1. Data Science and Analytics
Introduction to
Data Science and Analytics
Stephan Sorger
Unit 3. Excel Tools
Disclaimer:
All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only
Some material adapted from: Sorger, “Marketing Analytics: Strategic Models and Metrics”
Welcome to Module 3 of Introduction to Data Science and Analytics.
This module is important because it covers essential tools and techniques
we can use in Microsoft Excel.
© Stephan Sorger 2016; Data Science: Excel Tools; 1

2. Outline/ Learning Objectives
Topic Description
Basic Statistics Mean, Median, Variance, Standard deviation, RMS
Pivot Tables Extract significance from large data sets
Solver Maximize/Minimize criteria subject to constraints
ToolPak Analysis add-in functionality of Excel
In this module, we will cover several learning objectives:
-How to calculate basic statistics such as mean, median, and so forth
-How to extract significance from large data sets using pivot tables
-How to maximize or minimize objectives based on constraints
And
-How to implement and use the analysis add-in functionality built into Microsoft Excel
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3. Basic Statistics
We start our discussion with basic statistics
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4. Basic Statistics: Overview
Data set summarized by basic statistics:
Mean (average)
Median (half-way point)
RMS (Root Mean Square)
Standard Deviation (degree of variability)
Data set
In this section, we show how to calculate four common statistics:
Mean, which is the average of a data set;
Median, which is the mid-point of a data set;
RMS, or root mean square, which we use for other calculations;
and
Standard Deviation, which indicates the degree of variability of the data;
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5. Basic Statistics: Example
Single-season home run records: Barry Bonds, Mark McGwire, and Sammy Sosa
Each player wanted to break the record held by Roger Maris
Home run counts per season: Barry Bonds
2 4 5 6
7 3
Stemplot
-Separate each observation into a “stem” (left digits) and “leaf” (right digit)
So, 16 would be: 1 | 6 with “1” as the stem and “6” as the leaf
-Write stems vertically in increasing order from top to bottom
-Draw vertical line from top to bottom
-”Split” the stems for greater clarity by entering two “2”, “3”, “4”, etc.
-Interpret the stemplot: Study the distribution; Outlier at 73?
Here we have a dataset from San Francisco Giants baseball player Barry Bonds.
Big hitters like Bonds, McGwire, and Sosa wanted to break the home run record set by Roger Maris.
As such, we consider the performance by Bonds.
The table on top shows the home run counts per season.
We can view the distribution of the data quickly by executing a stemplot.
The stemplot separates each observation into a “stem” (the digits on the left)
And a “leaf” , the digits on the right.
We record the stems vertically in increasing order from top to bottom.
For greater visual clarity, we can add a vertical line.
Even from this simple plot, we can see clusters of data around the 30s,
And a potential outlier at 73.
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6. Basic Statistics: Mean
Mean = (Sum of all observation values) / (Number of observations)
Xbar = Mean = ( … + 73) / (16) =
What if we did not count the outlier in 2001?
Mean = ( …. + 49) / 15 = 32.93; One good season increased his average 2.5!
In statistics, we say that the mean is not a resistant measure of center,
because it cannot resist the influence of one extreme observation
The mean of a data set is defined as the sum of all the observation values
Divided by the number of observations, similar to the arithmetic average.
In our case, we add all the home run observations, 16, 25, 24, and so forth
And divide that sum by the number of observations, or 16, to get
In our stemplot, we noticed a potential outlier at 73.
What if we did not include it?
If we remove that observation from the dataset, we arrive at a mean of
Therefore, one good season increased his overall average by almost 2.5 points.
We use the team resistance to indicate the influence of one extreme observation.
Because we see such a large change removing one data point, we can say that mean in not a resistant measure of center.
