#### Topics

##### Mathematical Logic

##### Mathematical Logic

##### Matrices

##### Differentiation

##### Applications of Derivatives

##### Integration

##### Definite Integration

##### Applications of Definite Integration

##### Differential Equation and Applications

##### Matrices

##### Commission, Brokerage and Discount

##### Insurance and Annuity

##### Linear Regression

##### Time Series

##### Index Numbers

- Index Numbers
- Types of Index Numbers
- Index Numbers - Terminology and Notation
- Construction of Index Numbers
- Simple Aggregate Method
- Weighted Aggregate Method
- Cost of Living Index Number
- Method of Constructing Cost of Living Index Numbers - Aggregative Expenditure Method
- Method of Constructing Cost of Living Index Numbers - Family Budget Method
- Uses of Cost of Living Index Number

##### Linear Programming

##### Assignment Problem and Sequencing

##### Probability Distributions

- Mean of a Random Variable
- Types of Random Variables
- Random Variables and Its Probability Distributions
- Probability Distribution of Discrete Random Variables
- Probability Distribution of a Continuous Random Variable
- Binomial Distribution
- Bernoulli Trial
- Mean of Binomial Distribution (P.M.F.)
- Variance of Binomial Distribution (P.M.F.)
- Poisson Distribution

##### Continuity

##### Differentiation

##### Applications of Derivative

##### Indefinite Integration

##### Definite Integrals

##### Ratio, Proportion and Partnership

##### Commission, Brokerage and Discount

##### Insurance and Annuity

##### Demography

##### Bivariate Data and Correlation

##### Regression Analysis Introduction

##### Random Variable and Probability Distribution

##### Management Mathematics

#### definition

Let X be a random variable whose possible values `x_1, x_2, x_3, ..., x_n` occur with probabilities `p_1, p_2, p_3,..., p_n`, respectively. The mean of X, denoted by µ, is the number `sum_(i=1)^n x_i p_i` i.e. the mean of X is the weighted average of the possible values of X, each value being weighted by its probability with which it occurs.

The mean of a random variable X is also called the expectation of X, denoted by E(X).

Thus , E(X) = µ = `sum_(i = 1)^n x_ip_i = x_1p_1 + x_2p_2 + ... + x_np_n.`

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