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BIS 155 iLab 8 (Week 7) Descriptive statistics, formatting, graphs, and regression analysis
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BIS 155 iLab 8 (Week 7) Descriptive statistics, formatting, graphs, and regression analysis

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  • BIS 155 N iLab 8 (Week 7) Descriptive, format, graphs,regression analysis.xlsx

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 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.

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 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 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

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 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.

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

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