Statistics : BA (Applied) Psychology

Dr.Ruchi Gupta
ARUN KUMAR
Last Update August 1, 2021
0 already enrolled

About This Course

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

Target Audience

  • All Students

Curriculum

66 Lessons56h

Quadrant 1 Unit -I Introduction to Matrices Module-I

Lesson 1 Module-100:00:00

Quadrant 2 UNIT-I Elementary Row Transformations

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

Quadrant 1 UNIT 1 Rank of Matrix Module-I

In this lecture we will discuss the concept of Rank of a matrix, properties and the various methods to find out the Rank of a matrix. We will also learn about the Normal form of a matrix and Nullity of a Matrix. We will have discussed the application of Rank of a Matrix.We will also solve a few problems based on the Rank of a matrix.

Quadrant 1 UNIT1 Rank of Matrix Module -II

Quadrant 2 : Unit 1 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 4: Unit 1 Solving Linear Systems using Gaussian Elimination

Problems

Quadrant 2 UNIT 1 Gauss Jordan Row Reduction Method

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

Quadrant 3 Unit 1

FAQs

Quadrant 3 Unit !

FAQ

Quadrant 3 Unit 1

FAQ

Quadrant 4 Unit 1 Rank of a matrix

Problems

Quadrant 3 Unit 1

FAQ

Quadrant 4 Unit 1 Introduction to matrices

Problems

Quadrant 4 Unit 1 Introduction to matrices

Quiz

Quadrant 1UNIT 2 Homogeneous Linear Systems (Rank)Module -I

Quadrant 1UNIT 2 Homogeneous Linear Systems (Rank)Module-II

Quadrant 1 UNIT 2 Non Homogeneous Linear Systems(Rank) Module -I

In this lecture we will discuss the condition for consistency and inconsistency of systems of linear equation and nature of solution of a system of non homogenous linear system of equation. We will also go through the working rule for finding the solution of the equation AX=B.

Quadrant 1UNIT 2 Non Homogeneous Linear Systems(Rank) Module -II

Quadrant 1 UNIT 2 Eigen Value & Eigen Vector Module-I

In this module we introduce the concept of Eigen values and Eigen vectors and its applications.

Quadrant 1 UNIT 2 Eigen Value & Eigen Vector Module-2

Quadrant 4: Unit 2 Solving Homogenous Linear Systems

Problems

Quadrant 1 UNIT 2 Cayley Hamilton theorem Module-1

In this lecture, we will discuss some properties of eigen value and eigen vector and will solve a few problems based on properties of eigenvalues and eigenvectors and Cayley Hamilton theorem.

Quadrant 3-Unit 2

Faq on unit 2

Quadrant 1 UNIT 2 Cayley Hamilton theorem-module 2

Quadrant 1 UNIT 2 Diagonalization of matrices Module -I

In this lecture, we will see how eigenvalues and eigenvectors can be used to determine whether a square matrix is diagonalizable or not. We will also discuss the properties and application of diagonalization.

