Description 0

This series is completely for beginners if you don't know the basics its completely fine then also you can easy learn from this series and understand the complex concept of maths 4 in a easy way

Branches Covered ( Comps , Mechanical , Civil , EXTC , Electrical , Electronics )

Handmade Notes : Notes are Brilliant , Easy Language , East to understand ( Student Feedback )

Exam ke Pehle Notes ek baar Dekhlo revision aise hi jata hai

Complex Integration
Matrices / Linear Algebra : Matrix Theory
Probability
Sampling
Mathematical Programming
Vector Calculus (Mech /Civil )
Linear Programming ( Mech /Civil )
Correlation (EXTC/Electrical/Electronics)

Coming Soon

Nonlinear Programming
Calculus of Variation
Linear Algebra : Vector space

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Complex Integration 7
- Lecture2.1
  
  Complex integration along a Line or Parabola with Solved Example 17 min
- Lecture2.2
  
  Complex integration along a Circle with Solved Example 12 min
- Lecture2.3
  
  Cauchy’s Theorem Basics & Cauchy’s integral formula Type 1 13 min
- Lecture2.4
  
  Cauchy’s Theorem Type 2 and Type 3 10 min
- Lecture2.5
  
  Cauchy’s Theorem Type 4 and Type 5 12 min
- Lecture2.6
  
  Taylor and Laurent series Full Basic with Solved Example part 1 15 min
- Lecture2.7
  
  Taylor and Laurent series Full Basic with Solved Example part 2 17 min
Probability Distribution 14
- Lecture3.1
  
  Introduction to Probability Distribution 07 min
- Lecture3.2
  
  Discrete Random Variable in Probability Distribution 15 min
- Lecture3.3
  
  Distribution function of Discrete Random Variable 10 min
- Lecture3.4
  
  Continuous Random Variable in Probability Distribution 09 min
- Lecture3.5
  
  Continuous Distribution Function in Probability Distribution 14 min
- Lecture3.6
  
  Expectation in Probability Distribution 12 min
- Lecture3.7
  
  Mean and Variance Part 1 in Probability Distribution 11 min
- Lecture3.8
  
  Mean and Variance Part 2 in Probability Distribution 13 min
- Lecture3.9
  
  Moments and Moments Generating Function Part #1 in Probability Distribution 13 min
- Lecture3.10
  
  Moments and Moments Generating Function Part #2 in Probability Distribution 11 min
- Lecture3.11
  
  Binomial Probability Distribution 18 min
- Lecture3.12
  
  Poisson Distribution with Solved Example 11 min
- Lecture3.13
  
  Normal Distribution Full Basic Concept 12 min
- Lecture3.14
  
  Normal distribution with Solved Example 20 min
Linear Algebra / Matrices 11
- Lecture4.1
  
  Introduction to Matrices 07 min
- Lecture4.2
  
  Eigen value and Eigen vector in Matrices 17 min
- Lecture4.3
  
  Eigen value and Eigen vectors for Types of Matrices 06 min
- Lecture4.4
  
  Cayley Hamilton Theorem with Example Part 1 09 min
- Lecture4.5
  
  Cayley Hamilton Theorem with Example Part 2 05 min
- Lecture4.6
  
  Cayley Hamilton Theorem with Example Part 3 04 min
- Lecture4.7
  
  Monic and Minimal Polynomial of a Matrix 05 min
- Lecture4.8
  
  Algebraic multiplicity And Geometric multiplicity 10 min
- Lecture4.9
  
  Functions of a Square Matrix in Matrices 07 min
- Lecture4.10
  
  Reduction of Quadratic form to Canonical form 13 min
- Lecture4.11
  
  Orthogonal Matrix with Solved Example 09 min
Sampling Theory 11
- Lecture5.1
  
  What Is Sampling 08 min
- Lecture5.2
  
  Large Sampling Problems 16 min
- Lecture5.3
  
  Large Sampling Test Theory+Numerical 16 min
- Lecture5.4
  
  Hypothesis Testing Full concept 02 min
- Lecture5.5
  
  Testing Difference Between Means(case_1) With Examples 11 min
- Lecture5.6
  
  Small Sample Test Example 13 min
- Lecture5.7
  
  Small Sample Test 04 min
- Lecture5.8
  
  Testing Difference Between Means(case_1) With Examples Part 2 05 min
- Lecture5.9
  
