Learning Outcomes:

Understand all basic fundamentals of Statistics and its application on collected information

Prepare him/her self for making a proper interpretation of system based on parameters of distribution.

Apply knowledge of statistics and Probability to form a mathematical model to ensure conclusive hypothesis for problem.

Syllabus:

Unit No | Topics |
---|---|

1 | Frequency Distribution: Collection of data, Classification of data, Class interval, Types of Classes, Class frequency, Class mark, Class Boundaries, Width of a class, Frequency density, Relative frequency, Percentage frequency, Cumulative frequency. |

2 | Measures Of Central Tendency: Introduction, Arithmetic Mean, Simple and weighted for raw data, Discrete frequency distribution, Continuous frequency distribution, Properties of A.M., Merits & De merits of A.M., Median for raw data, Discrete frequency distribution, Continuous frequency distribution, Merits and demerits of Median, Mode for raw data, Merits & demerits of mode. |

3 | Measures Of Dispersion: Introduction, Range, coefficient of range, Quartiles, Quartiles deviations, coefficient of quartile deviations, Mean deviation and coefficient of mean deviation, S.D and variance for all types of frequency distribution, Coefficient of Dispersion, Coefficient of variation. |

4 | Correlation: Definition of Correlation, Types of Correlation, Scatter Diagram Method, Karl Person’s Correlation Coefficients, Correlation Coefficients for Bivariate frequency distribution, Probable error for Correlation Coefficients, Rank Correlation Co-efficient. |

5 | Regression: Definition of Regression, Regression lines, Regression Coefficients, Properties of regression Coefficients, and Fitting of regression lines and estimation for Bivariate frequency distribution, Multiple Linear Regression. |

6 | Probability Theory: Introduction, Random Experiment, Sample Space, Events, Complementary Events, Union and Intersection of Two Events, Difference Events, Exhaustive Events, Mutually Exclusive Events, Equally Likely Events, Independent Events, Mathematical & Statistical definition of Probability, Axiomatic definition of probability, Addition Theorem, Multiplication Theorem, Theorems of Probability, Conditional Probability, Inverse Probability. |

7 | Probability Distributions: Binomial Distribution: Introduction, Probability mass function of Binomial distribution, Mean and Variance of Binomial distribution, Properties of Binomial Distribution, Uses of Binomial Distribution. Poisson Distribution: Introduction, Probability mass function of Poisson distribution, Mean and Variance of Poisson distribution, Properties of Poisson Distribution, Applications of Poisson Distribution. Normal Distribution:Introduction, Probability density function of Normal distribution, Properties of Normal distribution, Importance of Normal Distribution. |

Text Books:

Name :

Probability, Statistics and Random Process

Author:

T Veerarajan

TMH

Edition:

3rd

Reference Books:

Name:

Fundamental of Applied Statistic

Publication:

Sultan Chand Publication

Name:

Statistical Methods

Publication:

Sultan Chand Publication

Name:

Business Statistics

Publication:

B.S. Shah Prakashan

Syllabus PDF:

Attachment | Size |
---|---|

Probability & Statistics.pdf | 164.93 KB |

branch:

CBA

BDA

MA

Course:

2016

Stream:

B.Tech