TQM TOOLS & TECHNIQUES

April 28, 2020

TQM TOOLS & TECHNIQUES

Statics is defined as the science that deals with the collection, tabulation, analysis, interpretation & presentation of quantitative data.

statistical techniques are applicable in all situations where quantification is possible. Example- Engineering, commerce, industry, trade, economics, life science, earth science, physical science, medical science etc.

Data Vs Information

Data	Information
Data is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized.	When data is processed, organized, structured or presented in a given context so as to make it useful, it is called information.
Example-Each student's test score is one piece of data.	Example-The average score of a class or of the entire school is information that can be derived from the given data.

Types of data-

1. Attribute data-

The data obtained by counting are called discrete (or attributes) data. It’s has two types Yes/no types & Counting types. It’s very simple to collect as compared to variable data. Example- Mail deliver- it’s on time or not on time? Phone answer, product confirmation Number of students pass in class? etc.

2. Variable data-

The data obtained by actual measurement are continuous (or variable data). Example Sampling inspection plan we are taking one sample out of 10 parts & check the weight of it. so the reading should be digit or in number are variable like 250, 255, 248 grams etc.

Measures of Central Tendency: -

A measure of central tendency is a summary statistic that represents the center point or typical value of a dataset. These measures indicate where most values in a distribution fall and are also referred to as the central location of a distribution. In statistics, the three most common measures of central tendency are the mean, median, and mode.

1. Mean-The mean is also called arithmetic average, is the sum of the observations divided by numbers of observations.

2. Median-The median is the middle value. if data contains an odd numbers of items, the median item of the array. if there is an even number of items, it’s the arithmetic mean of two middle numbers.

To find median-

If there are ‘n’ observations of the variant& they are arranged in ascending order, then median is given by (n+1)/2 value if ‘n’ is odd.

If ‘n’ is even then median is taken average of (n/2) or (n/2 +1).

3. Mode-the mode is most commonly occurring value. it’s defined as “The valve of the variable which occurs most frequently i.e., the value of maximum frequency.

Measure of Dispersion or Measure of Precision-

The spread of observations around the Centre is known as dispersion. There are three measure of dispersion-

1. Range-It’s defined as the difference between the highest & lowest observations.

Mathematically Mean

R = X_h-X_l

Where R= Range

X_h= Highest observation value

X_l= Lowest observation value

2. Mean deviation-The mean deviation is the mean of absolute difference of the values from the mean, median or mode.

Mean Deviation

Where A= Mean or mode or median of observations.

3. Standard Deviation-It’s measure of spread of data. Standard deviation is compute as the square root of the mean of the squares of the difference of variate values from their mean.

Where S= Standard deviation

X_i= Observed values

X-bar = Mean of observed values

N = Number of observed values

Control Chart-

A control chart is a graph that displays data taken over time & the variations of data. It’s a tool to distinguish between chance & assignable causes of variations in a process.

The controlled chart is used to check whether the process is controlled statically or not. The main objective of using control chart is to determine when a process is out-of-control so that necessary action may be taken.