# Linear Algebra & Statistical Techniques Dr.Ruchi Gupta Seema Aggarwal SAVITTA SAINI
Last Update June 4, 2021

Linear algebra is the branch of mathematics concerning linear equations which provides concepts which are useful to many areas of computer science. It’s really useful in Data Science when data written as vectors and then operations performed  on them in order to measure them. Linear Algebraic methods are necessary to do that.

Statistics is about collection, organization, displaying, analysis, interpretation and presentation of data

• All Students

44 Lessons

#### Quadrant 2 Elementary Row Transformations

In this lecture we will introduce elementary row transformations on matrices.

#### Quadrant 2 Solving Linear Systems using Gaussian Elimination

In this lecture we will learn to the study of the nature of the solutions of a system of linear equations.

#### Quadrant 2 &4 : Bases and Dimensions-1

We will study about basis and dimension of vector spaces

#### Quadrant 2: Bases and Dimensions -2

continuation of bases and dimensions of vector spaces

To study linear spaces

#### Quadrant 2 Gauss Jordan Row Reduction Method

In this lecture we will learn solving the system of linear equations by Gauss Jordan Row Reduction Method .

#### Quadrant 1 &4 : Vetor subspaces Module 1

To study vector subspaces

#### Quadrant 1:Linear combination of vectors

To know what is linear combination of vectors

#### Quadrant 1:Span of a Set

To understand span of a set

#### Quadrant 2 Properties Of Linear Transformations

In this lecture ,we will discuss some special types of linear transformations .We will also examine some elementary properties of linear transformations.

#### Quadrant 2: The matrix of a linear transformation continued

In this lecture we will discuss the matrix representation of a linear transformation and will solve a few problems.

#### Quadrant 2 Linear operator and similarity

In this lecture we will show that any two matrices for the same linear operator (on a finite-dimensional vector space) with respect to different ordered bases are similar.

#### Quadrant 2 Kernel and Image of a linear transformation

In this lecture, we will study two special subspaces associated with a linear transformation T: V → W, called the “kernel” and “range” of T. We will also illustrate techniques for calculating bases for both the kernel and range. We will see how dimensions of kernel and range are related to the dimension of the domain of a linear transformation.

#### Quadrant 2 Kernel and Image of a linear transformation continued

In this lecture we will discuss about the dimension theorem and will also solve some problems.

#### Quadrant 4 – Correlation Analysis

To determine correlation coefficient.

#### Quadrant 4 – Regression Analysis

To compute regression coefficients and regression equations.

To apply test of significance

#### Quadrant 2: Vector Subspaces -II

Algebra of Subspaces

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### Seema Aggarwal

Assistant Professor

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### SAVITTA SAINI

Assistant Professor

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Free
Level
All Levels
Lectures
44 lectures
Subject
Language
English

#### Material Includes

• pdf notes

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