thoughts on philosophy and science

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
We've covered the basic methods of applying statistics to your design and verification environment. Now let's present them all together and discuss what method is most suited for a particular circumstance.

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
Now that we are familiar with all the basic statistical methods we can look at using them in the manufacturing space to monitor a process for error.

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
This article will demonstrate analysis of variance to analyze the results of a design of experiment (DOE).

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
This article will introduce the concept of correlation on a data set using the R Project software.

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
This article will show step by step how to determine if one variable is dependent on a second variable. This method is useful when you are counting data and presenting it in table form.

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

Introduction
This article will introduce linear regression on a data set using the R Project software. This is useful if your data is "on a line" rather than a Gaussian distribution.

Preface
This series is aimed at providing tools for an electrical engineer to gain confidence in the performance and reliability of their design. The focus is on applying statistical analysis to empirical results (i.e. measurements, data sets).

I think dark matter is an awful explanation for an explanation of the "missing matter" problem. We can't see it? Oh it must be invisible?!? Lame.

I have another idea and it is much more simple. It starts with the basic question: What if the gravitational effects come from beyond the edge of our universe?

A couple of points of evidence and a concept appropriated from astronomy:

I recently wired my lab for ethernet and began automating my testing with Python scripts and the awesome Python(x,y) environment curated and maintained by Google. Freed from the burden of so much test setup this got me thinking about how to design and execute a test which fully and accurately characterized the unit under test. This is broken into the following objectives: