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RSeek.org R-project Search Engine. Swirl - Students. The swirl R package makes it fun and easy to learn R programming and data science.

swirl - Students

If you are new to R, have no fear. On this page, we'll walk you through each of the steps required to begin using swirl today! Step 1: Get R In order to run swirl, you must have R 3.0.2 or later installed on your computer. If you are on a Linux operating system, please visit our Installing swirl on Linux page. If you need to install R, you can do so here. GrapheR: A GUI for base graphics in R. How did I miss the GrapheR package?

GrapheR: A GUI for base graphics in R

The author, Maxime Hervé, published an article about the package [1] in the same issue of the R Journal as we did on googleVis. Yet, it took me a package update notification on CRANbeeries to look into GrapheR in more detail - 3 years later! And what a wonderful gem GrapheR is. The package provides a graphical user interface for creating base charts in R. It is ideal for beginners in R, as the user interface is very clear and the code is written along side into a text file, allowing users to recreate the charts directly in the console. Adding and changing legends? Introduction to R. Free Statistical Programming Courses.

This list is a little different than the others.

Free Statistical Programming Courses

Most of this site is focused on R materials that you can consume quickly. The links below are for online coursework to help further your understanding of statistical programming. If you have the time to devote to regular study of this material, you'll definitely be glad that you did. If this all looks too advanced, take a look at the free statistics courses. One thing to note is that these are not degree seeking programs.

Computing for Data Analysis. Code School - Try R. Design of Experiments – Full Factorial Designs. In designs where there are multiple factors, all with a discrete group of level settings, the full enumeration of all combinations of factor levels is referred to as a full factorial design.

Design of Experiments – Full Factorial Designs

As the number of factors increases, potentially along with the settings for the factors, the total number of experimental units increases rapidly. In many cases each factor takes only two levels, often referred to as the low and high levels, the design is known as a 2^k experiment. Given a three factor setup where each factor takes two levels we can create the full factorial design using the expand.grid function: expand.grid(Factor1 = c("Low", "High"), Factor2 = c("Low", "High"), Factor3 = c("Low", "High"))

Experimentation for Improvement. Experimentation for Improvement. About the Course Would you like to: improve the quality of drinking water;make a stronger concrete or brick;increase the sales from your store;find the right combination of settings for your favourite recipe;improve the quality of your company's product;reduce waste;minimize energy use?

Experimentation for Improvement

No matter what your area of interest (and there are no limits to the applications!) , it is clear: better experiments save time and money, and lead to improvement. In this course we will learn to use efficient factorial experiments, fractional factorials and response surface methods. If these terms sound intimidating, don’t fear! By the end of this 6-week course you will be able to design your own experimental program, changing multiple variables, and interpret the experimental data using simple tools, based on sound statistical principles. These tools and methods can be beneficial to solve the challenges you set for yourself above. List of numerical analysis software. Home - Scilab. The R Project for Statistical Computing. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. SEMATECH e-Handbook of Statistical Methods.

a520438. Free Statistical Software. Design of Experiments (DOE) Tutorial. Exploratory data analysis. In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.

Exploratory data analysis

A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. What Can Classical Chinese Poetry Teach Us About Graphical Analysis? - Statistics and Quality Data Analysis. A famous classical Chinese poem from the Song dynasty describes the views of a mist-covered mountain called Lushan.

What Can Classical Chinese Poetry Teach Us About Graphical Analysis? - Statistics and Quality Data Analysis

The poem was inscribed on the wall of a Buddhist monastery by Su Shi, a renowned poet, artist, and calligrapher of the 11th century. Deceptively simple, the poem captures the illusory nature of human perception. Written on the Wall of West Forest Temple. Anscombe's quartet. All four sets are identical when examined using simple summary statistics, but vary considerably when graphed Anscombe's quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed.

Anscombe's quartet

Anscombe's quartet. Data Analysis, Error and Uncertainty using Excel. Data Analysis, Error and Uncertainty - data analysis using Excel Produced by Graham Currell, University of the West of England, Bristol and David Read, University of Southampton, in association with: ● Royal Society of Chemistry, 'Discover Maths for Chemists' website, and● Essential Mathematics and Statistics for Science, 2nd Edition Graham Currell and Antony Dowman, Wiley-Blackwell, 2009 Return to Excel Tutorial Index.

Data Analysis, Error and Uncertainty using Excel

Data Analysis, Error and Uncertainty using Excel. Interactive Statistical Calculation Pages. Optimization of Analytical Methods Using Factorial Designs. Statistical Discovery Software from SAS. The R&D manager survival toolbox: DoE: Factorial designs. Experimental Design (Industrial DOE) © Copyright StatSoft, Inc., 1984-2002 Experimental Design (Industrial DOE) DOE Overview Experiments in Science and Industry Experimental methods are widely used in research as well as in industrial settings, however, sometimes for very different purposes.

Experimental Design (Industrial DOE)

The primary goal in scientific research is usually to show the statistical significance of an effect that a particular factor exerts on the dependent variable of interest (for details concerning the concept of statistical significance see Elementary Concepts). In industrial settings, the primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few (costly) observations as possible. Differences in techniques. Amc technical brief - fractional-factoria-designs-technical-brief-36_tcm18-214869.pdf. 8_Fractional Factorial Designs.PDF - chap8.1.pdf. Lesson 6: The 2^k Factorial Design. Introduction The 2k designs are a major set of building blocks for many experimental designs. These designs are usually referred to as screening designs. The 2k refers to designs with k factors where each factor has just two levels. These designs are created to explore a large number of factors, with each factor having the minimal number of levels, just two.

D.O.E. Handbook - Experimental Design. Experimental Designs. Most Practical DOE Explained (with Template) Kim Niles February 26, 2010 For purposes of learning, using, or teaching design of experiments (DOE), one can argue that an eight run array is the most practical and universally applicable array that can be chosen. There are several forms of and names given to the various types of these eight run arrays (e.g., 2^3 Full Factorial, Taguchi L8, 2^4-1 Half Fraction, Plackett-Burman 8-run, etc.), but they are all very similar.

A free Microsoft Excel spreadsheet with a 2^3 Full Factorial array showing the mathematical calculations accompanies this article (click below to download it). Generic steps for using the spreadsheet, precautions, and additional advice are included below. Click here to download template Viewing Tip: Usually, you can click on the icon link above to view the document in a new window — it may open within your browser using the application (in this case either Word or Excel). Generic Steps For Using The Attached Spreadsheet. Basic Design of Experiments Templates - DOE Excel - SigmaXL.com. Design of Experiments in Excel.