Random Process and Linear Algebra (MA3355) - Anna University
Course Instructor
Promath
₹999.00 ₹1999.00 50% OFF
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Course Overview
Schedule of Classes
Course Curriculum
6 Subjects
Unit - I : Probability and Random Variables
131 Learning Materials
1.1 Probability and Random Variable
1.1.1 Introduction of Probability
PDF
1.1.2 Set definitions
PDF
1.1.3 Operations on sets
PDF
1.1.4 Laws of algebra of sets
PDF
1.1.5 Experiments and sample spaces
PDF
1.1.6 Discrete and Continuous sample spaces
PDF
1.1.7 Events
PDF
1.1.8 Probability Definitions and axioms
PDF
1.1.9 Theorems on probability
PDF
1.1.10 Exercises
PDF
1.2 Mathematical model of experiments
1.2.1 Mathematical (or) classical definition of probability
PDF
1.2.2 Solved Problem 1
Video
2:31
1.2.3 Solved Problem 2
Video
2:18
1.2.4 Solved Problem 3
Video
2:34
1.2.5 Solved Problem 4
Video
2:00
1.2.6 Solved Problem 5
PDF
1.2.7 Mathematical tools
PDF
1.2.8 Solved Problem 1
Video
4:31
1.2.9 Solved Problem 2
PDF
1.2.10 Solved Problem 3
PDF
1.2.11 Exercises
PDF
1.3 Joint probability
1.3.1 Joint Probability
PDF
1.3.2 Introduction to Probability as Relative Frequency
PDF
1.3.3 Solved Problem 1
PDF
1.4 Conditional probability
1.4.1 Introduction of conditional probability
PDF
1.4.2 Solved Problem 1
Video
4:9
1.4.3 Solved Problem 2
Video
4:34
1.4.4 Solved Problem 4
PDF
1.5 Total probability and baye's theorem
1.5.1 Theorem of Total Probability
PDF
1.5.2 Baye's theorem
PDF
1.5.3 Solved Problem 1
Video
8:10
1.5.4 Solved Problem 2
Video
9:48
1.5.5 Solved Problem 3
PDF
1.5.6 Solved Problem 4
PDF
1.5.7 Solved Problem 5
PDF
1.5.8 Solved Problem 6
PDF
1.5.9 Exercises
PDF
1.6 Independent Events
1.6.1 Independent Events (multiplication law of probability)
PDF
1.6.2 Solved Problem 1
Video
1:31
1.6.3 Solved Problem 2
Video
2:1
1.6.4 Solved Problem 3
Video
3:30
1.6.5 Solved Problem 4
PDF
1.6.6 Solved Problem 5
Video
5:00
1.6.7 Exercises
PDF
1.7 Random Variable
1.7.1 Introduction of Random variable
PDF
1.7.2 Definition of Random variable
PDF
1.7.3 Discrete continuous and mixed random variable
PDF
1.7.4 conditions for a function to be a random variable
PDF
1.7.5 Probability distribution function
PDF
1.7.6 Probability of mass function
PDF
1.7.7 Probability of discrete random variable
PDF
1.7.8 Solved Problem 1
Video
9:52
1.7.9 Solved Problem 2
Video
3:55
1.7.10 Solved Problem 3
PDF
1.7.11 Solved Problem 4
Video
5:32
1.7.12 Solved Problem 5
PDF
1.7.13 Exercises
PDF
1.8 Continuous Random Variable
1.8.1 Probability distribution function
PDF
1.8.2 Probability density function
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1.8.3 Probability of continuous random variable
PDF
1.8.4 Solved Problem 1
Video
3:17
1.8.5 Solved Problem 2
Video
7:59
1.8.6 Solved Problem 3
Video
7:7
1.8.7 Solved Problem 4
PDF
1.8.8 Solved Problem 5
PDF
1.8.9 Solved Problem 6
PDF
1.8.10 Exercises
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1.9 Binomial distribution
1.9.1 Introduction to binomial Distribution
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1.9.2 Mean of binomial distribution
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1.9.3 Variance of binomial distribution
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1.9.4 Moment generating function
PDF
1.9.5 Solved Problem 1
Video
11:5
1.9.6 Solved Problem 2
Video
9:24
1.9.7 Solved Problem 3
Video
6:27
1.9.8 Solved problem 4
PDF
1.9.9 Solved problem 5
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1.9.10 Solved problem 6
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1.9.11 Solved problem 7
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1.9.12 Solved problem 8
PDF
1.9.13 Exercises
PDF
1.10 Poisson distribution
1.10.1 Introduction to poisson distribution
PDF
1.10.2 Mean of poisson distribution
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1.10.3 Variance of poisson distribution
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1.10.4 Moment of generating function
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1.10.5 Properties of poisson distribution 1
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1.10.6 Properties of poisson distribution 2
PDF
1.10.7 Solved Problem 1
Video
6:28
1.10.8 Solved problem 2
Video
7:2
1.10.9 Solved Problem 3
Video
8:28
1.10.10 Solved Problem 4
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1.10.11 Solved Problem 5
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1.10.12 Solved Problem 6
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1.10.13 Solved Problem 7
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1.10.14 Solved Problem 8
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1.10.15 Exercises
PDF
1.11 Geometric Distribution
1.11.1 Introduction to Geometric Distribution
PDF
1.11.2 Solved Problem 1
Video
4:39
1.11.3 Solved Problem 2
Video
9:6
1.11.4 Solved Problem 3
Video
5:15
1.11.5 Solved problem 4
PDF
1.11.6 Solved problem 5
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1.11.7 Solved problem 6