© Stephan Sorger 2016; Data Science: Excel Tools; 6

7. Basic Statistics: Median
M = Median = Center (middle) of set of observations
To find the median, we re-arranged the observations from smallest to largest (above)
For an odd number, the process is easy  just pick the middle one
But we have 16 observations, which is an even number
So we pick the “center pair” of observations # 8 and #9 (both of these are 34)
What if we remove the extreme observation of 73?
Next, we compute the median, or center of the set of observations.
To do so, we sort the data in ascending fashion to find the middle point.
In case of even numbers of observations, we select the middle pair.
In our case, we pick the center pair of observations, or 34.
If we removed the extreme observation of 73,
Our median would remain at 34.
Therefore, we say that medians represent a resistant measure of center.
Median is still 34; Therefore, we say that medians are a resistant measure
© Stephan Sorger 2016; Data Science: Excel Tools; 7

8. Basic Statistics: RMS RMS = Root Mean Square
A kind of average used in statistics and engineering
Used as a component of the calculation of the standard deviation
To compute, square all the numbers in the set, find the mean, and take the square root
RMS = SQRT ( (a1)^2 + (a2)^2 + (a3)^2 + …) / n )
where a1, a2, a3, … = observations
n = number of observations
Similar in size to average
(average was )
We move on to calculating the root mean square, or RMS of the data.
RMS represents a kind of average, and is used as a component
Of the calculation of the standard deviation, and for other purposes.
To compute it, we square each observation, find the mean, and take the square root.
In our case, we arrive at an RMS of 37.8, which is roughly equivalent
To our average calculation of 35.4.
© Stephan Sorger 2016; Data Science: Excel Tools; 8

9. Basic Statistics: Standard deviation
Measures the spread by examining how far the observations are from their mean
To compute, calculate the variance:
Variance = s^2 = [ (x1 – xbar)^2 + (x2 – xbar)^2 + …+ (xn – xbar)^2 ] / (n – 1)
s = SQRT (Variance)
For our previous baseball example, recall that the mean (xbar) = :
We end our discussion of basic statistics with the calculation of s, or standard deviation.
Standard deviation measures the spread of data
By examining the distance of the observations from their mean.
To compute the standard deviation,
We subtract the mean from each observation, square that amount,
And repeat the process for each observation, then divide the sum by n-1,
Where n represents the number of observations.
In our case, the standard deviation is 13.6,
Which shows that the observations are about 13.6 runs from the mean of 35.4.
We could compare this value to standard deviations from other hitters to
Compare the consistency of their hitting.
© Stephan Sorger 2016; Data Science: Excel Tools; 9

10. Pivot Tables
Pivot tables are an elegant way to analyze and communicate data. Much of the same functionality can be done by sorting, but pivot tables make the process much easier and faster.
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11. Pivot Tables Name Sales Date of Sale Product Channel
Alex Alpha $1,100 January Product A Store
Betty Beta $100 February Product B Internet
Debbie Delta $300 February Product B Store
Edie Epsilon $200 January Product B Internet
Gary Gamma $1,300 January Product A Store
Extract significant data points from large data set
Original Data Set
We start by examining a typical customer data set. Each row represents one customer. For example, we can see that Alex Alpha accounted for a total of $1,100 in sales, placed their order in January, selected product A, and used the retail store distribution channel. Suppose we want to see total sales for each product, or product sales by distribution channel, or some other view. We could sort the data to obtain that view, but we will see how developing pivot tables is a much better alternative.
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12. Pivot Tables Create Pivot Table … A B C D E F G
Choose the data set to analyze
Select a table or range:
Excel
Table Range:
Sheet1:$A$1:$E$6
Home
Insert …
Use an external data source
Pivot Table A B C D E F G
Choose where you want the Pivot Table report
New Worksheet
Existing Worksheet OK
To build pivot tables in Microsoft Excel, click on the Insert tab and select Pivot Table. The right side of the slide shows the Create Pivot Table dialog box that pops up. Click on the radio button marked Select a table or range, and then enter the cell range of the dataset. Most of the time, Excel can sense the location and will pre-enter it for you. Check to make sure Excel didn't assume incorrectly. Next, click on the radio button marked New Worksheet. Building the pivot table on a new worksheet is much cleaner than placing it on the existing worksheet. Click OK on the lower right.
