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Text or Whatsapp Olivia at +1 (307) 209-4351”>iLab> STEP 9: Regression



iLab Overview


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 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.



Submit your assignment to the Dropbox, located
at the top of this page. For instructions on how to use the Dropbox, read these”>step-by-step instructions.

(See the Syllabus
section “Due Dates for Assignments & Exams” for due dates.)

You will turn in one Excel workbook for this
iLab. The workbook will consist of nine separate worksheets, including the
Documentation sheet.

File naming convention: If your name is Jane
Doe, then your file should be named very similar to: Doe_J_Week7_iLab.xlsx.

Click to download the”>Grading Rubric for Lab

Required Software

Microsoft Office: Excel

Options for accessing Microsoft Excel 2013:

Use a personal copy on
your PC. You can request a copy of Microsoft Office 2013 via the Student
Software Store icon on the Course Resources Page

If you are a MAC user,
click to read the”>MAC User Information.

virtual lab to access Microsoft Excel in the virtual lab.
For additional virtual lab information and tutorials on saving files, click the
iLab icon on the Course Resources page.

Lab Steps

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.

Be sure to place the documentation sheet as your
first sheet.

STEP 3: Descriptive

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 average temperature 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.

Freeze the top row, so
that the column headers are visible as you scroll through the data.

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.

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 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

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 difference between the
results? Does this better explain the difference between average and median for

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.

Create a scatter chart
of the data, below the data, with a title of Revenue Growth.

Draw a trendline
associated with the data points. Be sure to select the inclusion of the
Equation and R-squared values on the chart.

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

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

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 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

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

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


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