Quadrant 1 UNIT 2Diagonalization of matrices Module- II

Quadrant 3 Unit 2

FAQ

Quadrant 4 Unit 2 Eigenvalues

Problems

Quadrant 4:UNIT 2 Eigen values

quiz

Quadrant 3 Unit 2

FAQ

Quadrant 4 Unit 2 Cayley Hamilton

Problems

Quadrant 3 Unit 2

FAQ

Quadrant 4:Unit 2

Problems on eigen values and eigen vectors

Quadrant 1UNIT3 Lesson 11 Module 1

Quadrant 1 : Unit 3 Vector Spaces module II

Quadrant 1:UNIT3 Vector subspaces module 2

Quadrant 2: UNIT3 Vector Subspaces -II

Algebra of Subspaces

Quadrant 1: UNIT3 Linear combination of vectors

To know what is linear combination of vectors

Quadrant 1:UNIT3 Span of a Set

To understand span of a set

Quadrant 3: UNIT 3

Frequently Asked Questions 1

Quadrant 4: UNIT3 Vector Spaces

To study about vector spaces

Quadrant 1:Unit 3 Linear Independece of vectors

To study about linear independence and linear depence of vectors

Qudrant 1:Unit 3 Linear independece of vectors Module 2

problems on linear independence of vectors

Quadrant 3 Unit 3

FAQ 2

Quadrant 4 : UNIT3 Vetor subspaces

To study vector subspaces

Quadrant 2 UNIT3 Linear Independence and Dependence of vectors

To show llinear depeneence and of independence of vectors

Quadrant 2 Unit 3 Bases and Dimensions

We will study about basis and dimension of vector spaces

Quadrant 3 Unit 3

FAQ

Quadrant 2: Unit 3 Bases and Dimensions -2

continuation of bases and dimensions of vector spaces

Quadrant 3 Unit 3

FAQ

Quadrant 2:UNIT3 Linear Space

To Study about linear Spaces

Quadrant 2: UNIT3 Identical spaces

To study linear spaces

Quadrant 4 : Unit 3 Bases and Dimensions-1

Problems

Quadrant 4 Unit 3 Ass 1

Problems on vector spaces

Quadrant 4 Unit 3 Ass 2

Problems on vector subspaces

Quadrant 4:Unit 3 Ass 3

Problems on vector spaces

Quadrant 4 :Unit 3 Ass 4

Problems on linear combinations

Quadrant 4:Unit 3 Ass 5

Problems on vector spaces

Quadrant4: unit 3 Ass 6

Problems on vector spaces

Quadrant 4:Unit 3 Ass 7

Problems on vector spaces

Quadrant 4: Unit 3 Bases and Dimensions 2

Problems

Quadrant 4 :Unit 3 linear combination

Quadrant 4 Unit 3 Lesson 12

Problems

Quadrant 4 Unit 3 L 16

Problems

Quadrant 2 UNIT4 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: UNIT4 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 3 Unit 4

FAQ

Quadrant 2 Unit 4 Linear operator and similarity

Quadrant 4 UNIT4 Linear Transformation

Problems

Quadrant 2 UNIT4 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 2UNIT4 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 3 Unit 4

FAQ 2

Quadrant 2:UNIT4 Isomorphic spaces

To study about isomorphic spaces

Quadrant 4 UNIT4 Linear Algebra Ass 2

Problems

Quadrant 4 Unit 4 Ass 4

Problems on linear Transformation

Quadrant 4 Unit 4 A3

Problems on linear Transformation

Quadrant 4 Unit 4 Ass 5

Problems on linear transformation

Quadrant 4 Unit 4 A5

Problems on linear transformation

Quadrant 4:Unit 4 Ass 6

Problems on linear Transformation

Quadrant 4 Unit 4 A ss 7

Problems on LT

Quadrant 4 Unit 4 A7

Problems on LT

Quadrant 4 Unit 4 A8

Problems on LT

Quadrant 4 Unit 4 A9

Problems on Liner Transformation

Quadrant 2: UNIT 4 Linear Isomorphism

This topic will cover properties of isomorphism

Quadrant 3 Unit 4

FAQ 3

Quadrant 4 Unit 4 A 10

Problems on linear Transformation

Quadrant 4 :UNIT4Linear Algebra Ass 1

Problems

Quadrant 4 :UNIT4Linear algebra Ass 3

Problems

Quadrant 4 :Unit 4 L24

Problems

Quadrant 3 Unit 4

FAQ 4

Quadrant 4:UNIT5 Correlation

To study the behaviour of two variables

Quadrant 3 Unit 5

FAQ

Quadrant 4:UNIT 5 Rank Correlation

Problems related with rank correlation

Quadrant 3 Unit 5

FAQ 2

Quadrant 3 Unit 5

FAQ 3

Quadrant 4:UNIT5 Curve fitting 1

To fit a curve of higher degree when data is given

Quadrant 4:UNIT5 Curve Fitting 2

To fit exponential curve to given data

Quadrant 4: UNIT5 Probability Distribution-1

Problems on Binomial Distribution

Quadrant 4: UNIT5 Probabllity Distribution-2

Problems on Poisson distribution

Quadrant 3 Unit 5

FAQ

Quadrant 4: UNIT5 Probability Distribution 3

Problems on Normal Distribution

Quadrant 2 UNIT 6Lecture 36

SIMPLE RANDOM SAMPLING

Quadrant 2 UNIT6 Lecture 37

HYPOTHESIS TESTING

Quadrant3 Unit 6

FAQ

Quadrant 2 UNIT6Lecture 38

Test of significance for small samples

Quadrant 2 UNIT6 Lecture 40

Chi-Square Test

Quadrant 3 Unit 6

FAQ 2

Quadrant 4: unit 6 chi square

Problems on chi-square test

Quadrant 4: unit 6 large sample

Problems on hypothesis testing

Quadrant 3 Unit 6

FAQ

Quadrant 4:UNIT 6 Small Samples

Problems on hypothesis testing -small samples

Quadrant 4:UNIT 6 Student-test

Problems on Students t-Test

Quadrant 3 Unit 6

FAQ

Your Instructors

Dr.Ruchi Gupta

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ARUN KUMAR

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Duration 56 hours
Lectures
66 lectures
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