Quantitative Analysis with R
What do you mean by Six Sigma? Just answer this in your mind as you finished, continue to read the following
Six Sigma is a comprehensive set of statistical tools used for the variance analysis whose outcome is to reduce the variation of a process. Thanks to the IT era, we have a tremendous support from statistical software to solve the mathematical equation of the six sigma tools instead of solving the problem by your brain and hands.
In the market, we have statistical commercial software like Minitab, SAS, STAT, SPSS, and R where R is absolutely standing out due to free of charge, again absolute free for any one regardless of non-profit or profit organizations, while it is so superior in the analysis capacity as mentioned in this research of statistical software comparisons. Besides, the interesting fact is that increasing data-science jobs related to R is so predominant, which was revealed by this published chart below:
Let us name reasons why R is so powerful and dominating:
- 1. It is open source, free of charge for any kind of users and which company the users work for
- 2. Compatible with various operating systems: Windows, Linux, and Mac OSX
- 3. Used for statistical analysis, data manipulation, big data, visualization and many applications in different fields, such as economy, finance, biology, and many more
- 4. Integrated with many software, even Excel via RExcel
- 5. So many we let you explore by your own
There is only one weakness which makes you hesitate to use R. That is the syntax that must be known and remembered by users for their analysis with R. Let us illustrate it as below:
1. You need to know which syntax or function is used for the your specific analysis. For instance, in case you want to make descriptive statistic of a variable, namely "SAS", stored in the data of "softwares.csv" in R, you would type the syntax as follow:
setwd("C:/Tap tin thuc hanh R/Muc 1-Gioi thieu") # set directory to the data
softwares <- read.csv ("softwares.csv", header=TRUE) # attach the data to a defined object of "softwares"
summary(softwares$SAS) # Show result of descriptive statistics of "SAS" variable
The result would be displayed as below:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8620 12500 32100 34320 52800 64400
2. Besides, parameters inside the syntax or function are also critical for working in R. For instance, you would like to plot 3 variable in the same plot:
matplot(x=softwares$Year, softwares[,2:4], type = c("b"), pch=1:3,col = 1:3,
main="Comparison of Statistical software", xlab="Year",
ylab="Publications on Google Scholar") # format the plot
legend("topleft", legend = c("R", "SAS", "SPSS"), col=1:3, pch=1:3) # adding the legend and its position
3. Because R is a programming language, so you are required to type exact syntaxes otherwise an error is informed. Looking back the first example when you use the "summary" function to display descriptive statistics. But now, you type "Summary" instead of "summary", the error will look like that
#Error in (function (classes, fdef, mtable): unable to find an inherited method for function ‘Summary’ for signature ‘"integer"
"Your Problem, Our Solvers"! Lean helper provide you a solver, namely "Quantitative Analysis with R" in which we consolidate most typical syntaxes for basic statistics and Six Sigma. With the handbook in your hands, you just only do the following simple steps to take you through "A to Z" analysis, which open the world of statistics and Six Sigma:
- 1. Define which analysis you want to take, for instance "One-Way ANOVA"
- 2. Searching the name of analysis method and go its page
- 3. Cope the syntax and paste it into R
- 4. Input your own data into the syntax
- 5. Get the analysis result
Just do 5 above simple steps, you as well as your organization can save thousands of dollar for buying the license of the commercial software while utilizing the most powerful machine of analysis, R
|| downloadable link in pdf
||Anyone who works and interests in Statistics and Six Sigma
- 1. R overview and installation
- 2. R Functions and objects
- 3. Basic statistics
- 4. Visualization with plots
- 5. Analysis of Variance
- 6. Regression analysis
- 7. Six Sigma methods: Gage R&R, % Yield, Taguchi loss function, Six Sigma Process Capacities, and more
- 8. Data science: Text Mining
|No of pages
||M.Sc. Hien, Nguyen Ngoc - Six Sigma Black Belt
||Vietnamese. For English, please take note as you checkout!
||Q&A and basic training for R via Skype