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1.11.8 Solved problem 7
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1.11.9 Solved problem 8
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1.11.10 Exercises
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1.12 Uniform distribution
1.12.1 Introduction to uniform distribution
PDF
1.12.2 Moment generating function of uniform distribution
PDF
1.12.3 Solved Problem 1
Video
8:23
1.12.4 Solved Problem 2
Video
3:35
1.12.5 Solved Problem 3
Video
9:26
1.12.6 Solved Problem 4
PDF
1.12.7 Exercises
PDF
1.13 Normal/Gaussian Distribution
1.13.1 Introduction to gaussian or Normal distribution
PDF
1.13.2 Application of gaussian distribution
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1.13.3 Characteristics of the gaussian distribution
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1.13.4 Normal distribution as a limiting form of binomial distribution
PDF
1.13.5 Solved Problem 1
Video
2:58
1.13.6 Solved Problem 2
Video
5:9
1.13.7 Solved Problem 3
Video
13:30
1.13.8 Solved Problem 4
PDF
1.13.9 Solved Problem 5
PDF
1.13.10 Exercises
PDF
1.14 Exponential Distribution
1.14.1 Introduction to Exponential Distribution
PDF
1.14.2 Exponential Distribution
PDF
1.14.3 Solved Problem 1
Video
6:55
1.14.4 Solved Problem 2
Video
4:46
1.14.5 Solved Problem 3
Video
5:54
1.14.6 Memoryless Property of the exponential distribution
PDF
1.14.7 Solved Problem 4
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1.14.8 Solved Problem 5
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1.14.9 Exercises
PDF
Unit - II : Two - Dimensional Random Variables
105 Learning Materials
2.1 Two Dimensional Random Variables
2.1.1 Introduction
PDF
2.1.2 Joint Probability Distribution
PDF
2.1.3 Joint Cumulative Distribution Function
PDF
2.1.4 Marginal Probability Distribution Function
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2.1.5 Conditional Probability Distribution Function
PDF
2.1.6 Expectation of a Function g(X, Y)
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2.2 Problems based on Discrete Random Variables
2.2.1 Introduction
PDF
2.2.2 Solved Problem 1
Video
10:35
2.2.3 Solved Problem 2
Video
10:14
2.2.4 Solved Problem 3
Video
10:56
2.2.5 Solved Problem 4
PDF
2.2.6 Solved Problem 5
PDF
2.2.7 Solved Problem 6
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2.2.8 Solved Problem 7
PDF
2.2.9 Exercises
PDF
2.3 Problems based on Continuous Random Variables
2.3.1 Introduction
PDF
2.3.2 Solved Problem 1
Video
6:51
2.3.3 Solved Problem 2
Video
8:28
2.3.4 Solved Problem 3
Video
8:33
2.3.5 Solved Problem 4
PDF
2.3.6 Solved Problem 5
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2.3.7 Solved Problem 6
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2.3.8 Solved Problem 7
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2.3.9 Solved Problem 8
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2.3.10 Solved Problem 9
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2.3.11 Solved Problem 10
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2.3.12 Solved Problem 11
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2.3.13 Exercises
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2.4 Correlation
2.4.1 Covariance
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2.4.2 Correlation
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2.4.3 Types of Correlation
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2.4.4 Methods of studying correlation
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2.4.5 Scatter Diagram & Simple Graph Method
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2.4.6 Karl Pearson's Coefficient of Correlation
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2.4.7 Properties of Correlation Coefficient
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2.4.8 Solved problem 1
Video
6:18
2.4.9 Solved problem 2
Video
8:29
2.4.10 Solved problem 3
Video
8:26
2.4.11 Solved problem 4
Video
9:9
2.4.12 Solved problem 5
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2.4.13 Solved problem 6
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2.4.14 Solved problem 7
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2.4.15 Solved problem 8
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2.4.16 Solved problem 9
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2.4.17 Solved problem 10
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2.4.18 Solved problem 11
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2.4.19 Solved problem 12
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2.4.20 Solved problem 13
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2.4.21 Solved problem 14
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2.4.22 Solved problem 15
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2.4.23 Solved problem 16
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2.4.24 Solved problem 17
