Don’t blame the U.S. Constitution for the Electoral College flaws. Blame the State Constitutions.
The rumblings over eliminating the Electoral College have continued unabated for years, with a spike in intensity after the 2016 election. Opponents of Electors point to the popular vote declaring Hillary Clinton the winner, yet the Electoral count gave the win to Trump. Clinton supporters called foul. Their argument: the people vote for the President. A simple majority should determine who wins.
Let’s step back in time to December 1788. The new U.S. Constitution was ratified in September of that year. …
I was born in 1960 just outside New York City to college degree’d parents. My father always voted Democrat, my mother Republican. Economic and social policy discussions around our dinner table were never driven by party ideology. My parents were too sensible for that.
My life path has led me to live in the Rockies, Northwest, Southwest, South, Atlantic Coast and now the Midwest. My wife and I discovered small town America 30+ years ago, and we’ve lived in small towns ever since.
At one time I thought I leaned conservative. 25 years ago I found out that, by the current definition, I’m not. Given my work as an engineer in Manufacturing, my religious faith and living in small towns, most of my associates/friends have been conservatives. I’ve spent a lot of time listening to their concerns and frustrations with society. Based on those interactions, I think these are the reasons for the adherence of rural America to…
Note: The modifications to this article were to correct typos and grammatical errors
In Part I of this post (https://medium.com/@ryandmonson/applying-data-science-in-manufacturing-part-i-background-and-introduction-ccb15743e001) I discussed how Manufacturing processes are data rich environments and the possibility of improving processes through the application of machine learning techniques. Of specific interest was the possibility of a new process control paradigm: instead of controlling process parameters per a value range, control through parameter relationships.
In Part II (https://medium.com/swlh/applying-data-science-in-manufacturing-part-ii-batch-process-methodology-and-lessons-learned-d18d360d8953) model building to establish parameter relationships was performed on a batch process dataset. A classification model was built which demonstrated excellent predictive accuracy.
For Part III (https://medium.com/analytics-vidhya/applying-data-science-in-manufacturing-part-iii-continuous-process-methodology-and-lessons-463021c33b05) a continuous process dataset was evaluated. This dataset was an order of magnitude larger than the batch process both row wise and column wise. Calibration of the target measurements was a concern and this author felt further information was needed from the process owner to build a useful model. …
In Part II of this post (https://medium.com/@ryandmonson/applying-data-science-in-manufacturing-part-ii-batch-process-methodology-and-lessons-learned-d18d360d8953) several models for predicting alloy grade from a batch manufacturing process were created. Classification modeling was far more accurate than Regression modeling in predicting the training target variable. At the end of the article a post mortem was documented, outlining lessons learned and thoughts on making the modeling results useful to the batch process manufacturing operation.
In Part III a continuous manufacturing process will be analyzed. This process differs from the batch process as follows:
It is characteristic of human nature to be inclined to regard anything which is disagreeable as untrue, and then without much difficulty to find arguments against it- Sigmund Freud
We are all of us all the time,
coming together and falling apart.
The point is, we are not rocks.
Who wants to be one anyway?
Impermeable, unchanging, our history already played out. — John Rosenthal
“Ok, Boomer. Let’s hear it. Let’s hear your perspective.”
My father pulled out the “on” button, turned the knob to channel 2, 3 or 4 on our black and white Zenith TV so the entire family could watch the news reports when MLK was assassinated. …
In Part I of this post (https://medium.com/@ryandmonson/applying-data-science-in-manufacturing-part-i-background-and-introduction-ccb15743e001) I hypothesized that Machine Learning modeling and subsequent control of process parameters could help reduce variation in Manufacturing.
In this post I’ll go through steps to create a predictive model for alloy grade using inputs from a metal alloy manufacturing dataset on the Kaggle website. Training and testing datasets for the metal alloy manufacturing are found on Kaggle at https://www.kaggle.com/esotericazzo/metal-furnace-dataset. All coding is in Python.
READ AND SUMMARIZE DATASETS
Code for importing Numpy, Pandas and OS:
import numpy as np
import pandas as pd
#Input data files are available in the read-only "../input/" directory
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
Data is like garbage: you better be sure what you’re going to do with it before you collect it- Mark Twain
This is a four part post:
-Part I — Background and Introduction
-Part II — Batch Processing: Methodology and Lessons Learned
-Part III — Continuous Processing: Methodology and Lessons Learned
-Part IV — Summary and Conclusions
After 30+ years with various Manufacturing organizations as a New Product Development, Process and Quality Engineer plus a stint as an Organizational Development Consultant I temporarily left industry to pursue education in Data Science.
My course of study included 20 guided projects, assignments where a public data set was analyzed to answer a question or solve a problem. None of the guided projects were associated with Manufacturing. That’s understandable, given the conservative culture in that sector of the economy. Data is closely guarded. Records are still completed with pen and paper. New technologies are cautiously employed, and only when necessary, rarely as an opportunity. …
“Breakthrough is the creation of good change, control the prevention of bad change…all managerial activity is directed at either breakthrough or control- J.M. Juran
In a competitive economy, above all, the quality and performance of the managers determine the success of a business; indeed, they determine its survival — Peter Drucker
The calls for change within business organizations over the extent of my career (30+ yrs) are too numerous to cite. Generally warnings about ceasing to exist are part of those calls. …
“Far too many corporations think they have a smart system and stupid workers. They’ve got it backwards.- Bruce E. Babbit”
It is more convenient to assume that reality is similar to our preconceived ideas than to freshly observe what we have before our eyes- Robert Fritz.
I was having a dialog with a friend recently. I highly prize speaking with this individual. Unlike the usual discussion ( same root as percussion or concussion, a heaving back and forth in a winner-take-all competition), we come away from our time together having tweaked our positions on the topic. …
“The right to be consulted is earned and re-earned, by demonstrating the capacity to be helpful.” — G.B. Ranney, Youden Address, “Context of Statistical Practice”, ASQ Statistics Division Newsletter, Vol. 17, №1
“One quality characteristic of statistical services might be whether the plan for a study, or the discussion of findings, or the description of how to use a method is designed to be understood by the user.” — ibid.
(Note: This article is a follow on to my recently published article “”Do Data Science Practitioners get too ‘Scientific’? “)
Many years ago I wrote a mini paper for the American Society for Quality (ASQ) Statistics Division titled “”Left Brained and Right out of Touch”. Though the article exuded a sense of moral superiority (time and experience have knocked much of that out of me), the concepts and conclusions still have relevance. …