READING 3 ︎︎︎ Six Sigma - Statistical Tools

Basic statistics

Why Using Statistical Tools in Lean Six Sigma?

Statistics is the science of collecting, analyzing, presenting, and interpreting data (Britanica). The word ‘’data’’ refers to information that has been collected from an experiment, a survey, an historical record, etc. Statistics is the methodology which we use for interpreting and drawing conclusions from the collected data.

There are two major types of statistics. The branch of statistics devoted to the summarization and description of data is called descriptive statistics. The branch of statistics concerned with using sample data to make an inference about a population of data is called inferential statistics.

In Lean six sigma, we use both branches of statistics, our goal is to gain an understanding of a problem from data. Any data analysis should contain following steps:


Descriptive statistics:

Descriptive statistics are numbers that quantitatively describe or summarize features of a collection of information. They are just descriptive; they do not involve generalizing beyond the data at hand. Generalizing from our data to another set of cases is the business of inferential statistics.

Inferential statistics:

In a work environment, doing experiments we often rely on a sample --- that is, a small subset of a larger set of data --- to draw inferences about the larger set. The larger set is known as the population (a production line output, a city’s population, bank clients…) from which the sample is drawn. Inferential statistical analysis infers properties of a population by assuming that the observed data set is sampled from a larger population
We use correlation, regression, probability, estimation and hypothesis testing to draw approximate numbers for the whole population from the sample.




Data types:




Qualitative data

Qualitative data or categorical data are measures of 'types' and may be represented by a name, symbol, or a number code. It is a kind of data that can be expressed in descriptions and feelings.  (Qualitative = Quality)

For example, a list of the products bought by different families at a grocery store would be categorical data, since it would go something like (milk, eggs, toilet paper…), we have two types of qualitative data: nominal and ordinal.

Nominal data: it is any type of data used to label something without giving it a numerical value (gender, native language…).

Ordinal data is qualitative data categorized in a particular order or on a ranging scale, the order of the qualitative information matters more than the difference between each category (ex: liking a flavor on a scale from 1 to 10).

Quantitative data

Quantitative data are measures of values or counts and are expressed as numbers. (Quantitative = Quantity), we have two types of quantitative data: discrete and continuous.

Discrete data (countable) are whole numbers, and are usually a count of objects. For instance, one study might count how many pets’ different families own.

Measured data, in contrast to discrete data, are continuous, and thus may take on any real value. For example, the amount of time a group of children spent watching TV would be measured data.