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2.4.25 Exercises
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2.5 Correlation and Grouped data
2.5.1 Correlation of Grouped Data
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2.5.2 Solved problem 1
Video
15:7
2.5.3 Solved problem 2
Video
12:46
2.5.4 Solved problem 3
PDF
2.5.5 Exercises
PDF
2.6 Rank Correlation Coefficient
2.6.1 Rank Correlation Coefficient
PDF
2.6.2 Solved problem 1
Video
3:42
2.6.3 Solved problem 2
Video
9:6
2.6.4 Solved problem 3
PDF
2.6.5 Solved problem 4
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2.6.6 Solved problem 5
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2.6.7 Equal or Repeated Ranks
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2.6.8 Solved problem 1
Video
9:37
2.6.9 Solved problem 2
PDF
2.6.10 Exercises
PDF
2.7 Regression
2.7.1 Introduction
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2.7.2 Methods of studying Regression
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2.7.3 Regression Lines
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2.7.4 Solved problem 1
Video
9:45
2.7.5 Solved problem 2
Video
15:28
2.7.6 Solved problem 3
Video
7:3
2.7.7 Solved problem 4
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2.7.8 Solved problem 5
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2.7.9 Solved problem 6
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2.7.10 Solved problem 7
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2.7.11 Angle Between Two Regression Lines
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2.7.12 Solved problem 1
Video
4:12
2.7.13 Solved problem 2
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2.7.14 Solved problem 3
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2.7.15 Exercises
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2.8 Transformation of Random Variables
2.8.1 Transformation of Random Variables
PDF
2.8.2 Solved problem 1
Video
9:38
2.8.3 Solved problem 2
Video
10:7
2.8.4 Solved problem 3
Video
13:50
2.8.5 Solved problem 4
PDF
2.8.6 Solved problem 5
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2.8.7 Solved problem 6
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2.8.8 Exercises
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2.9 Central Limit Theorem
2.9.1 Central Limit Theorem [Lindberg-Levy’s form]
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2.9.2 Convergence
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2.9.3 Normal area Property
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2.9.4 Uses of Central Limit Theorem
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2.9.5 Theorem
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2.9.6 Solved problem 1
Video
4:39
2.9.7 Solved problem 2
Video
6:14
2.9.8 Solved problem 3
PDF
2.9.9 Solved problem 4
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2.9.10 Solved problem 5
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2.9.11 Solved problem 6
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2.9.12 Solved problem 7
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2.9.13 Area under the Standard Normal Probability Curve
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2.9.14 Exercises
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Unit - III : Random Process
73 Learning Materials
3.1 Random Processes/Stochastic Processes
3.1.1 Introduction
PDF
3.1.2 Random Process
PDF
3.1.3 Random Variable vs Random Process
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3.1.4 Classification of Random process
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3.1.5 Statistical Averages
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3.1.6 Properties of Random process
PDF
3.1.7 Applications of Random process
PDF
3.1.8 Solved Problem 1
Video
13:1
3.1.9 Solved Problem 2
PDF
3.2 Strict sense stationary (SSS) Processes
3.2.1 Stationary Random Processes/Strict Sense Stationary(SSS) processes
PDF
3.2.2 Important formulae
PDF
3.2.3 Applications of SSS
PDF
3.2.4 Solved Problem 1
Video
9:39
3.2.5 Solved Problem 2
Video
5:31
3.2.6 Solved Problem 3
Video
11:26
3.2.7 Solved Problem 4
PDF
3.2.8 Solved Problem 5
PDF
3.2.9 Exercises
PDF
3.3 Wide sense Stationary (WSS) processes
3.3.1 Wide Sense Stationary(WSS) processes
PDF
3.3.2 Applications of WSS
PDF
3.3.3 Solved Problem 1
Video
9:24
3.3.4 Solved Problem 2
Video
9:50
3.3.5 Solved Problem 3
Video
7:31
3.3.6 Solved Problem 4
Video
8:51
3.3.7 Solved Problem 5
PDF
3.3.8 Solved Problem 6
PDF
3.3.9 Solved Problem 7
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3.3.10 Solved Problem 8
PDF
3.3.11 Exercises
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3.4 Ergodic Processes
3.4.1 Ergodic Theorem
PDF
3.4.2 Ergodic processes
PDF
3.4.3 Solved Problem 1
Video
13:22
3.4.4 Solved Problem 2
Video
5:1
3.4.5 Solved Problem 3
Video
8:38
3.4.6 Solved Problem 4
Video
11:35
3.4.7 Solved Problem 5
PDF
3.4.8 Exercises
PDF
3.5 Markov Processes
3.5.1 Introduction to Markov Process
