## How Statistics Became a Model-blind Data-reduction Enterprise? Karl Pearson

In the last blog post, we have covered Francies Galton and his Galton Board. In this post, we will talk about Karl Peason. Pearson was affected by Galton’s idea on correlation. He believes correlation is bigger than causation. Causation was reduced to nothing more than a special case of correlation. He said, “That a certain …

## How Statistics Became a Model-blind Data-reduction Enterprise? Francies Galton.

Date: 5 June 2021 This is Chapter 2 of the book, The Book of Why. This chapter is an account of the history of statistics and how it departs from understanding causation to only correlation. I personally did not validate the accuracy and completeness of the stories in the Chapter. I feel that the Author …

## If causation is not correlation, then what is it?

Date: 30 May 2021 I am currently reading a book called The Book of Why. I just finished Chapter 1, The Ladder of Causation, and would like to give you a quick summary on what I have learnt. In the recent advancement and success of machine learning and artificial intelligence, it seems that many problems …

## Seasonal Data: Making Sense of Data Chapter 16, 17, 18

I have combined a summary of Chapter 17, 18 into one blog post since they all talk about how to handle seasonal data. Chapter 17 talks about how to smoothing out seasonal data. Chapter 18 talks about how to adjust the data to allow for seasonality. Chapter 16 talks about Average and Range Charts which …

## Charts for Count Data: Making Sense of Data Chapter 13

In the following 3 chapters, the author will talk about count data. It may sound easy to count. But, there are some subtleties to keep in mind when interpreting count data. The author introduces a concept called Areas of Opportunity. Before we can define what is Areas of Opportunity and also give my own understand …

## Interpreting the Process Behavior Chart: Making Sense of Data Chapter 8

In this chapter, the author talks about how to detect the signal of assignable cause from a process behaviour chart. He first presents the characteristics of a predictable process, and then gives three major detection rules for an unpredictable process. However, my concern is how can we communicate them to the audience since it is …

## What Makes the XmR Chart Work? Making Sense of Data Chapter 11

In Chapter 7: Visualize Your Process Behavior, the author introduces a tool called the XmR chart. XmR chart is used to detect assignable causes/ unpredictable events/ events that are outside routine variations. With that purpose in mind, the XmR chart includes two limits, Upper Nature Process Limit (UNPL) and Lower Natural Process Limit (LNPL). Any …

## Visualize Your Process Behaviour: Making Sense of Data Chapter 7

In this chapter, the author introduces two types of variation, predictable and unpredictable, and the improvement strategies applied to each variation respectively. XmR chart is also presented as a tool to distinguish the types of variation. However, the author does not explain why XmR chart works in this Chapter. This made me a bit confused …

## Some Arithmetic: Making Sense of Data Chapter 6

This chapter is also basic. It is about some common summary statistics, relationships between ratios, percentages, and proportions, and data types. I am not going to cover the summary statistics here since I am sure you already know what is averages, medians, ranges, root mean square deviations, and standard deviations. The relationship between ratios, percentages, …

## Graphical Purgatory: Making Sense of Data Chapter 5

I think Chapters 4, 5, 6 are some general introduction to visualization. Although at this stage, I haven’t completed Chapter 6 and onward, I think the chapters are to prepare readers for more advanced topics and the author put those chapters in for completeness. The summary of Chapter 5 is actually simple: less is more. …