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PDF | On Dec 1, , S. M. Shahidul Islam and others published A text book on Business Mathematics. tive mathematical methods in a business oriented environment. she/he is recommended to take a look at the brilliant books by Penrose. Business MBA students who studied business mathematics and statistics using As is often the case with equations and numbers, I am sure this book still has.

Matrix is thus, defined as a rectangular array of ordered numbers in rows and columns. The numbers are also known as elements of matrix. A matrix consisting of m rows and n columns is written in the form as: The first subscript 'm' refers to the 'number of rows' and the second subscript 'n' refers to the 'number of columns' in the particular matrix. Thus a12 is the element of 1 st row and 2 nd column in the matrix.

A is a 2x3 matrix, X is a 3x5 matrix, and B is a 2x1 matrix. This elementary operation is called row operation if it applies to rows, and column operation if it applies to columns. The elementary operation is of any one of the three types 1 Interchange of any two rows or columns , 2 Multiplication or division of the elements of any row or column by any non- zero number, 3 Addition of the elements of any row or column to the corresponding elements of any other row or column or multiplied by any number.

To solve homogeneous linear equations, the Guass-Jordon method also called Triangular Term Reduction method is applied.

In this, the given linear equation is reduced to an equivalent simpler system, which is studied in both homogeneous and non homogeneous equations. It is thus, calculated by first finding A -1 and then post-multiplying A -1 with B.

Illustration 25 Solve the equations and find the values by linear equations using the matrix inverse method. Solution The equations for three days cost can be written like this: Since Det A is not zero, inverse of matrix A exists.

In many fields, scientists plan for construction of their objects in terms of sets. In trade, industry and commerce analysis, we have sets of data, sets of items produced, sets of outcomes of decisions and alike. While expressing the words such as family, association, group, crowd, we often used to convey the idea of a set in our day life.

Enumerations and calculations leading to numbers form set and which gives a good insight into the depth of nature. In set, symbols are used, on which its operations can be done. Please use headphones Notation A collection of any type of numbers, things, or objects is referred to "set".

The constituent numbers of it is termed as its "elements". These elements may be presented by enclosing them in brackets or may be described in a form with statement of the properties.

The sets is denoted by capital letters A, B, C,and and their elements in lower case letters a, b, c, A set is known by its elements. A set may be presented either in tabular method or Roster method , descriptive phrase method or rule method or set builder. These are discussed with examples. It means A is a set of all even numbers between 1 and 9; and 2, 4, 6, 8 are elements of A.

The function of this set is described in tabular form enlisting each element of it within the bracket. This method is known as tabular method. In this, the elements of A simply place a phrase describing the elements of the set within the bracket.

This method is called descriptive phrase method. In this, the set is determined by its elements but not by the description. This method is known as rule method. However, it should be noted that the set A we get in any of the three different ways as shown above, remains the same.

Venn Diagram A diagram is said to be Venn diagram if the elements of set are represented by points in circular or similarly enclosed curve. The Venn diagram shows the letters a, e, i, o, u which are the elements of set V. A set remains unchanged even if the order of its elements is changed. Thus, the above set can be written as: Express the following in the set notation: Integers less than 5 ii. The letters in the word 'English' iii. Integers greater than and less than Even numbers up to v.

The three smallest integers greater than 20, and three smallest integers less than Solution i. Set A consists of the elements x such that, x is a college having more than students.

Set B consists of the elements b such that b is all the numbers greater than 4. Set C consists of the elements c such that c is all integers less than 0 i. C is the set of all negative integers. The number of elements is denoted by n A and can be counted by a finite number.

A set is infinite, if it has an infinite number of elements. The elements of such set cannot be counted by a finite number. A set of points along a line or in a plane is known as point set. Illustration 30 State the following in terms of finite or infinite sets i.

D is a finite set iii. B is a finite set v. No vi. Yes Types of Sets a. A set which contains no elements is called a null set or empty set, and is denoted by Phi b. A set which contains only one element is known as unit set.

