- •Introduction
- •Basic concepts of probability theory
- •Classical definition of probability
- •Relative frequency
- •Geometric probabilities
- •Glossary
- •Exercises for Seminar 1
- •Exercises for Homework 1
- •Basic formulas of combinatorial analysis
- •Operations over events
- •Glossary
- •Exercises for Seminar 2
- •Exercises for Homework 2
- •Theorem of addition of probabilities of incompatible events
- •Complete group of events
- •Opposite events
- •Conditional probability
- •Theorem of multiplication of probabilities
- •Glossary
- •Exercises for Seminar 3
- •Exercises for Homework 3
- •Independent events
- •Where a is the appearance of at least one of the events a1, a2, …, An; .
- •Glossary
- •Exercises for Seminar 4
- •Exercises for Homework 4
- •Theorem of addition of probabilities of compatible events
- •Formula of total probability
- •Probability of hypotheses. Bayes’s formulas.
- •Glossary
- •Exercises for Seminar 5
- •Exercises for Homework 5
- •Repetition (recurrence) of trials. The Bernoulli formula
- •Local theorem of Laplace
- •Integral theorem of Laplace
- •Glossary
- •Exercises for Seminar 6
- •Exercises for Homework 6
- •Random variables. The law of distribution of a discrete random variable
- •A random variable is understood as a variable which as result of a trial takes one of the possible set of its values (which namely – it is not beforehand known).
- •Mathematical operations over random variables
- •(Mathematical) expectation of a discrete random variable
- •Dispersion of a discrete random variable
- •Glossary
- •Exercises for Seminar 7
- •Exercises for Homework 7
- •Distribution function of a random variable
- •Properties of a distribution function
- •Continuous random variables. Probability density
- •Properties of probability density
- •Glossary
- •Exercises for Seminar 8
- •Exercises for Homework 8
- •Basic laws of distribution of discrete random variables
- •1. Binomial law of distribution
- •2. The law of distribution of Poisson
- •3. Geometric distribution
- •4. Hypergeometric distribution
- •Glossary
- •Exercises for Seminar 9
- •Exercises for Homework 9
- •Basic laws of distribution of continuous random variables
- •1. The uniform law of distribution
- •2. Exponential law of distribution
- •3. Normal law of distribution
- •Glossary
- •Exercises for Seminar 10
- •Exercises for Homework 10
- •The law of large numbers and limit theorems
- •The central limit theorem
- •Glossary
- •Exercises for Seminar 11
- •Exercises for Homework 11
- •Mathematical statistics. Variation series and their characteristics
- •Numerical characteristics of variation series
- •Glossary
- •Exercises for Seminar 12
- •Exercises for Homework 12
- •Bases of the mathematical theory of sampling
- •Glossary
- •Exercises for Seminar 13
- •Exercises for Homework 13
- •Methods of finding of estimations
- •Notion of interval estimation
- •Glossary
- •Exercises for Seminar 14
- •Exercises for Homework 14
- •Testing of statistical hypotheses
- •Glossary
- •Exercises for Seminar 15
- •Exercises for Homework 15
- •Individual homeworks
- •Variant 1
- •Variant 2
- •Variant 3
- •Variant 4
- •Variant 5
- •Variant 6
- •Variant 7
- •Variant 8
- •Variant 9
- •Variant 10
- •Variant 11
- •Variant 12
- •Variant 13
- •Variant 14
- •Variant 15
- •Variant 16
- •Variant 17
- •Variant 18
- •Variant 19
- •Variant 20
- •Variant 21
- •Variant 22
- •Variant 23
- •Variant 24
- •Variant 25
- •Final exam trial tests (for self-checking)
- •Appendix
- •Values the functions and
- •List of the used books
- •Contents
Variant 3
1. A die is tossed once. Find the probabilities of the following events:
a) appearance of an odd number of aces;
b) appearance of no less than 4 aces;
c) appearance of no more than 2 aces.
2. Products of a certain kind are delivered in a shop from three factories. The first factory delivers 45% of all products, the second – 40%, and the third – 15%. The probability that a product of the first factory is non-defective is equal to 0,6, of the second – 0,7, and of the third – 0,85. One product has been bought. It appeared non-defective. Determine the probability that the bought product has been made by the third factory.
3. A coming up seeds of a given grade of cucumber is 0,8. Find the probability that the number of grown seeds will be between 775 and 810 from 1000 planted seeds.
4. Two independent random variables X and Y are given by the following tables of distribution:
X |
1 |
2 |
4 |
|
Y |
2 |
3 |
P |
0,4 |
0,3 |
0,3 |
|
P |
0,7 |
0,3 |
Compose the table of distribution of the random variable X – Y and check the property: D(X – Y) = D(X) + D(Y).
5.
A random variable X
is given by the density of distribution
in the interval (0; 4), and f(x)
= 0 outside of the interval. Find: a) the dispersion of the random
variable X;
b) the probability of hit of the random variable X
into the interval (2; 5).
6. A fat content of milk of cows in collective farms of a region (in percentage) is a normally distributed random variable with mathematical expectation 4 and mean square deviation 1. Find the probability that the random variable differs from its mathematical expectation no more than 0,3 (collective farm – колхоз).
7. The dispersion of each of pairwise independent random variables does not exceed 15. Estimate the probability that the deviation of the arithmetic mean of 2000 these variables from the arithmetic mean of their mathematical expectations will not exceed 0,5.
8. There are the following data on the size of annual revenues of 60 joint-stock companies:
812 |
521 |
1222 |
1140 |
1282 |
1244 |
698 |
1009 |
565 |
1396 |
723 |
701 |
755 |
592 |
1195 |
983 |
772 |
1125 |
638 |
1226 |
618 |
859 |
205 |
677 |
1086 |
1349 |
845 |
1250 |
732 |
1122 |
749 |
829 |
912 |
789 |
578 |
1310 |
926 |
1368 |
948 |
1085 |
632 |
918 |
545 |
872 |
909 |
1219 |
925 |
1294 |
608 |
720 |
502 |
1012 |
615 |
1228 |
1128 |
769 |
552 |
836 |
542 |
828 |
1) Compose the interval and the discrete variation series taking the beginning of the first interval equal 500, and the width of each interval equal 50.
2) Construct the histogram and the polygon of relative frequencies of distribution.
3) Find the mode and the median (using the discrete series).
4) Find empirical functions of distribution of continuous and discrete variation series; and construct their graphs.
