreted carefully and can be skewed depending on the questions asked and data collected.

1. Dervv 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

2. 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 m,,h in
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

3. 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
; Ge
: Please
do ng Started—
download
-Worksheet Template
is 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.

4. 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. Asyou can see, the data alr
reaixrb^m
averagetemperature for eacl
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

5. 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 Aithe statistics associated with the
rankings are virtually worthless, as they really
don't provide any insight to the 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.

6. 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 Line Chart1 worksheet, calculate the
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< for the ages list
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.

7. Calculate the average and median for both Income
and Rent. As you look at your results, do you notice
the 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
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mount ($0°0) on .he Y
ads.
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
importantissue.

8. 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
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

9. 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.
A H H
ent should
u have
cop
l,
the
*>«■ied the data,
i
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

10. 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 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
sh
oul
d include
the E
quation 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.

11. 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
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Statistics are not
perfect, immeasurable ta analysis. Y
have to ask the right questions.
Be sure to submit your work for this iLab to
the Dropbox basket labeled Week 7: ilab 8.