A set which contains the totality of elements is called universal set. It is often drawn as a rectangle and is denote by E d. If the two sets have no elements in common, they are disjoint sets. If the two sets overlap, that overlapping portion will include the common points between the two sets is known as overlapping set.

If an element x belongs to a set A, then it is said to be the membership set. If some elements in a set have a common special property, they can be said to belong to a subset. If each and every element of one set is also an element of another set, the two sets are said to be equal or identical sets. Two sets are said to be equivalent if there is a one-to-one correspondence between the elements in two sets.

The elements appearance order in each set is immaterial.

The class of all subsets of a set A is called the power set of A. It is denoted by P A. Illustration 31 State the following sets: A is not a null set v. If x belongs to a set A, and if x does not belong to a set A, then we write x A, and x A respectively xii.

The basic operations on sets are classified into four. They are a Complementation, b Difference, c Intersection and d Union.

These are discussed below with suitable examples. Complementation If A is a subset of the universal set E, then the set of elements of E that do not belong to A, is known as the complement of A. The unshaded portion of of E in the above Venn diagram is referred to the complement of A. Therefore complement of a set A in a universal set E, is the set of all elements in E which are not elements of A. The complement of A may be written as A 1.

Intersection The intersection of two sets A and B is the set of all elements which belong to both A and B. Define matrix. Explain the operations on matrices. What is determinant of a square matrix? Discuss its properties. What is inverse of a matrix? Explain it with example. Explain how the matrix algebra is useful in solving a set of linear simultaneous equations.

Find the rate of commission on items A, B, C from the data given below: April 90 20 May 50 50 30 June 40 70 20 6. What is a Venn diagram? Explain complement, union, difference and intersection with Venn diagrams. Explain the following: Addition of matrices ii. Transpose of matrix iii. Minor and Cofactor iv. Cramer's rule v.

Vector matrix vi. Rank of matrix vii.

Zero matrix viii. Symmetric matrix ix. Monga, G. Raghavachari, M. Scarles, S. Jerome Cardon, an Italian mathematician was pioneer to write a book on 'Games of Chance' which was published posthumously in Credit goes to the French mathematicians Blaise Pascal and Pierre de Fermat who developed a systematic and scientific procedure for probability theory on solving the stake in an incomplete gambling match posed by a notable French gambler and noble man Chevalier de Mere.

A Swiss mathematician James Bernoulli made an extensive study on prob- ability is a major contribution to the theory of probability and combinatory.

Contribution to the theory of probability was made by Abraham de Moivre - The Doctrines of chances and the Revered Thomas Bayes - Inverse Probability. In 19th century, Pierre Simon de Laplace made an extensive research on all the early ideas and published his monumental work under caption "Theory of Analytical Probability" in which became pioneer in theory of probability. Meaning of Probability The word probability is very commonly used in day-to-day conversation, and people have a rough idea about its meaning.

We often come across statements like probably it may rain today. It means not sure about the occurrence of rain but there is possibility of occurrence of rain. What do we mean when we say the probability to win the match is 0. Do we mean that we will win three-fourths of the match. We actually mean that the conditions show the likelihood of winning the match is only 75 percent. Hence, the term probability is sensible numerical expression about uncertainty.

In brief, probability is numerical value about uncertainty with calculated risk. If an event is certain not to occur, its probability is zero and if it is certain to occur, its probability is one.

The subject probability has been developed to a great extent and today, no discipline in social, physical or natural sciences is left without the use of probability. It is widely and popularly used in the quantitative analysis of business and economic problems; and is an essential tool in statistical inferences which form the basis for the decision theory.

In other words, the role played by probability in modern science is of a substitute for certainty. Thus, the probability theory is a part of our everyday life. Please use headphones Terminology The various terms which are used in defining probability under different approaches are discussed below.

Random Experiment: If it is conducted repeatedly under homogeneous conditions and the result is not unique but may be any one of the various possible outcomes, it is called random experiment.

In other words, an experiment is said to be random if we cannot predict the outcome before the experiment is carried. For example, coin toss is a random experiment.

