Intelligent Data Analysis

3Statistical Methods for Intelligent Data Analysis: Introduction and Various Concepts

Shubham Kumaram, Samarth Chugh, and Deepak Kumar Sharma

Division of Information Technology, Netaji Subhas University of Technology (Formerly Netaji Subhas Institute of Technology), Delhi, India

3.1 Introduction

In recent years, the widespread use of computers and the internet has led to generations of data on an unprecedented scale [1–4]. To make an effective use of this data it is necessary that this data must be collected and analyzed so that inferences can be made to improve various products and services. Statistics deals with collection, organization, and analysis of data. Organization and description of data is studied under descriptive statistics, whereas analysis of data, and making predictions based on it is dealt with in inferential statistics.

3.2 Probability

3.2.1 Definitions

Before we delve deeper into the understanding of statistical methods and its applications, it is imperative that we review some definitions and go through concepts that will be used all throughout the chapter [5].

3.2.1.1 Random Experiments

A random experiment is defined as follows:

1. All outcomes of an experiment that are possible must be known;
2. The outcome of each trial in an experiment must not be known before it takes place; and
3. Multiple trials, as per requirement, can be repeated without any change in the chances of attaining the outcome.

Outcome space, Ω, of an experiment refers to all possible values ...