DOX Tools

4 min readOct 21, 2019

The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation[1]. Montgomery[2] specifies that experimental design is a tool for engineers and scientists to use for product design and development as well as process development and improvement.

Statistical software are specialized computer programs for analysis in statistics. In general, statistical software is needed to calculate necessary numerical values when you analyze the subject in DOX. There are various statistical software tools[3] you can use in your study. Some authors prefer to use open-source, public domain, or freeware tools in their DOX books while others use proprietary software or add-ons.

Recommendations

You can see couple of products in below table which I prefer to use in my studies.

Products — Open Source

GNU Octave — For help: GNU Octave Wiki
gretl — For help:Gretl User’s Guide
R — For help:R packages for DOX
SciPy — For help:DOX packages for Python

Products — Proprietary

EViews — For help:EViews Help
Maple — For help:Maple Help
Minitab — For help:Minitab Support
Mathematica — For help:Statistical Data Analysis
MATLAB — For help:Mathworks Documentation
SPSS — For help:SPSS Help

An Example

Example from Montgomery[2], p.25.

An engineer is studying the formulation of a Portland cement mortar. He has added a polymer latex emulsion during mixing to determine if this impacts the curing time and tension bond strength of the mortar. The experimenter prepared 10 samples of the original formulation and 10 samples of the modified formulation.

Figure: Screenshot of the Table 2.1. in Montgomery’s book[2], p.26.

Solution in R Language

See Appendix: R Code.

> t.test(y1,y2,var.equal=TRUE)	Two Sample t-testdata:  y1 and y2
t = -2.1869, df = 18, p-value = 0.0422
alternative hypothesis: true difference
                        in means is not equal to 0
95 percent confidence interval:
 -0.54507339 -0.01092661
sample estimates:
mean of x mean of y 
   16.764    17.042> t.test(y1,y2)	Welch Two Sample t-testdata:  y1 and y2
t = -2.1869, df = 17.025, p-value = 0.043
alternative hypothesis: true difference
                        in means is not equal to 0
95 percent confidence interval:
 -0.546174139 -0.009825861
sample estimates:
mean of x mean of y 
   16.764    17.042

Figure: Dot diagram, created with R.

Figure: Histogram, created with R.

Figure: Box plots, created with R.

Figure: The normal density function, created with R.

Solution in Wolfram Mathematica

In[4]:= y1={16.85,16.40,...,16.59,16.57}
Out[4]= {16.85,16.4,...,16.59,16.57}
In[5]:= y2={16.62,16.75,...,17.08,17.27}
Out[5]= {16.62,16.75,...,17.08,17.27}In[46]:= TTest[{y1,y2},Automatic, "TestData"]
Out[46]= {-2.18688,0.0421967}
In[48]:= Mean[y1]-Mean[y2]
Out[48]= -0.278
In[65]:= TTest[{y1,y2},0]
Out[65]= 0.0421967
In[69]:= MeanDifferenceCI[y1,y2]
Out[69]= {-0.546174,-0.00982586}
In[68]:= MeanDifferenceCI[y1,y2,EqualVariances->True]
Out[68]= {-0.545073,-0.0109266}