Understanding key concepts of Statistics

The key question for Artificial Intelligence is 'Can Machines Think!'

For understanding machine learning algorithms, the basic concepts required are related to Data Analytics.

R and MATLAB is the programming languages which needs to be known for data analytics

Descriptive Statistics and Exploratory Data Analysis

  • How do we describe data
  • Concerned with data visualization graphically
  • Different ways of representing data


1. Measures of central tendency
- Summarizing the data

Understanding of Mean, Median and Mode.

2. Measures of dispersion
- Inherent variability

Understanding of Variance or standard deviation

Probability distribution is richest way to express the data. Understanding of entire characterization of data set

Inferential Statistics

 Deals with Population and Samples from a broader universe

CASE: Have finite population. Defining state-space.

Population: Height of every single student of university
Sample: 30 randomly chosen, for instance, people from Population

We have some output (mean/median) and we compare it to some probable value.

ANNOVA is used for analysis of variance.

Regression

 Regression analysis used inferential statistics and many other modules for essence of machine learning.

Ordinary least square Technique is used for regression

Its about creating relationship between dependent variable (output variable) with independent variable (input variable).

Independent Variable: Annual Rainfall in particular rural region measured annually
Dependent Variable: Crop yield.

Dataset of input and outputs. Core idea is to create relationship between these to factors. We can fit a line through the data to represent relationship.

Finally, prediction is the key idea. For given independent variable, I need to predict dependent variable.
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Reference: nptel.iitm.ac.in


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