Trial and Event: For example, the result is not unique by tossing a coin repeatedly. We may get either head or tail. Thus, tossing of a coin is a trial and getting head or tail is an event. Independent and Dependent Events: For example, in tossing a die repeatedly, the event of getting number 3 in 1st throw is independent of getting number 3 in the 2nd, 3rd or subsequent throws.

Event is said to be dependent, if the occurrence of an event affects the occurrence of the other. Mutually Exclusive Events: For example, if a die is thrown in a trial, by getting an outcome of say number 6, the occurrence of remaining numbers is excluded in that trial.

Hence, all the outcomes are mutually exclusive. Equally Likely Events: Exhaustive Events: For example, if a coin is tossed, we can get head H or tail T. Hence exhaustive cases are 2. Thus in a toss of two coins, exhaustive cases are 4, i. In a throw of n coins, exhaustive events are 2 n.

Simple and Compound Events: For example, the probability of drawing a red ball from a bag containing 8 white and 7 red balls. The joint occurrence of two or more events are termed compound events. For example if a bag containing 9 white and 6 red balls, and if two successive draws of 3 balls are made, we are going to find out the probability of getting 3 white balls in the first draw and 3 red balls in the second draw. Complementary Events: A is called complementary event of B and vice-versa if A and B are mutually exclusive and exhaustive events.

For example, when a die is thrown, occurrence of an even number 2, 4, 6 and odd number 1, 3, 5 are complementary events. Probability Tree: Sample Space: In other words, the sample space is the set of all exhaustive cases of the random experiment. The elements of the sample space are the outcomes. Algebra of Sets: In the figure given below, we represent the union, difference, and intersection of two events by means of Venn diagrams.

The region enclosed by a rectangle is taken to represent the sample space whereas given events are represented by ovals within the rectangle. Permutation and Combination The word permutation means arrangement and the word combination means group or selection. For example, let us take three letters A, B and C. The order of elements is immaterial in combinations, while in permutations the order of elements matters. Permutation A permutation of n different objects taken r at a time is an ordered arrangement of only r objects out of the n objects.

In other words, the number of ways of arranging n things taken r at a time. It is denoted symbolically as n Pr, where n is total number of elements in the set, r is the number of elements taken at a time, and P is the symbol for permutation.

Thus, n! For example, 1. Find the number of permutations of the letters a, b, c, d, e taken 2 at a time. In how many ways can 8 differently coloured marbles be arranged in a row? The number of combinations of n objects taken r at a time is denoted by n Cr given by n! In how many ways can a committee of 3 persons be chosen out of 8? How many different sets of 4 students can be chosen out of 20 students? In how many ways can a committee of 4 men and 3 women be selected out of 9 men and 6 women.

Probability Theorems The computation of probabilities can become easy and be facilitated to a great extent by the two fundamental theorems of probability - the Addition Theorem and the Multiplication Theorem. The probability of occurring either event A or event B where A and B are mutually exclusive events is the sum of the individual probability of A and B.

If the total number of possibilities is n, then by definition the probability of either A or B event happening is: For example, if the probability of buying a pen is 0.

When the events are not mutually exclusive, the above said theorem is to be modified. The probability of occurring of at least one of the two events, A and B which are not mutually exclusive is given by: In case of three events: One ball is drawn at random. What is the probability that it is marked with a number that is multiple of 5 or 7?

Solution The sample space i. The possible sample points that are multiples of 5 are The possible sample points that are multiples of 7 are What is the probability that it is marked with a number that is a multiple of 3 or 4? Since there are some numbers which are multiples of both 3 and 4, we need to exclude them so that they don't get counted twice. Find the probability that the number of drawn ball will be a multiple of a 5 or 9, and b 5 or 7 Solution a Probability of getting a ball that has a multiple of 5, i.

What is the probability that he passes the English test? Then, i. For what choice of P are A and B mutually exclusive? For what choice of P are A and B independent? Find the probability of the target being hit at all when they both try. A product is assembled from three components - X, Y, and Z. The probability of these components being defective is 0. What is the probability that the assembled product will not be defective? Items produced by a certain process may have one or both of the two types of defects A and B.