Launching Pivot Table in Excel
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13. Drag fields between areas below:
Pivot Tables
Pivot Table Field List
Choose fields to add to report:
Customer
Excel’s Pivot Table Field List, Based on Original Input Data Set;
Select “Sales” and “Product” to get basic table of sales by product
Sales
Date
Product
Channel
Drag fields between areas below:
Report Filter
Column Labels
Different versions
of Excel look different;
PC vs. MAC
Row Labels
Values
Excel will build a pivot table field list and present it to you as a dialog box. It lists all the fields of our dataset, which consist of the labels at the top of each column, such as Customer and Sales. Because we want to see sales by product, select the Sales and Product fields.
Pivot Table Field List
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14. Drag fields between areas below:
Pivot Tables
Pivot Tables: Basic Report: Sales by Product;
Select “Date” to see how sales vary over time A B C D
Pivot Table Field List 1
Row Labels
Sum of Sales
Choose fields to add to report: 2
Product A
2400
Customer 3
Product B
600 x
Sales 4
Grand Total
3000
Date x
Product 5
Channel 6 7
Drag fields between areas below: 8
Report Filter
Column Labels 9 10
Row Labels
Values 11
Product
Sum of Sales 12
This slide shows the result if we select the Sales and Product fields. Note that the table shows Product A and Product B as rows. We see that Product A delivered $2400 in sales, and product B delivered $600, for a total of $3,000. If we want to see how those product sales varied over time, we would select the Date field.
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15. Drag fields between areas below:
Pivot Tables
Pivot Tables: Sales by Product and Date
Select “Channel” to see how sales vary with type of Channel (store) A B C D
Pivot Table Field List 1
Row Labels
Sum of Sales
Choose fields to add to report: 2
Product A
2400
Customer 3
January
2400 x
Sales 4
Product B
600 x
Date x
Product 5
January
200
Channel 6
February
400 7
Grand Total
3000
Drag fields between areas below: 8
Report Filter
Column Labels 9 10
Row Labels
Values 11
Product
Sum of Sales
This slide shows the result when we select the Sales, then Product, and then Date fields. The table still shows total product sales for product A and product B, but now the sales are broken down by date. If we want to see how our product sales by date varied over distribution channel, we would select the Channel field.
Date 12
© Stephan Sorger 2016; Data Science: Excel Tools; 15

16. Drag fields between areas below:
Pivot Tables
Pivot Tables: Sales by Product, Date, and Channel (Added Date, and then Channel)
What if we had added Channel, and then Date? A B C D
Pivot Table Field List 1
Row Labels
Sum of Sales
Choose fields to add to report: 2
Product A
2400
Customer 3
January
2400 x
Sales 4
Retail Store
2400 x
Date x
Product 5
Product B
600 x
Channel 6
January
200 7
Internet
200
Drag fields between areas below: 8
February
400
Report Filter
Column Labels 9
Internet
100 10
Retail Store
300
Row Labels
Values 11
Grand Total
3000
Product
Sum of Sales
This slide shows the result when we select the Sales field, then the Product field, then the Date field, and then the Channel field. The table shows Sales for product A and B, broken down by date, and further broken down by channel. For example, we see product B total sales as $600, with February sales of $400. $100 of February sales came to us using the Internet channel, and $300 were sold through the retail store. Suppose we wanted to visualize the data in a different format, such as emphasizing channel over date. That is, show the total sales through each channel, and how those sales varied over time. To do this, we would select channel first, and then select date.
