## Statistics Simplified

We are bombarded with tons of data everyday in a business environment. The key challenge is how to convert this data into information and information into knowledge and finally knowledge into wisdom. While wisdom is an individual's ability to apply knowledge or understanding in a given situation; it is the transformation of data into knowledge where statistics comes to our rescue. Statistics is the science of collecting, organizing, and interpreting data whether it is numerical or non-numerical. In other words, statistics helps us measure our business processes leading to higher order of manageability. Click to read more >>

## Scales of Measurement

The choice of statistical analysis techniques for a data set depends on it's scale of measurement. Therefore it is critical to develop a clear understanding of the same. Click to read more >>

## Measure of Central Tendency

Central tendency is a kind of middle point of a distribution and whereas dispersion is the degree of scatter or spread of the data. There are several measures of central tendency. We will explore three such measures - median, mode, and mean in detail. Click to read more >>

## Measure of Dispersion

The frequency distribution graph of any data set clearly illustrates the dispersion in that data set. There are several measures of dispersions. We will discuss conceptual details of Range, Interquartile Range, Mean Absolute Deviation (MAD), Standard Deviation, Variance, Coefficient of Variation, and Z-Score. Click to read more >>

## So, where is the information?

We have drawn so many graphs. We have covered tons of mathematics - mean, mode, median, and MAD. But where is the information? When we look at mean of a data set, what do we understand from it? Markov's Inequality and Chebychev's Inequality answer some of these questions. Click to read more >>

## Probability and Statistics

Probability and statistics are closely related. A simple example illustrates this relationship. Click to read more >>

## Distribution

Distribution simply tells us how data elements of a given data set are distributed within its range. It assumes utmost importance in context of process management. Natural variations always occur in a process and they can not be traced to a specific cause. They are random within a predictable range or in other words, it follows a distribution pattern. It is really interesting to note that although each outcome of a random event is uncertain when seen in isolation; but collectively they follow a predictable pattern. This pattern is called its distribution function. Let us take an example to understand the concept. Click to read more >>

## Distribution in Real Life

Distribution has several real life examples that touch our daily lives. Examples covered in this discussion include data compression, reliability, project planning and quality management. Click to read more >>

## Sampling

Sampling is a method to draw inference about one or more characteristics of a large group of items by examining a smaller but representative selection of group items. Steps to successful sampling are discussed, including topics like determining the sample size and selecting the sampling technique. Click to read more >>

## Scatter Plot

Scatter plot is a technique to discover relationship between a dependent variable (y) and an independent variable (x) by plotting “y” against “x”. Once plotted, it is very easy to spot the correlation between “x” and “y” variables. Click to read more >>