Statistical Analysis in MS Excel using KADD Stat!

Most of us know that we can do few statistical analysis using ‘Analysis Tool pack’ addin in Microsoft Excel. We can also do more than that using KADD STAT.

KADD STAT is an add-in which comes for free of cost and easy to use. Mostly all the versions after Excel 2003 would support. So, students and other people who want to use some basic statistical stuff can utilize this awesome one.

Download KADD here https://kelley.iu.edu/mabert/e730/KADD.xla

How to Install the New Version of KADD

Also see video here

Option A

If KADD is currently not stored on your computer, then use the following steps:

  1. Download KADD into a folder on your hard drive
  2. Open up Excel
  3. Go to the Tools option and click on Add-Ins
  4. Click on the Browse button (on the right side of the dialog box) and go to the folder with KADD
  5. Double-Click on KADD
  6. KADDSTAT 3.02 will now show up as an Add-In option. Check it and press OK
  7. KADD will then show up as a menu option across the top. You may have to close Excel and then open it up again before you see KADD in Excel across the top

Option B

IF you have already installed the older version of KADD, then use the following steps to upgrade it to the new version:

  1. Download KADD to your hard drive
  2. Open up Excel
  3. Go to the Tools option and click on Add-Ins. KADD (the older version) will be an option in Add-In menu. Remove it by clicking off the check mark and shut down Excel
  4. Now open up Excel again and follow Steps 3 through 7 from Option A

Ready to rock?

A Glance at the available list of analysis in the add-in

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List of analysis

You can calculate probability values

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Probabilities
  1. Find confidence Intervals in different scenarios
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Confidence Intervals

2. Plot a Normal curve

3. Plot ‘Box plots’ for your data

4. Find out minimal sample size for different scenarios

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Sample Size

5. Perform Hypothesis testing

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Hypothesis Testing

6. Draw different quality control charts and find out process capability for normal data

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Quality Control

7. Find out correlation and regression 

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Regression and Correlation

8. Become a forecasting pro ! 🙂

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Forecasting

9. Some financial calculations

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Risk and Return

10. Find out Expected value and variability

11. Perform Decision Trees 

12. Linear Programming

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Reference Tables

Doing this is simple if you have data and know what to do. Give it a try and enjoy ! Happy learning…

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Steps to Learn Data Science using R

One of the common difficulties individuals face in learning R is lack of an organized way. They don’t know, from where to start, how to proceed, which way to choose? However, there is a surplus of good free resources accessible on the Internet, this could be overwhelming as well as puzzling at the mean time.

After mining through infinite resources & archives, here is a comprehensive Learning way on R to learn R from the beginning. This will help you to learn R rapidly and proficiently.

Step 1: Download and Install R

The easy way to proceed is to download the basic version of R and installation instructions from CRAN site. R is available for Windows, Mac and Linux. Windows and Mac users most likely want one of these versions of R. R is part of many Linux distributions, you should check with your Linux package management system in addition to the link above.

You can now install various packages. There are more than 9000 packages in R for different purposes. Here is a link to understand packages called CRAN Views.  You can accordingly select the sub type of packages that you want.

To install a package you can just do this

For example, if we want to install a package called “animation” then we use

install.packages("animation")

Normally the package should just install, however:

  • if you are using Linux and don’t have root access, this command won’t work.
  • you will be asked to select your local mirror, i.e. which server should you use to download the package.

You must also install RStudio. It helps R coding much easier since it allows you to type multiple lines of code, handle plots, install and maintain packages and navigate your programming environment.

Step 2: Learn the basics

You need to start by knowing the basics of the language, libraries and data structure. The R track from Datacamp is the best place to start your journey. See the free Introduction to R course at https://www.datacamp.com/courses/introduction-to-r. After doping this course, you would be comfortable writing basic scripts on R and also understand data analysis. Alternately, you can also see Code School for R at http://tryr.codeschool.com/

If you want to learn R offline on your own time – you can use the interactive package swirl from http://swirlstats.com

Primarily learn  read.table, data frames, table, summary, describe, loading and installing packages, data visualization using plot command.

Step 3: Learn Data Management:

You need to use them a lot for data cleaning, especially if you are going to work on text data. The best way is to go through the text manipulation and numerical manipulation assignments. You can learn about connecting to databases through the RODBC  package and writing sql queries to data frames through sqldf  package.

Step 4: Study specific packages in R– data.table and dplyr Here we go ! Here is a brief introduction to numerous libraries. We need to start practising some common operations.

  • Practice the data.table tutorial  thoroughly here. Print and study the cheat sheet for data.table
  • Next, you can have a look at the dplyr tutorial here.
  • For text mining, start with creating a word cloud in R and then learn learn through this series of tutorial: Part 1 and Part 2.
  • For social network analysis read through these pages.
  • Do sentiment analysis using Twitter data – check out this and this analysis.
  • For optimization through R read here and here

Step 5: Effective Data Visualization through ggplot2

  • Read Edward Tufte and his principles on how to make data visualizations here . Especially read on data-ink, lie factor and data density.
  • Read about the common pitfalls on dashboard design by Stephen Few.
  • For learning grammar of graphics and a good way to do it in R. Go through this link from Dr Hadley Wickham creator of ggplot2 and one of the most brilliant R package creators in the world today. You can download the data and slides as well.
  • Are you interested in visualzing data on spatial analsysis. Go through the amazing ggmap package.
  • Interested in making animations thorugh R. Look through these examples. Animate package will help you here.
  • Slidify will help supercharge your graphics with HTML5.

Step 6: Learn Data mining and Machine Learning Now, we come to the most valuable skill for a data scientist which is data mining and machine learning. You can see a very comprehensive set of resources on data mining in R here at http://www.rdatamining.com/ . The rattle package really helps you with an easy to use Graphical User Interface (GUI).  You can see a free open source easy to understand book here at http://togaware.com/datamining/survivor/index.html You will go through an overview of  algorithms like regressions, decision trees, ensemble modelling and   clustering.  You can also see the various machine learning options available in R by seeing the relevant CRAN view here. Resources:

Step 7: Practice Practice with example data available with you and on the internet. Stay in touch with what your fellow R coders are doing by subscribing to http://www.r-bloggers.com/ , http://stats.stackexchange.com and www.stackoverflow.com. Go through the questions and answers that users come up with. Start interacting by asking questions and providing the answers for the questions which you can ! Happy learning !!! 🙂