Date 12
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17. Drag fields between areas below:
Pivot Tables
Pivot Tables: Sales by Product, Date, and Channel (Added Channel, and then Date) A B C D
Pivot Table Field List 1
Row Labels
Sum of Sales
Choose fields to add to report: 2
Product A
2400
Customer 3
Retail Store
2400 x
Sales 4
January
2400 x
Date x
Product 5
Product B
600 x
Channel 6
Internet
300 7
January
200
Drag fields between areas below: 8
February
100
Report Filter
Column Labels 9
Retail Store
300 10
February
300
Row Labels
Values 11
Grand Total
3000
Product
Sum of Sales
This slide shows what would have happened had we selected the channel field first, and then the date field. Note that the table emphasizes channel over date. For example, Product B sold a total of $600, with $300 sold through our Internet distribution channel. Of that $300, $200 was sold in January, and $100 in February.
Date 12
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18. Drag fields between areas below:
Pivot Tables
Adding Field to Report Filter A B C D
Pivot Table Field List 1
Row Labels
Sum of Sales
Choose fields to add to report: 2
Product A
2400
Customer 3
January
2400 x
Sales 4
Product B
600 x
Date x
Product 5
January
200
Channel 6
February
400
Add to Report Filter 7
Grand Total
3000
Drag fields between areas below: 8
Report Filter
Column Labels 9 10
Row Labels
Values 11
Product
Sum of Sales
We can also view information by adding a report filter. For example, the table currently shows sales by product and date. If we wanted to see channel as well, we could right-click on channel, and select the term, "Add to Report Filter."
Date 12
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19. Drag fields between areas below:
Pivot Tables
Selecting Reports using Report Filter A B C D
Pivot Table Field List 1
Channel (All)
Choose fields to add to report: 2
(All)
Customer 3
Internet x
Sales
Retail Store 4 x
Date x
Product 5 x
Channel 6 7
Drag fields between areas below: 8
Report Filter
Column Labels 9
Channel OK 10
Row Labels
Values 11
Product
Sum of Sales
This slide shows what happens when we invoke the report filter for channel. The dialog box shows the different values for channel, in this case Internet and Retail Store, as well as an All option. We click OK to see the resulting table.
Date 12
© Stephan Sorger 2016; Data Science: Excel Tools; 19

20. Drag fields between areas below:
Pivot Tables A B C D
Pivot Table Field List 1
Sum of Sales
Column Labels
Choose fields to add to report: 2
Row Labels
Internet
Retail Store
Grand Total
Customer 3
Product A
2400
2400 x
Sales 4
January
2400
2400 x
Date x
Product 5
Product B
300
300
600 x
Channel 6
January
200
200 7
February
100
300
400
Drag fields between areas below: 8
Grand Total
300
2700
3000
Report Filter
Column Labels 9
Channel 10
Row Labels
Values 11
Product
Sum of Sales
This slide shows the resulting table with the report filter. Note that the table still shows product sales by product and date, and further breaks out sales by channel, with separate columns for the Internet and Retail Store sales channels, as well as a Grand Total column.
We have shown only a fraction of the possible views possible with pivot tables. We encourage you to build your own dataset, or start with an existing dataset from your organization, and experiment building different views of the data using pivot tables. You will be amazed by all the different views you can achieve, and will quickly find pivot tables to be an indispensable tool for analytics success.
Date 12
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21. Microsoft Excel Solver
In this section, we discuss the Solver functionality within Microsoft Excel.
We can use Microsoft Excel Solver to maximize or minimize objectives of interest subject to constraints.
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22. Or Minimized Objective
Excel Solver: Maximization under Constraints
Common Terms for Process:
-Linear Optimization
-Linear Programming
-Maximization/ Minimization
INPUTS
OUTPUTS
Objective Function
Maximized Objective
Or Minimized Objective
Linear
Optimization
Model
Constraints
The promotion allocation model uses linear optimization to calculate the optimum allocation of budget across promotion vehicles. It is an example of a normative model, as we discussed in chapter 1. The model takes an objective function and constraint equations as inputs, and delivers the maximized objective and other information as outputs. We cover the model in this lecture.
