>> Hello, everyone. In this segment I will give a brief introduction to statistics. What is statistics? Statistics is the study of how best to collect and analyse the data, and how best to draw conclusions from data. Statistics is the science of learning from data and of measuring, controlling and communicating uncertainty. In this course we will make use of statistical thinking and methods. While doing this we will need to keep in mind three concepts. The first one is that statistics is an applied field with a wide range of practical applications. Second concept, statistics will help us to learn from real and interesting data. Third one, statistics advance our understanding of ourselves and our world. We know that data is messy and statistical tools are imperfect, but when we understand the strong and weak points of statistical tools we can use them to learn about the real world. When we have a problem or a question, and this question can come from diverse domains like bio-medicine, environment, engineering, business, therapy, banking or health, and we may need to use statistics or statistical analysis, then mainly there are three steps we need to process. The first step is to collect relevant data on the topic. As a second step we analyse the data. And a third one is to form a conclusion. In the language of statistics one of the most basic concepts is sampling. In most statistical problems a specified number of measurements, or data, a sample, is drawn from the much larger body of measurements, called the population. A population is a set of all measurements. A sample is a subset of measurements selected from the population. When we are presented with a set of measurements, whether a sample or a population, we will need to find a way to organize and summarize it. Differential statistics that presents techniques for describing sets of measurements is called descriptive statistics. If the set of measurements is -- is the entire population we need only to draw conclusions based on the descriptive statistics. However, it might be too expensive or too time consuming to work on the entire population. Descriptive statistics is the term given to the analysis of data that helps us to describe, show or summarize the data in a meaningful way. Such that, for example, patterns might emerge from the data. Descriptive statistics answers questions such as how widely dispersed is the data or what value is in the middle of the data. The branch of statistics that allows us to use samples or make generalizations about the populations from which the samples are drawn is called inferential statistics. Inferential statistics consists of techniques that allows us to use samples to make generalizations about the populations from which the samples were drawn. It is therefore important that sample accurately represents the population. The objective for inferential statistics is to make inferences. That is, draw conclusions, make predictions, make decisions. Inferential statistics aims to answer questions like, are there significant relationship in our measures? Or, does a particular average of one group differ from the average of another group? So descriptive statistics and inferential statistics helps us to do statistical analysis and lets us take different approaches for the solutions of our problems.