  Testing Difference Between Means(case_2) 12 min
- Lecture5.10
  
  Non Parametric Test Type_1_Numerical 08 min
- Lecture5.11
  
  Non Parametric Test 04 min
Mathematical Programming / Linear Programming 8
- Lecture6.1
  
  Basics of simplex method in LPP 11 min
- Lecture6.2
  
  Standard form of LPP Simplex Method 12 min
- Lecture6.3
  
  Types of Solutions in Simplex Method in LPP 14 min
- Lecture6.4
  
  How to Solve LPP using Simplex method 18 min
- Lecture6.5
  
  LPP Using Big M method ( Penalty Method ) 25 min
- Lecture6.6
  
  Duality of LPP ( Primal and Dual ) 17 min
- Lecture6.7
  
  Relationship between Primal and Dual 17 min
- Lecture6.8
  
  Dual Simplex Method in LPP 17 min
Vector Calculus 6
- Lecture7.1
  
  Divergence and curl 10 min
- Lecture7.2
  
  Scalar Potential 14 min
- Lecture7.3
  
  Vector differentiation basics and gradient 15 min
- Lecture7.4
  
  Green theorem 15 min
- Lecture7.5
  
  Gauss divergence theorem 07 min
- Lecture7.6
  
  Stokes Theorem 17 min
Calculus of Variation 11
- Lecture8.1
  
  Euler’s Differential Equation 11 min
- Lecture8.2
  
  Functions of Second Order Derivative 09 min
- Lecture8.3
  
  Gram Schmidt Process With Numerical 19 min
- Lecture8.4
  
  Hypothesis Concerning Several Proportions 07 min
- Lecture8.5
  
  Linear Combinations Of Vector 05 min
- Lecture8.6
  
  Non Parametric Test Type_2_Numerical 05 min
- Lecture8.7
  
  Pythagorean Theorem 04 min
- Lecture8.8
  
  Subspaces 10 min
- Lecture8.9
  
  Cauchy Schwarz Inequality in R^ 05 min
- Lecture8.10
  
  Lagranges Method 09 min
- Lecture8.11
  
  Rayleigh Ritz Method 12 min
Correlation (EXTC, Electrical , Electronics) 3
- Lecture9.1
  
  Introduction to correlation 08 min
- Lecture9.2
  
  Karl Pearson coefficient correlation 06 min
- Lecture9.3
  
  Spearman Rank 13 min
Regression (EXTC, Electrical , Electronics) 3
- Lecture10.1
  
  Introduction to Regression 05 min
- Lecture10.2
  
  Regression part 2 13 min
- Lecture10.3
  
  Regression part 3 15 min
Notes 10
- Lecture11.1
  
  Complex Integration Notes
- Lecture11.2
  
  Matrices Notes
- Lecture11.3
  
  Probability Notes
- Lecture11.4
  
  Sampling Theory Notes
- Lecture11.5
  
  Linear Programming Problems Notes
- Lecture11.6
  
  Calculus of Variation (EXTC, Electrical , Electronics Notes)
- Lecture11.7
  
  Linear Algebra Vector Space (EXTC, Electrical , Electronics Notes )
- Lecture11.8
  
  Correlation,Regression and Curve Fitting (EXTC, Electrical , Electronics Notes )
- Lecture11.9
  
  Graph and Group theory (IT)
- Lecture11.10
  
  Lattice theory (IT)
Solution Keys 5
- Lecture12.1
  
  Solution Key module 1
- Lecture12.2
  
  Practice problems solution key matrices
- Lecture12.3
  
  Practice problem solution key for Probability
- Lecture12.4
  
  Solution key for Sampling Theory
- Lecture12.5
  
  Linear Programming solution key
Z transform 8
- Lecture13.1
  
  Introduction to Z transform 16 min
- Lecture13.2
  
  Z Transforms Change Of Scale 05 min
- Lecture13.3
  
  Z Transforms Linearity 09 min
- Lecture13.4
  
  Z Transforms multiplication by k 10 min
- Lecture13.5
  
  Z Transforms Of Standard Functions 08 min
- Lecture13.6
  
  Z Transforms Shifting Property 05 min
- Lecture13.7
  
  Inverse Z transform using binomial expansion 08 min
- Lecture13.8
  
  Inverse Z transform using partial fractions 20 min

What Is Sampling

A sample is an outcome of a random experiment. When we sample a random variable, we obtain one specific value out of the set of its possible values. That particular value is called a sample. The possible values and the likelihood of each is determined by the random variable’s probability distribution.

There are two types of sampling methods:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

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