It is known that 20 per cent of the items have type A defects and 10 per cent have type B defects. Furthermore, 6 per cent are known to have both types of defects.

What is the probability that a randomly selected item will be defective? Solution a. Let P A be the probability that an item will have A-type of defect, and P B be the probability that an item will have B-type of defect. The behaviour of each successive customer is independent. If three customers A, B and C enter together, what is the probability that the salesman will make a sale to at least one of the customers. The probability of occurring of two independent events A and B is equal to the product of their individual probabilities.

Thus, the total number of successful events in both cases is a1 x a2. Similarly, the total number of possible cases is n1 x n2. By definition, the probability of occurrence of both events is: Illustration 10 Four cards are drawn at random from a pack of 52 cards. Find the probability that i. They are a King, a Queen, a Jack and an Ace ii. Two are Kings and two are Aces iii.

All are Diamonds iv. Two are red and two are black v. There is one card of each suit vi. There are two cards of Clubs and two cards of Diamonds Solution A pack contains 52 cards. We can draw 4 cards from 52 cards, in n Cr i. A pack of 52 cards consisting of four cards each of King, Queen, Jack and Ace. Since drawing a King is independent of the ways of getting a Queen, a Jack and an Ace, the sample points or the favorable number of cases are 4 C1 x 4 C1 x 4 C1 x 4 C1. A pack of 51 cards contains 13 Diamond cards.

So we can draw 4 out of 13 Diamond cards in 13 C4 ways. In a pack of 52 cards, there are 13 cards of each suit.

We can draw one card of each suit in 13 C1 x 13 C1 x 13 C1 x 13 C1 ways. In a pack of 52 cards, there are 13 Club cards and 13 Diamond cards. So, we can draw two cards of Clubs and two cards of Diamonds in 13 C2 x 13 C2 ways.

Two balls are drawn in succession at random. What is the probability that one of them is white and the other is red? What is the probability that 2 are red, 2 are white, and 1 is blue? Two successive drawings of 3 balls are made. Find the probability that the first drawing will give 3 white and the second 3 red balls, if i the balls were replaced before the second trial, ii the balls were not replaced before the second trial.

In first draw, 3 balls out of 13 can be drawn in 13 C3 ways ways which is the exhaustive number of cases. Since the balls were not replaced before the second draw, events A and B are dependent events. Find the probability that: If India and Australia play 4 test matches, what is the probability that i India will lose all the four test matches, ii India will win at least one test match.

What is the probability of the University selecting a Hindi- knowing woman teacher? What is the probability of selecting a fair complexioned rich girl? Of total , one is nominated at random to be the University Executive. Find the probability that i the teacher has only a PG degree, ii the teacher has a PhD degree and is from Commerce subject, iii the teacher has a PhD degree and if from Economics subject, iv the teacher has a PhD degree.

The data is: If two events A and B are dependent, the probability of the second event occurring will be affected by the outcome of the first that has already occurred. The term conditional probability is used to describe this situation.

Robert L. Birte defined the concept of conditional probability as: In other words, probability of B, given A. The conditional probability occuring due to a particular event or reason is called its reverse or posteriori probability. The revision of given i. The Bayes' theorem is defined as: Jolliffee in his book captioned 'Commonsense Statistics for Economists and Others' has defined the concept of Bayes theorem as: With twisting conditional probabilities the other way round, i.

Probability before revision by Bayes' rule is called a priori or simply prior probability, since it is determined before the sample information is taken in account. A probability which has undergone revision via Bayes' rule is called posterior probability because, it represents a probability computed after the sample information is taken into account.

Posterior probability is also called revised probability in the sense that it is obtained by revising the prior probability with the sample information. Posterior probability is always conditional probability, the conditional event being the sample information. Thus, a prior probability which is unconditional probability becomes a posterior probability, which is conditional probability by using Baye's rule.