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23. Solver: Maximization under Constraints: Process
Promotion Data
Budget Allocation
Vehicle
Contribution
Promotion
Objective
Promotion
Constraints
Optimization
Model
Vehicle Contribution: Determine effectiveness of campaigns, based on historical data
Promotion Objective: Declare promotion objective in equation form
Promotion Constraints: Specify promotion constraints in equation form
Optimization Model: Execute model
The linear optimization process entails four steps. In the first step, we calculate the vehicle contribution, which is the amount of results generated per use of the vehicle. For example, we could estimate the number of impressions, or views, delivered by each ad. In the next two steps, we express our promotion objective and constraints in equation form. We cover how to do that in the next slides. We then execute the model.
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24. Budget NTE (not to exceed) $XXX/yr
Excel Solver: Constraints
Financial Constraints
Contractual Constraints
Budget NTE (not to exceed) $XXX/yr
Typical
Promotion
Constraints
Contracts with outside agencies
Legal Constraints
Company Policy Constraints
Must follow legal regulations
Observe company policies
We start by discussing promotion constraints. The slide shows a few typical constraints. First is budget. Almost every organization faces a limited budget, such as a certain amount of money that can be spent each month on promotion. Next, we face legal constraints, such as legal regulations that cover that jurisdiction. Third, we often face contractual constraints, such as specific limits placed on us due to performance contracts with external agencies. And fourth, we must observe company policies.
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25. Excel Solver Promotion Vehicle Audience/Ad Cost/Ad Maximum Quantity
D: Direct Marketing 30 Viewers/Ad $30/Ad 30
P: Pay Per Click 30 Viewers/Ad $40/Ad 20
S: Social Media 40 Viewers/Ad $60/Ad 10
Direct Marketing: s sent directly to individuals within target market
Pay Per Click: Campaigns displaying ads during relevant Internet searches
Social Media: Paid advertisements on social media platforms
We will demonstrate the process using an example. In the example, we promote our goods and services using three different promotion vehicles--direct marketing, pay per click, and social media. In direct marketing, we send s directly to individuals we think will be interested in our offerings. In pay per click, we work with Google and other search engines to display ads for our offerings when users search for relevant topics. In social media, we insert paid ads on various social media platforms, such as Facebook and LinkedIn. We examine the performance of previous campaigns and discover that our direct marketing campaigns result in 30 viewers per ad, and that they cost $30 to develop and send. Similarly, pay per click delivers 30 viewers per ad and costs $40, and social media results in 40 viewers per ad and costs $60. Our current personnel count and contracts with outside advertising agencies gives us the capacity to create 30 direct marketing campaigns, as well as 20 pay per click and 10 social media campaigns per month.
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26. Excel Solver Linear Optimization Element Equation
Objective Function Z = 30*D + 30*P + 40*S
Constraint #1: Budget B = 30*D + 40*P + 60*S <= $2,000
#2: Maximum campaigns/ month: D D <= 30
#3: Maximum campaigns/ month: P P <=20
#4: Maximum campaigns/ month: S S <= 10
Objective Function
Z = 30 * D + 30 * P + 40 * S
In this slide, we show how to create the objective function. We state that we want to maximize the number of impressions from all promotion vehicles. The variable Z represents the objective quantity, in this case, the number of impressions. Impressions come from direct mail, pay per click, and social media campaigns, represented by the variables D, P, and S, respectively. The coefficients for each of the campaign variables are equal to the number of viewers per campaign, as we discussed in the previous slide. For example, we found that direct campaigns resulted in 30 viewers per ad, so we place a 30 before the D. Therefore, our objective function is stated as Z equals 30 multiplied by D plus 30 multiplied by P plus 40 multiplied by S.