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3.5.2 One step transition probability
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3.5.3 Transition probability matrix
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3.5.4 Probability distribution of a Markov chain
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3.5.5 Chapman – Kolmogorov Theorem
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3.5.6 Stationary distribution for a Markov chain
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3.5.7 Solved Problem 1
Video
4:56
3.5.8 Solved Problem 2
Video
15:24
3.5.9 Solved Problem 3
Video
9:26
3.5.10 Solved Problem 4
PDF
3.5.11 Solved Problem 5
Video
11:35
3.5.12 Solved Problem 6
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3.5.13 Solved Problem 7
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3.5.14 Solved Problem 8
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3.5.15 Solved Problem 9
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3.5.16 Solved Problem 10
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3.5.17 Exercises
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3.6 Poisson Process
3.6.1 Introduction
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3.6.2 Poisson Process
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3.6.3 Probability function of Poisson process X(t)
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3.6.4 Mean and variance of Poisson Process
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3.6.5 Autocorrelation of Poisson Random Process
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3.6.6 Covariance and Coefficient of correlation of Poisson Process
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3.6.7 Properties of Poisson Process - Property 1 & 2
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3.6.8 Property 3 & 4
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3.6.9 Property 5
PDF
3.6.10 Solved Problem 1
Video
3:30
3.6.11 Solved Problem 2
Video
4:41
3.6.12 Solved Problem 3
Video
9:15
3.6.13 Solved Problem 4
PDF
3.6.14 Solved Problem 5
PDF
3.6.15 Solved Problem 6
PDF
3.6.16 Solved Problem 7
Video
5:43
3.6.17 Solved Problem 8
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3.6.18 Solved Problem 9
Video
6:31
3.6.19 Exercises
PDF
Unit - IV : Vector spaces
101 Learning Materials
4.1 Vector Spaces
4.1.1 Introduction
PDF
4.1.2 Theorem 1
PDF
4.1.3 Theorem 2
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4.1.4 Theorem 3
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4.1.5 Theorem 4
PDF
4.1.6 Solved problem 1
Video
15:27
4.1.7 Solved problem 2
Video
14:58
4.1.8 Solved problem 3
Video
11:32
4.1.9 Solved Problem 4
PDF
4.1.10 Solved Problem 5
PDF
4.1.11 Solved Problem 6
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4.1.12 Solved Problem 7
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4.1.13 Solved Problem 8
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4.1.14 Solved Problem 9
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4.1.15 Solved Problem 10
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4.1.16 Solved Problem 11
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4.1.17 Exercises
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4.2 Sub Spaces
4.2.1 Introduction
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4.2.2 Theorem 1
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4.2.3 Theorem 2
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4.2.4 Theorem 3
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4.2.5 Theorem 4
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4.2.6 Theorem 5
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4.2.7 Theorem 6
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4.2.8 Solved problem 1
Video
3:41
4.2.9 Solved problem 2
Video
4:27
4.2.10 Solved problem 3
Video
4:37
4.2.11 Solved Problem 4
PDF
4.2.12 Solved Problem 5
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4.2.13 Solved Problem 6
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4.2.14 Solved Problem 7
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4.2.15 Solved Problem 8
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4.2.16 Solved Problem 9
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4.2.17 Solved Problem 10
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4.2.18 Solved Problem 11
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4.2.19 Exercises
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4.3 Linear Combination and Systems of Equations
4.3.1 Introduction
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4.3.2 Linear Span
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4.3.3 Theorem 1
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4.3.4 Theorem 2
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4.3.5 Theorem 3
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4.3.6 Theorem 4
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4.3.7 Theorem 5
