Welcome to Microsoft Excel 2016 2. Basic Operations 3. Printing in Excel 4. Using Functions 5. Decision Functions 6. Regression Analysis 9. VBA User-Defined Functions 10. VBA Subroutines 11. Using Solver 13. Numerical Integration 14. Differential Equations 15. Statistics for Experimenters. . For Excel® 2007, always download SolverSetup. In Excel® 2010, choose File Help and look in the lower right. In Excel® 2013 and Excel® 2016, choose File Account About Excel® and look at the top of the dialog. Download SolverSetup64 ONLY if you see ‘‘64-bit’’ displayed.
The textbook uses XLMiner software throughout. Here’s how to get it for your course.
For Instructors: Setting Up the Course Code
To set up a course code for your course, please email Frontline Systems at [email protected], or call 775-831-0300, press 0, and ask for the Academic Coordinator. Course codes MUST be renewed each semester.
The course code is free, and it can usually be issued within 24 to 48 hours (often the same day). It will enable your students to download and install XLMiner® for Education with a 140 day license, and will enable Frontline Systems to assist students with installation, and provide technical support to you during the course.
The App provides all 19 interactive functions included in the Excel Analysis ToolPak, plus a 20th function often requested by users, logistic regression. The App's Task Pane input fields and worksheet output will be very familiar to Excel Analysis ToolPak users. The 20 functions are: ANOVA: Single Factor. ANOVA: Two-Factor with Replication. Jun 11, 2018 Install Data Mining Add-in In Excel 2016 11 Jun 2018. Edit: This has also been confirmed working in Excel 2019 / Office 365. Ever since the release of Excel 2016 a compatible SQL Server Data Mining Add-in has been missing. There has been numerous requests for Microsoft to release an official update but so far this hasn’t happened.
Please give the course code, plus the instructions below, to your students. If you’re evaluating the book for adoption, you can use the course code yourself to download and install the software as described below.
For Students: Installing XLMiner® for Education
1) To download and install XLMiner® for Education from Frontline Systems, to work with Microsoft Excel® for Windows, please visit: www.solver.com/xlminer-textbook-license
2) Fill out the registration form on this page, supplying your name, school, email address (key information will be sent to this address), Course Code (obtain this from your instructor), and Textbook Code (enter SDMBI3).
3) On the download page, change 32-bit to 64-bit ONLY if you’ve confirmed that you have 64-bit Excel® (see below). Click the Download Now button, and save the downloaded file (SolverSetup.exe or SolverSetup64.exe).
4) Close any Excel® windows you have open.
5) Run SolverSetup/SolverSetup64 to install the software. When prompted, enter the installation password and the license activation code contained in the email sent to the address you entered on the form above. If you have problems downloading or installing, please email [email protected] or call 775-831-0300 and press 4 (tech support). Say that you have XLMiner® for Education, and have your course code and textbook code available.
If you have problems setting up or solving your model, or interpreting the results, please ask your instructor for assistance. Frontline Systems cannot help you with homework problems.
Gopro download to mac. • If you purchase this textbook but you aren’t enrolled in a course, call 775-831-0300 and press 0 for assistance with the software.
• If you purchased the e-book edition, contact [email protected] with some form of ‘proof of purchase’, and mention the book title ('Data Mining for Business Intelligence' or 'Data Mining for Business Analytics').
• For Excel® 2007, always download SolverSetup. In Excel® 2010, choose File > Help and look in the lower right. In Excel® 2013 and Excel® 2016, choose File > Account > About Excel® and look at the top of the dialog. Download SolverSetup64 ONLY if you see ‘‘64-bit’’ displayed.
Excel at Data Mining – Creating a Classification Matrix
Today, I’m going to show you how to create and read a Classification Matrix in less than 5 minutes with the Microsoft Excel data mining add-in. The assumption of this post is that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server with Analysis Services to which you have access rights.
For the example, we will be using a tutorial spreadsheet that can be found on Codeplex here. You also need to attach the AdventureworksDW2012 data file to SQL Server. The file can be downloaded here.
Our goal is to create a Classification Matrix based on a mining structure with all of its associated models that we have created previously.
Read below if you are unable to view the video walkthrough.
Step 1: Creating the Profit Chart
We go to the DATA MINING tab, the Accuracy and Validation group, and select the Classification Matrix icon. From here, select the Mining Structure that you wish to analyze. In this case we will be analyzing a Naïve Bayes Model and a Logistic Regression Model that we previously created using Adventureworks2012DW. Click Next. On the Specify Column to Predict screen, make sure that BikeBuyer, Show results as percentage, and Show results as count are selected.
Click Next and then Finish. And, that is how easy it is to actually create a Classification Matrix!
Step 2: Reading the Classification Matrix
Before we start looking at the Classification Matrix, let’s take a moment to think about how we can analyze our models using test data that has actual results in it. We’ll take customer A and have our model look at her information. It predicts that she WILL purchase a bike. Next, we can see what she actually did in the dataset. We have two possible outcomes: she either bought a bike and validated our model or she did not buy a bike and our model “missed” on her. The first case we call a true positive, and the second case we call false positive. Thinking about a NO prediction will lead to similar categories of true negative and false negative. An easy way to remember these terms is that first word tells you if you actual and prediction match and the second word tells you the prediction. True negative means that actual matched the prediction, and the prediction was NOT BUYING. False positive means that the actual does not match the prediction, and the prediction was a BUYING. Note that positive and negative depends on the definition of your model. We could have easily tried to predict people who would not buy bikes, and this would reverse all our definitions because NOT BUYING becomes the positive and BUYING becomes the negative.
So, now we realize that we have four categories (true positive, false positives, true negatives, false negatives) that can be summarized as a 2 x 2 matrix. This is exactly what we did when we created the Classification Matrix.
Starting at the top, we see an overview breakdown of how well the models performed against the test data. Keep in mind that Total Correct tells how many the model was right on for both positive and negative results.
Then below that, we see two matrices for the Bayes Model. One of them has percentages and the other has counts. Across the top of our matrix, we have our actuals and along the side we have our predictions. At each intersection of the matrix, we have a number that tells us how many of the test results fell into that bucket. At the intersection of 0 (actual) with 0 on the percentage matrix, we see 61.91%. This tells us that the model was accurately able to identify 61.91% of the people who ended not purchasing a bike (true negatives). Moving down one spot, we see that the remaining 38.09% were false negatives in that we predicted customers would not buy but they ended up buying a bike. The counts matrix shows the actual raw numbers used to calculate the percentages in the percentage matrix.
And that is how you create and read a Classification Matrix in less than five minutes using the Excel Data Mining Add in.
Billy Decker is a consultant at StatSlice Systems. He graduated with a dual degree in Physics and Mathematics from the University of Texas at Austin and received his Masters Degree in Physics from the University of Texas at Arlington. He previously worked for Global Technical Services as a Senior Training Analyst and Bell Hellicopter as an Instructional Designer. His technical experience includes, but is not limited to, SQL, SAP, Business Objects, QlikView, and Sharepoint.
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