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27. Excel Solver Linear Optimization Element Equation
Objective Function Z = 30*D + 30*P + 40*S
Constraint #1: Budget B = 30*D + 40*P + 60*S <= $2,000
#2: Maximum campaigns/ month: D D <= 30
#3: Maximum campaigns/ month: P P <=20
#4: Maximum campaigns/ month: S S <= 10
Promotion Constraints: Budget
B = 30 * D + 40 * P + 60 * S ≤ $2,000
Similarly, we calculate our budget equation, again using D, P, and S as the variables for direct , pay per click, and social media, respectively. For the budget equation, the coefficients for each variable would be the cost for each campaign. For example, the direct mail campaign costs us $30 per campaign, so we place a 30 before the D. Let's say that our company limits us to $2,000 of promotion budget per month. Then the equation for budget would be what we spend, which is 30 multiplied by D plus 40 multiplied by P plus 60 multiplied by S, and ensure that amount is less than or equal to our budget limit, which is $2,000.
© Stephan Sorger 2016; Data Science: Excel Tools; 27

28. Excel Solver Linear Optimization Element Equation
Objective Function Z = 30*D + 30*P + 40*S
Constraint #1: Budget B = 30*D + 40*P + 60*S <= $2,000
#2: Maximum campaigns/ month: D D <= 30
#3: Maximum campaigns/ month: P P <=20
#4: Maximum campaigns/ month: S S <= 10
Promotion Constraints
D ≤ 30: Cannot exceed 30 direct marketing campaigns per month
P ≤ 20: Cannot exceed 20 pay per click campaigns per month
S ≤ 10: Cannot exceed 10 social media campaigns per month
We had stated we had the capacity to produce only a finite number of campaigns. In our case, we can create 30 direct, 20 pay per click, and 10 social media campaigns per month. We would need to hire more people, or contract for more outside services, in order for us to create more. We express this constraint by simply stating the campaign variable less than or equal to the limit. For example, D is less than or equal to 30, meaning we can not create more than 30 direct marketing campaigns per month.
© Stephan Sorger 2016; Data Science: Excel Tools; 28

29. Identify limiting factors
Excel Solver: Execution
Optimization Model:
Setup
Optimization Model:
Execution
Optimization Model:
Interpretation
Specific format
Excel Solver function
Identify limiting factors
To run the optimization model, we follow a three step process. We set up the model in a specific format, we execute the model using Microsoft Excel's Solver function, and then interpret the results. We will cover each step.
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30. Excel Solver: Execution: Setup
Columns for D, P, and S parameters
Changing cells for D, P, and S A B C D E F 1 D P S 2
Changing Cells
Target Cell
(Contains objective equation) a b c 3
Constraint, Left Side
(Contains constraint equation) 4
Target Cell 30 30 40 d
Constraint, Right Side
(Contains constraint value) 5 6
Constraint #1: Budget 30 40 60 e f 7
Constraint #2: D ≤ 30 1 g h 8
Constraint #3: P ≤ 20 1 i j 9
Constraint #4: S ≤ 10
This slide shows the format I recommend. In the first column, we show the elements of the optimization model, including the rows for the changing cells, target cell, and constraints. In the middle set of columns, we show the information for the D, P, and S parameters, which the D, P, and S stand for direct , pay per click, and social media campaigns. In the right side set of columns, we see the values for the target cell and constraints. Microsoft Excel refers to variables as changing cells. In our case, the changing cells are D, P, and S. Excel refers to the objective function as the Target Cell. In our case, we would enter the objective equation in typical Excel equation form into the target cell. In the constraint area, place the equations for constraints in the left column cells, and enter the values for the constraint limits on the right side. I recommend placing all of the numeric values, such as coefficients, in separate cells, rather than hard-coding them into equations. This way, if you find that the effectiveness scores or costs change, they are easy to change. I also recommend placing ones in the cells corresponding to the D, P, and S variables, forming a diagonal. That way you can easily see which variables the constraints refer to. In the next slide, we run the model by using Excel's Solver function. 1 k l
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31. Excel Solver: Execution: Launch
Home …
Data …
Solver A B C D E F G
To run Microsoft Excel's Solver function, click on the Data tab, and then click on the Solver function. For Macintosh systems using Excel before 2011, go to Solver.com and download the free app that will add the Solver functionality into Excel. Macintosh machines with Excel 2011 and beyond will have Solver built in.