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4.3.8 Theorem 6
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4.3.9 Theorem 7
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4.3.10 Solved problem 1
Video
3:58
4.3.11 Solved problem 2
Video
6:3
4.3.12 Solved problem 3
Video
5:55
4.3.13 Solved Problem 4
PDF
4.3.14 Solved Problem 5
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4.3.15 Solved Problem 6
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4.3.16 Solved Problem 7
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4.3.17 Solved Problem 8
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4.3.18 Solved Problem 9
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4.3.19 Solved Problem 10
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4.3.20 Exercises
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4.4 Linear Dependence and Linear Independence
4.4.1 Introduction
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4.4.2 Theorem 1
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4.4.3 Theorem 2
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4.4.4 Theorem 3
PDF
4.4.5 Solved problem 1
Video
5:56
4.4.6 Solved problem 2
Video
5:39
4.4.7 Solved problem 3
Video
6:7
4.4.8 Solved Problem 4
PDF
4.4.9 Solved Problem 5
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4.4.10 Solved Problem 6
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4.4.11 Solved Problem 7
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4.4.12 Solved Problem 8
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4.4.13 Solved Problem 9
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4.4.14 Solved Problem 10
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4.4.15 Solved Problem 11
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4.4.16 Solved Problem 12
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4.4.17 Exercises
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4.5 Bases
4.5.1 Introduction
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4.5.2 Theorem 1
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4.5.3 Theorem 2
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4.5.4 Theorem 3
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4.5.5 Theorem 4
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4.5.6 Solved problem 1
Video
6:2
4.5.7 Solved problem 2
Video
5:26
4.5.8 Solved Problem 3
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4.5.9 Solved Problem 4
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4.5.10 Solved Problem 5
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4.5.11 Solved Problem 6
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4.5.12 Exercises
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4.6 Dimensions
4.6.1 Introduction
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4.6.2 Theorem 1
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4.6.3 Theorem 2
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4.6.4 Theorem 3
PDF
4.6.5 Solved problem 1
Video
6:25
4.6.6 Solved problem 2
Video
6:1
4.6.7 Solved problem 3
Video
5:52
4.6.8 Solved Problem 4
PDF
4.6.9 Solved Problem 5
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4.6.10 Solved Problem 6
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4.6.11 Solved Problem 7
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4.6.12 Solved Problem 8
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4.6.13 Solved Problem 9
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4.6.14 Solved Problem 10
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4.6.15 Solved Problem 11
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4.6.16 Exercises
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Unit - V : Linear Transformation and Inner Product Spaces (Upcoming)
82 Learning Materials
5.1 Linear Transformations
5.1.1 Introduction
PDF
5.1.2 Solved Problem 1
Video
8:2
5.1.3 Solved Problem 2
Video
6:2
5.1.4 Solved Problem 3
PDF
5.1.5 Solved Problem 4
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5.1.6 Solved Problem 5
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5.1.7 Solved Problem 6
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5.1.8 Solved Problem 7
PDF
5.1.9 Exercises
PDF
5.2 Algebra of Transformation
5.2.1 Introduction
PDF
5.2.2 Theorem 1
PDF
5.2.3 Solved Problem 1
Video
7:13
5.2.4 Solved Problem 2
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5.2.5 Solved Problem 3
Video
7:31
5.2.6 Solved Problem 4
PDF
5.2.7 Solved Problem 5
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5.2.8 Solved Problem 6
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5.2.9 Solved Problem 7
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5.2.10 Exercises
PDF
5.3 Rank and Nullity of a Linear Operator
5.3.1 Introduction
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5.3.2 Theorem
PDF
5.3.3 Solved Problem 1
Video
6:4
5.3.4 Solved Problem 2
PDF
5.3.5 Solved Problem 3
Video
7:44
5.3.6 Exercises
PDF
5.4 Rank and Nullity Theorem (Dimension Theorem)