Please use headphones Illustration 20 Two sets of candidates are competing for positions on the Board of Directors of a company. The probability that the first and second sets will win are 0.

If the first set wins, the probability of introducing a new product is 0. What is the probability that the product will be introduced. Past records show that the first machine produces 40 per cent of output and the second machine produces 60 per cent of output. Further, 4 per cent and 2 per cent of products produced by the first machine and the second machine respectively, were defectives.

If a defective item is drawn at random, what is the probability that the defective item was produced by the first machine or the second machine. And we may say that the defective item is more likely drawn from the output produced by the first machine.

Illustration 22 The probability that management trainee will remain with a company is 0. The probability that an employee earns more than Rs. The probability that an employee is a management trainee who remained with the company or who earns more than Rs. What is the probability that an employee earns more than Rs.

Illustration 23 Suppose that one of the three men, a politician, a businessman and an academician will be appointed as the Vice Chancellor of a University. Their probability of appointments respectively are 0. The probabilities that research activities will be promoted by these people if they are appointed are 0. What is the probability that research will be promoted by the new Vice- Chancellor? Appointment of an academician as the Vice-Chancellor would certainly develop and promote education as it is known by the probability theory.

Random Variable and Probability Distribution By random variable we mean a variable value which is computed by the outcome of a random experiment. In brief, a random variable is a function which assigns a unique value to each sample point of the sample space. A random variable is also called a chance variable or stochastic variable. A random variable may be continuous or discrete. If the random variable takes on all values within a certain interval, then it is called a continuous random variable while if the random variable takes on the integer values such as 0, 1, 2, 3, The function p x is known as the probability function of random variable of x and the set of all possible ordered pairs is called probability distribution of random variable.

The concept of probability distribution is in relation to that of frequency distribution. While the frequency distribution tells how the total frequency is distributed among different classes of the variable, the probability distribution tells how the total frequency is distributed among different classes of the variable, the probability distribution tells how the total probability of 1 is distributed among various values which random variable can take.

In brief, the word frequency is replacing by probability. Illustration 24 A dealer of Allwyn refrigerators estimates from his past experience the probabilities of his selling refrigerators in a day. These are as follows: Getting an odd number is termed as a success. Find the probability distribution of number of success. In two throws of a die, X denoted by S becomes a random variable and takes the values 0, 1, 2. This means, in the two throws, we can get either no odd numbers, or 1 odd number, or both odd numbers.

Obtain the probability distribution of the number of bad apples in a draw of 3 apples at random. Solution Denote X as the number of bad apples drawn. Now X is a random variable which takes values of 0, 1, 2, 3. Assume that the number of deaths per one thousand is four persons in this group. What is the expected gain for the insurance company on a policy of this type? Solution Denote premium by X and death rate by P X.

Any unsold copies are, however, a dead loss. Gopi has estimated the following probability distribution for the number of copies demanded. Also compute the variance.

She has calculated that the cost of manufacturing as Rs. It is, however, perishable and any goods unsold at the end of the day are dead loss. She expects the demand to be variable and has drawn up the following probability distribution: Find the value of K. Find an expression for her net profit or loss if she manufactures 'm' pieces and demand is 'n' pieces. Consider separately the two cases -- 'n' lesser than or equal to 'm', and 'n' greater than 'm'. Find the net profit or loss, assuming that she manufactures 12 pieces.

Find the expected net profit. Calculate expected profit for different levels of production. The probability of a distribution cannot exceed 1. If she manufactures 'm' pieces on any day, the cost is Rs. If the number of pieces demanded on any day 'n' is less than or equal to peices produced 'm', then all the pieces demanded are sold, and the sale proceeds is Rs.

But, if the number of pieces demanded on any day 'n' is greater than the pieces produced 'm', then the maximum sales is limited to 'm' and thus the sale proceeds is Rs. Lets apply the finding in ii. Profit for m Demand Production m Probability n 10 11 12 13 14 15 10 10 8 6 4 2 0 0. Hence, the production of 12 pieces per day will optimise Ravali's food stall enterprise's expected profit. Mathematical Expectation and Variance The concept, mathematical expectation also called the expected value, occupies an important place in statistical analysis.

