# Applied Probability and Statistics

**Topics:**Normal distribution, Probability theory, Probability density function

**Pages:**22 (4331 words)

**Published:**June 11, 2013

APPLIED PROBABILITY AND STATISTICS

DEPARTMENT OF COMPUTER SCIENCE

DEPARTMENT OF COMPUTER SCIENCE

STATISTICAL DISTRIBUTION

STATISTICAL DISTRIBUTION

SUBMITTED BY –

PREETISH MISHRA (11BCE0386)

NUPUR KHANNA (11BCE0254)

SUBMITTED BY –

PREETISH MISHRA (11BCE0386)

NUPUR KHANNA (11BCE0254)

SUBMITTED TO –

PROFESSOR

SUJATHA V.

SUBMITTED TO –

PROFESSOR

SUJATHA V.

ACKNOWLEDGEMENT –

ACKNOWLEDGEMENT –

First and foremost we like to thank our supervisor of the project Mrs Sujatha V. for her valuable guidance an advice. She inspired us greatly to work in this project. Her willingness to motivate us contributed majorly in this project. We also would like to thank her for showing us some example that related to our project.

Besides, we would also like to thank VIT University for providing us with a good environment and facilities to complete this project. Also, we would like to thank school of computer science (SCSE) of VIT University, for offering this subject and computing project. It has given us the opportunity to participate and learn about various methods of calculating statistical distribution.

Finally, an honourable mention goes to my team for completing this project. Without helps of particular mentioned above, we would have faced many difficulties while doing this project.

CONTENTS –

CONTENTS –

PAGE NUMBER

1. INTRODUCTION 4 – 11 2. SOURCE PROGRAM 12 – 21 3. SAMPLE INPUT/OUTPUT 22 – 23 4. TOOLS REQUIRED 24 5. RESULT 24 6. APPLICATIONS OF STATISTICAL DISTRIBUTION 25 7. CONCLUSIONS 25 8. REFERENCES 26

INTRODUCTION

INTRODUCTION

In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence.

Familiar examples of dependent phenomena include the correlation between the physical statures of parents and their offspring, and the correlation between the demand for a product and its price. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling; however, statistical dependence is not sufficient to demonstrate the presence of such a causal relationship (i.e., Correlation does not imply causation).

In this project though we show computation of several distribution techniques. The program allows computing several distributions that are 1. Binomial distribution

2. Poisson distribution

3. Normal distribution

4. Normal distribution (2 variables)

5. Chi-square distribution

6. Student T distribution

BINOMIAL DISTRIBUTION –

The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success...

References: –

* B. S. Everitt: The Cambridge Dictionary of Statistics, Cambridge University Press, Cambridge (3rd edition, 2006). ISBN 0-521-69027-7

* Bishop: Pattern Recognition and Machine Learning, Springer, ISBN 0-387-31073-8

* ^ Hamilton Institute. "The Binomial Distribution" October 20, 2010.

* Joachim H. Ahrens, Ulrich Dieter (1974). "Computer Methods for Sampling from Gamma, Beta, Poisson and Binomial Distributions". Computing 12 (3): 223–246. doi:10.1007/BF02293108

* Aldrich, John; Miller, Jeff. "Earliest Uses of Symbols in Probability and Statistics"

* M.A. Sanders. "Characteristic function of the central chi-squared distribution". Retrieved 2009-03-06.

* Hogg, R.V.; Craig, A.T. (1978). Introduction to Mathematical Statistics. New York: Macmillan.

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