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Probability Expected Value

Since its humble beginnings at the gambling tables in seventeenth century, probability theory has been developed and employed to treat and solve many weighty problems. It is the foundation of the classical decision procedures of estimation and testing. Probability models can be very useful for making predictions. The word ‘probability’ or ‘chance’ is very commonly used in day-to-day conversation and generally people have a vague idea about its meaning for example, we come across statements like ‘’probably it may rain tomorrow’’, ‘’it is likely that Mr. x may not come for taking his class today ‘’ the chances of teams a mad b winning a certain match are equal’’ ‘’probably you are right’’ it is possible that I may not be able to join you at the tea party’’ all these terms-possible, probably , likely, etc.. convey the same sense i.e. the event is not certain it take place or, in other words these is uncertainty about hap dining of the event in question. In layman’s terminology the word ‘’probability’’ thus connotes that there is uncertainty about the happening of the event. However, in mathematics and statistics we try to present conditions under which we can make sensible numerical statements about uncertainty and apply certain methods of calculating numerical values of probabilities mad expectations. In statistical sense the term probability is thus established by definition and is not connected with beliefs or any form of wishful thinking.

Some of its important topics are:

1. Bayes' theorem

2. Counting rules

3. Elementary set theory

4. Mathematical expectation

5. Probability and expected value

6. Probability defined

7. Calculation of probability

8. Probability events

9. Conditional probability

10. Random variable

11. Probability sampling methods

Some of its important topics are:

1. Bayes' theorem

2. Counting rules

3. Elementary set theory

4. Mathematical expectation

5. Probability and expected value

6. Probability defined

7. Calculation of probability

8. Probability events

9. Conditional probability

10. Random variable

11. Probability sampling methods

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Assignment Help

Business Forecasting
Central Value Measures
Correlation Regression Analysis
Data Tabulation
Diagrammatic Graph Presentation
Dispersion Measures
Experimental Designs
F Test Variance Analysis
Index Numbers
Partial Multiple Correlation
Probability Expected Value
Sampling Designs
Skewness Moments Kurtosis
Statistical Survey
Decision Theory
Statistical Fallacies
Statistical Quality Control
Statistics What Why
Theoretical Distribution
Vital Statistics
F2 Test

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