The expected value of a random variable is the weighted arithmetic mean of the probabilities of the values that the variable can possibly assume. Brite has defined the mathematical expectation as: It is the expected value of outcome in the long run. In other words, it is the sum of each particular value within the set X multiplied by the probability.

Symbolically The concept of mathematical expectation was originally applied to games of chance and lotteries, but the notion of an expected value has become a common term in everyday parlance. This term is popularly used in business situations which involve the consideration of expected values. Illustration 31 Mr. Reddy, owner of petrol bunk sells an average of Rs. Statistics from the Meteorological Department show that the probability is 0. Find the expected value of petrol sale and variance.

Proposal A - Profit of Rs. Solution Calculate the expected value of each proposal. Proposal A: Hence the business man should prefer proposal C. Illustration 33 The probability that there is at least one error in an accounts statement prepared by A is 0.

A, B and C prepared 10, 16 and 20 statements respectively.

Find the expected number of correct statements in all and the standard deviation. In frequency distribution, measures like average, dispersion, correlation, etc. In population, the values of variable may be distributed according to some definite probability law, and the corresponding probability distribution is known as Theoretical Probability Distribu- tion.

We have defined the mathematical expectation, random variable, and probability distribution function and also discussed these.

In the present section, we will cover the following univariate probability distributions: The first two distributions are discrete probability distributions and the third one is a continuous probability distribution.

Binomial Distribution Binomial distribution is named after the Swiss mathematician James Bernoulli who innovated it. The binomial distribution is used to determine the probability of success or failure of the one set in which there are only two equally likely and mutually exclusive outcomes. This distribution can be used under specific set of assumptions: The random experiment is performed under the finite and fixed number of trials.

The outcome of each trail results in success or failure. All the trails are independent in the sense the outcome of any trail is not affected by the preceding or succeeding trials.

The probability of success or failure remains constant from trial to trial. The success of an event is denoted by 'p' and its failure by 'q'. Since the binomial distribution is a set of dichotomous alternatives i. By expanding the binomial terms, we obtain probability distribution which called the binomial probability distribution or simply the binomial distribution. Rules of binomial expansion In binomial expansion, the rules should be noted. The constants of binomial distribution are: Determine the values of p and q.

Multiply each term of the expanded binomial by N total frequency in order to obtain the expected frequency in each category. Please use headphones Illustration 34 A coin is tossed six times. What is the probability of obtaining four or more heads.

Solution In a toss of an unbiased coin, the probability of head as well as tail is equal, i. Solution Probability of getting head and tail are denoted by p and q respectively.

The probability of r successes i. What is the probability that out of six workmen, three or more will contact the disease? Number of heads observed is recorded at each throw, and the results are given below. Find the expected frequencies. What are the theoretical values of mean and standard deviation?

Also calculate the mean and standard deviation of the observed frequencies. Arrange data: X dx Frequency F. The probability frequency is more scientific and mathematical model so that the arriving results are more accurate and precise. Illustration 39 Given data shows the number of seeds germinating out of 10 on damp filter for sets of seeds. By expanding 0. X Expected Frequencies N x n Cr q n-r p r 0 x 0.

This distribution describes the behaviour of rare events and has been known as the Law of Improbable events. Poisson distribution is a discrete probability distribution and is very popularly used in statistical inferences. The binomial distribution can be used when only the sample space number of trials n is known, while the Poisson distribution can study when we know the mean value of occurrences of an event without knowing the sample space.

Such distribution is fairly common. The standard deviation is m. Application and Uses Poisson distribution can explain the behaviour of the discrete 'random variables where the probability of occurrence of events is very small and the number of trials is sufficiently large As such, this distribution has found application in many fields like Queuing theory, Insurance, Biology, Physics, Business, Economics, Industry etc.