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32. Subject to the Constraints:
Excel Execution: Add Constraints
Solver Parameters
Add Constraint
Set Target Cell:
$E$4
Solve
Cell Reference:
Constraint:
Equal To:
Max.
Min.
$E$6
<=
=$F$6
By Changing Cells: OK
$B$2: $D$2
Options
Subject to the Constraints:
$E$6 <= $F$6
Add
$E$7 <= $F$7
Positive Integer constraint:
To ensure our answers are positive integers:
-Select box “Make variables non-negative”
-Add constraint: Changing Cells > Int (pull-down menu)
$E$8 <= $F$8
$E$9 <= $F$9
Solver will open a dialog box called Solver Parameters. Enter the location of target cell in the Set Target Cell box. Enter the location of the changing cells in the By Changing Cells box. We wish to maximize revenue, so click on the Max. button. For the constraints box, you will need to enter the constraints one at a time. To enter constraints, click on the Add button. The Add Constraints dialog box will include three elements--cell reference, sign, and constraint value. For cell reference, enter the location of the constraint equation. Recall we consolidated all constraint equations in the left columns of the constraint section of the spreadsheet. For the constraint box, enter the location of the constraint value. Recall we consolidated all constraint values in the right columns of the constraint section of the spreadsheet. The middle box is a pull-down, allowing you to select from various options, such as less than or equal to, greater than or equal to, and so forth. Because we face maximum limits, select the less than or equal to sign. Then click OK. Repeat the process until all the constraints are added, and then click on the Solve button in the Solver Parameters dialog box.
© Stephan Sorger 2016; Data Science: Excel Tools; 32

33. Excel Solver: Execution
Columns for D, P, and S parameters
Changing cells for D, P, and S A B C D E F 1 D P S 2
Changing Cells
Target Cell
(Contains objective equation) 30 20 5 3
Constraint, Left Side
(Contains constraint equation) 4
Target Cell 30 30 40
1700
Constraint, Right Side
(Contains constraint value) 5 6
Constraint #1: Budget 30 40 60
2000
2000 7
Constraint #2: D ≤ 30 1 30 30 8
Constraint #3: P ≤ 20 1 20 20 9
Constraint #4: S ≤ 10
Solver will populate the spreadsheet with the values it finds. It shows the number of D, P, and S campaigns needed to maximize impressions. It also shows the value of the target cell, which equals the total number of impressions. On the lower right, the spreadsheet also shows that we ran against the limits for D and P, but not for S. We discuss this topic further in the next slide. 1 5 10
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34. Solver Results: Summary
Excel Solver: Interpretation
Promotion Vehicle Solver Result Cost/Ad Total Cost per Vehicle
D: Direct Marketing 30 (30 max.) $30/Ad $900
P: Pay Per Click 20 (20 max.) $40/Ad $800
S: Social Media 5 (10 max.) $60/Ad $300
Total Spending $2,000
Solver Results: Summary
This slide shows the results produced by Solver. The table shows the summary, indicating the total cost per vehicle.
