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1 |
Introduction, Set Concept, Sample Space, Permutation, Combination.
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2 |
Introduction to Probability, Probability Axioms, Geometric Probability, Conditional Probability, Bayes'' Theorem.
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3 |
Random Variables and Distributions: Discrete, Continuous, Two-Dimensional Random Variables.
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4 |
Expected Value, Variance, Standard Deviation and Their Properties.
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Moments, Chebyshev Inequality.
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6 |
Some Discrete Distributions: Bernoulli, Binomial, Multinomial, Geometric Distributions.
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Some Discrete Distributions: Negative Binomial, Hypergeometric, Poisson Distributions.
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8 |
Distribution of Continuous Random Variables: Normal Distribution, Standard Normal Distribution.
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Distribution of Continuous Random Variables: Uniform, Exponential, Gamma, Beta distributions.
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Sampling, Sample selection, data organization and analysis, frequency distribution, measures of central tendency, measures of dispersion, graphical representations and coefficient of variation.
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11 |
Sampling distributions and estimation: Point estimation, Interval estimation for population mean with known variance.
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12 |
Sample size in interval estimation for population mean with known variance, Chebysev and sample size.
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13 |
Interval estimation for population mean when variance is unknown, Interval estimation for population standard deviation and variance.
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14 |
Interval estimation for the ratio of the difference between the means and variances of two normally distributed populations, Interval estimation for pairs of measurements.
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16 |
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18 |
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20 |
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