5.4.1 Theorem 1
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5.4.2 Theorem 2
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5.4.3 Theorem 3
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5.4.4 Solved Problem 1
Video
7:27
5.4.5 Solved Problem 2
Video
4:31
5.4.6 Solved problem 3
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5.4.7 Solved problem 4
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5.5 Inner Product Space
5.5.1 Introduction
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5.5.2 Solved problem 1
Video
000
5.5.3 Solved problem 2
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5.5.4 Solved problem 3
Video
000
5.5.5 Solved Problem 4
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5.5.6 Solved Problem 5
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5.5.7 Exercises
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5.6 Norm
5.6.1 Introduction
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5.6.2 Property 1
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5.6.3 Property 2
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5.6.4 Property 3
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5.6.5 Property 4
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5.6.6 Property 5
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5.6.7 Property 6
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5.6.8 Solved Problem 1
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5.6.9 Solved Problem 2
Video
000
5.6.10 Solved Problem 3
Video
000
5.6.11 Solved Problem 4
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5.6.12 Solved Problem 5
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5.6.13 Solved Problem 6
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5.6.14 Solved Problem 7
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5.6.15 Exercises
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5.7 Orthogonality
5.7.1 Introduction
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5.7.2 Orthonormal
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5.7.3 Theorem 1
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5.7.4 Solved Problem 1
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5.7.5 Solved Problem 2
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5.7.6 Solved Problem 3
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5.7.7 Solved Problem 4
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5.8 Gram Schmidt Orthogonalization
5.8.1 Theorem Gram Schmidt Orthogonalization
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5.8.2 Orthogonal Complement
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5.8.3 Projection Theorem
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5.8.4 Solved Problem 1
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5.8.5 Solved Problem 2
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5.8.6 Solved Problem 3
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5.8.7 Solved Problem 4
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5.8.8 Solved Problem 5
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5.8.9 Exercises
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5.9 Adjoint of Linear Operator
5.9.1 Adjoint of Linear Operator
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5.9.2 Theorems
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5.9.3 Solved Problem 1
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5.9.4 Solved Problem 2
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5.10 Least Squares Approximation
5.10.1 Least Squares Approximation
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5.10.2 Theorems
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5.10.3 Solved Problem 1
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5.10.4 Solved Problem 2
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5.10.5 Solved Problem 3
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5.10.6 Solved Problem 4
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5.10.7 Solved Problem 5
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5.10.8 Exercises
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Important Q&A (MA3355)
75 Learning Materials
1. Probability and Random Variables
1.1 Part A: Q&A
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1.2 Part B: Q&A - 1
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1.3 Part B: Q&A - 2
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1.4 Part B: Q&A - 3
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1.5 Part B: Q&A - 4
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1.6 Part B: Q&A - 5
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1.7 Part B: Q&A - 6
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1.8 Part B: Q&A - 7
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1.9 Part B: Q&A - 8
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1.10 Part B: Q&A - 9
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1.11 Part B: Q&A - 10