© Stephan Sorger 2016; Data Science: Excel Tools; 34

35. Solver Results: Constraints
Excel Solver: Interpretation
Promotion Vehicle Solver Result Max. Allowable Status
D: Direct Marketing Binding
P: Pay Per Click Binding
S: Social Media Not Binding
Budget $2,000 $2,0000 Binding
Solver Results: Constraints
The table shows the recommended number of campaigns to execute, relative to the capacity maximums. Note that we ran against the limits for D and P, but not for S. Solver refers to constrained situations as Binding and non-constrained situations as Non-Binding. We can interpret this result by stating that we have more capacity in social media than we need. For example, if you had a staff of 10 social media personnel executing one campaign each, you would have a capacity of 10 social media campaigns. By stating that the social media amount recommended is 5, with a maximum allowable of 10, we are in essence saying that 5 of the 10 people are doing nothing all day. We should re-deploy those excess people to doing something else. For example, we might train them to execute direct marketing or pay per click campaigns. If we re-deployed those assets over those two areas, we could then re-run the model with higher constraint values for direct marketing and pay per click, and generate more revenue as a result.
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36. Microsoft Excel Analysis ToolPak
In this section, we discuss the Analysis ToolPak add-in available for certain versions of Microsoft Excel.
It provides analysis functionality relevant to data science, as we shall soon see.
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37. Analysis ToolPak Purpose: Provides data tools for financial,
statistical, and
engineering
data analysis
PC Installation:
1. Click on upper left:
-Called “Office Button”
on Excel pre-2010
-Called “File Tab”
on Excel post-2010
2. Excel Options
The Analysis ToolPak add-in for Microsoft Excel provides regression analysis and other data tools
for financial, statistical, and engineering data analysis.
The add-in is available for Microsoft Windows based computers.
For computers with Apple Macintosh operating systems,
We will discuss an alternative in a few slides from now.
For Microsoft-Windows based computers,
we will need to enable the add-ins preinstalled in Excel.
To do so, select the upper left button.
Microsoft refers to the upper left button in pre-2010 versions of Excel
as the Office Button.
Microsoft changed the name to File Tab in post-2010 versions.
After clicking on the upper left, select Excel Options.
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38. Analysis ToolPak 3. Click “Add-Ins”
Next, we select Add-ins to bring up the add-ins screen.
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39. Analysis ToolPak 4. Click “Add-Ins” again
We select Excel Add-Ins from the pull-down menu and click Go.
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40. Analysis ToolPak 5. Select: “Analysis ToolPak” “Solver Add-in”
Click OK
We select the Analysis ToolPak checkbox for the Analysis ToolPak and the Solver Add-In checkbox to get Solver, and then we click OK.
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41. Analysis ToolPak 6. Access Add-ins through Data tab
Apple Macintosh users:
ToolPak not available.
Instead, download
StatPlus (free) add-in.
Go to
StephanSorger.com
and follow instructions
Once we enable the add-ins, we can access them through the Data tab.
For computers with Apple Macintosh operating systems, the
Analysis ToolPak is not available.
Instead, we recommend using commercially available analytics tools
such as StatPlus.
You can get links to the tools by going to the StephanSorger.com
Website and following the instructions on the site.
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42. Analysis ToolPak Several analysis tools available.
Typically use the following:
-Descriptive statistics
-Exponential smoothing
-Moving average
-Regression
-t-test; z-test
The Analysis ToolPak includes several tools useful for data analysis.
A few examples include
Descriptive statistics, for basic statistics summarizing a data set;
Exponential smoothing, to more easily identify long term trends in data;
Moving average, often used for data set smoothing;
Regression analysis to identify the relationship between a dependent variable and one or more independent variables;
And tools to test data validity, such as t tests and z tests.
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43. Outline/ Learning Objectives
Topic Description
Basic Statistics Mean, Median, Variance, Standard deviation, RMS
Pivot Tables Extract significance from large data sets
Solver Maximize/Minimize criteria subject to constraints
ToolPak Analysis add-in functionality of Excel
In this module, we covered several essential Excel tools.
We discussed basic statistics to summarize data sets,
such as mean, median, variance, standard deviation, and RMS.
We also covered various Excel functions, such as pivot tables,
To extract significance from data sets,
Solver, to maximize objectives subject to constraints;
And the Microsoft Excel Analysis ToolPak add-in
To obtain analytics functionality within Excel.
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