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Description
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 spaceNo items in this section -
Complex Integration
- Complex integration along a Line or Parabola with Solved Example
- Complex integration along a Circle with Solved Example
- Cauchy’s Theorem Basics & Cauchy’s integral formula Type 1
- Cauchy’s Theorem Type 2 and Type 3
- Cauchy’s Theorem Type 4 and Type 5
- Taylor and Laurent series Full Basic with Solved Example part 1
- Taylor and Laurent series Full Basic with Solved Example part 2
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Probability Distribution
- Introduction to Probability Distribution
- Discrete Random Variable in Probability Distribution
- Distribution function of Discrete Random Variable
- Continuous Random Variable in Probability Distribution
- Continuous Distribution Function in Probability Distribution
- Expectation in Probability Distribution
- Mean and Variance Part 1 in Probability Distribution
- Mean and Variance Part 2 in Probability Distribution
- Moments and Moments Generating Function Part #1 in Probability Distribution
- Moments and Moments Generating Function Part #2 in Probability Distribution
- Binomial Probability Distribution
- Poisson Distribution with Solved Example
- Normal Distribution Full Basic Concept
- Normal distribution with Solved Example
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Linear Algebra / Matrices
- Introduction to Matrices
- Eigen value and Eigen vector in Matrices
- Eigen value and Eigen vectors for Types of Matrices
- Cayley Hamilton Theorem with Example Part 1
- Cayley Hamilton Theorem with Example Part 2
- Cayley Hamilton Theorem with Example Part 3
- Monic and Minimal Polynomial of a Matrix
- Algebraic multiplicity And Geometric multiplicity
- Functions of a Square Matrix in Matrices
- Reduction of Quadratic form to Canonical form
- Orthogonal Matrix with Solved Example
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Sampling Theory
- What Is Sampling
- Large Sampling Problems
- Large Sampling Test Theory+Numerical
- Hypothesis Testing Full concept
- Testing Difference Between Means(case_1) With Examples
- Small Sample Test Example
- Small Sample Test
- Testing Difference Between Means(case_1) With Examples Part 2
- Testing Difference Between Means(case_2)
- Non Parametric Test Type_1_Numerical
- Non Parametric Test
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Mathematical Programming / Linear Programming
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Vector Calculus
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Calculus of Variation
- Euler’s Differential Equation
- Functions of Second Order Derivative
- Gram Schmidt Process With Numerical
- Hypothesis Concerning Several Proportions
- Linear Combinations Of Vector
- Non Parametric Test Type_2_Numerical
- Pythagorean Theorem
- Subspaces
- Cauchy Schwarz Inequality in R^
- Lagranges Method
- Rayleigh Ritz Method
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Correlation (EXTC, Electrical , Electronics)
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Regression (EXTC, Electrical , Electronics)
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Notes
- Complex Integration Notes
- Matrices Notes
- Probability Notes
- Sampling Theory Notes
- Linear Programming Problems Notes
- Calculus of Variation (EXTC, Electrical , Electronics Notes)
- Linear Algebra Vector Space (EXTC, Electrical , Electronics Notes )
- Correlation,Regression and Curve Fitting (EXTC, Electrical , Electronics Notes )
- Graph and Group theory (IT)
- Lattice theory (IT)
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Solution Keys
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Z transform
Distribution function of Discrete Random Variable
Distribution function of Discrete Random Variable
For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). This function provides the probability for each value of the random variable. The cumulative distribution function (c.d.f.) of a discrete random variable X is the function F(t) which tells you the probability that X is less than or equal to t. So if X has p.d.f. P(X = x), we have: F(t) = P(X £ t) = SP(X = x)
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