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1.12 Part B: Q&A - 11
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1.13 Part B: Q&A - 12
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1.14 Part B: Q&A - 13
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1.15 Part B: Q&A - 14
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1.16 Part B: Q&A - 15
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1.17 Part B: Q&A - 16
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1.18 Part B: Q&A - 17
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1.19 Part B: Q&A - 18
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2. Two - Dimensional Random Variables
2.1 Part A: Q&A
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2.2 Part B: Q&A - 1
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2.3 Part B: Q&A - 2
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2.4 Part B: Q&A - 3
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2.5 Part B: Q&A - 4
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2.6 Part B: Q&A - 5
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2.7 Part B: Q&A - 6
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2.8 Part B: Q&A - 7
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2.9 Part B: Q&A - 8
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2.10 Part B: Q&A - 9
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2.11 Part B: Q&A - 10
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2.12 Part B: Q&A - 11
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2.13 Part B: Q&A - 12
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2.14 Part B: Q&A - 13
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2.15 Part B: Q&A - 14
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2.16 Part B: Q&A - 15
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3. Random Process
3.1 Part A: Q&A
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3.2 Part B: Q&A - 1
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3.3 Part B: Q&A - 2
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3.4 Part B: Q&A - 3
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3.5 Part B: Q&A - 4
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3.6 Part B: Q&A - 5
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3.7 Part B: Q&A - 6
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3.8 Part B: Q&A - 7
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3.9 Part B: Q&A - 8
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3.10 Part B: Q&A - 9
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3.11 Part B: Q&A - 10
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3.12 Part B: Q&A - 11
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3.13 Part B: Q&A - 12
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3.14 Part B: Q&A - 13
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3.15 Part B: Q&A - 14
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3.16 Part B: Q&A - 15
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3.17 Part B: Q&A - 16
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4. Vector spaces
4.1 Part A: Q&A
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4.2 Part B: Q&A - 1
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4.3 Part B: Q&A - 2
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4.4 Part B: Q&A - 3
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4.5 Part B: Q&A - 4
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4.6 Part B: Q&A - 5
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4.7 Part B: Q&A - 6
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4.8 Part B: Q&A - 7
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4.9 Part B: Q&A - 8
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4.10 Part B: Q&A - 9
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4.11 Part B: Q&A - 10
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4.12 Part B: Q&A - 11
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5. Linear Transformation and Inner Product Spaces
5.1 Part A: (Q&A)
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5.2 Part B: Q&A - 1
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5.3 Part B: Q&A - 2
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5.4 Part B: Q&A - 3
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5.5 Part B: Q&A - 4
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5.6 Part B: Q&A - 5
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5.7 Part B: Q&A - 6
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5.8 Part B: Q&A - 7
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5.9 Part B: Q&A - 8
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5.10 Part B: Q&A - 9
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5.11 Part B: Q&A - 10
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Course Instructor
Promath
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