The practical areas where the Poisson distribution can be used is listed below. It is used in Biology to count the number of bacteria, 4. Physics to count the number of disintegrating of a radioactive element per unit of time, 5. In addition to the above, the Poisson distribution can also use in things like counting number of accidents taking place per day, in counting number of suicides in a particular day, or persons dying due to a rare disease such as heart attack or cancer or snake bite or plague, in counting number of typographical errors per page in a typed or printed material etc.

Please use headphones Illustration 40 An average number of phone calls per minute into the switch-board of Reddy Company Limited between the hours of 10 AM to 1 PM is 2. Find the probability that during one particular minute there will be i no phone calls at all, ii exactly 3 calls and iii at least 5 calls. Solution Let us denote the number of telephone calls per minute by X. The Poisson probability function is: Refer Table for e We cannot get table value for 2. First we have to find the value for e Now, multiply them to get 0.

The data are: Solution First determine observed frequency. Without referring to Table, we can calculate the value of e. For example, e This can be computed as: A more suitable distribution for dealing with the variable whose magnitude is continuous is normal distribution. It is also called the normal probability distribution. Uses 1. It aids solving many business and economic problems including the problems in social and physical sciences.

Hence, it is cornerstone of modern statistics. It becomes a basis to know how far away and in what direction a variable is from its population mean. It is symmetrical. Hence mean, median and mode are identical and can be known. It has only one maximum point at the mean, and hence it is unimodel i. Definition In mathematical form, the normal probability distribution is defined by: The normal deviate at the mean will be zero viz.

This is known as changing to standardized scale.

In equation the changing to standardized scale is written as The normal curve is distributed as under: Mean 1 covers Mean 2 covers Mean 3 covers Hence, in order to fit a curve we must know the ordinates i. Find the x , N and class interval, if any, of the observed distribution. Illustration 41 The customer accounts at the Departmental Store have an average balance of Rs.

Assuming that the account balances are normally distributed, find i. What proportion of the accounts is over Rs. What proportion of the accounts is between Rs. Proportion of accounts over Rs. Deduct the value of 0. Hence, Proportion of the accounts between Rs. Illustration 42 In a public examination students have appeared for statistics. The average mark of them was 62 and standard deviation was Assuming the distribution is normal, obtain the number of students who might have obtained i 80 percent or more, ii First class i.

Thus, x 0. In other words, the students who secure more than ranks fall under the area of 0. The Z value corresponding to 0. What do you mean by probability. Discuss the importance of probability in statistics? What is meant by mathematical expectation? Explain it with the help of an example? What is Bayes' theorem? Explain it with suitable example? What is meant by the Poisson distribution? What are its uses?

Explain the terms i. Mutually exclusive events ii. Independent and dependent events iii. Simple and compound events iv. Random variable v. Permutation and combination vi. Trial and event vii. Sample space 6. Find the probability that 2 are white and 1 is black A bag containing 8 white, 6 red and 4 black balls. Three balls are drawn at random. Find the probability that they willbe white. A bag contains 4 white and 8 red balls, and a second bag 3 white and 5 black balls.

One of the bags is chosen at random and a draw of 2 balls is made it. Find the probability that one is white and the other is black. A class consists of students, 25 of them are girls and the remaining are boys, 35 of them are rich and 65 poor, 20 of them are fair glamor What is the probability of selecting a fair glamor rich girl.

Three persons A, B and C are being considered for the appointment as Vice- Chancellor of a University whose chances of being selected for the post are in the proportion 5: The probability that A if selected will introduce democratisation in the University strut is 0. What is the probability that democratisation would be introduced in the University. The probability that a trainee will remain with a company is 0. The probability that an employee is a trainee who remained with the company or who earns more than Rs.

What is the probability than an employee earns more that Rs. In a bolt factory, machines A, B, C produce 30 per cent, 40 per cent and 30 per cent respectively.

Of their output 3, 4, 2 per cents are defective bolts. A bolt is drawn at random from the product and is found to be defective.

What are the probabilities that it was produced by machines A, B and C. A factory produces a certain type of output by two types of machines. The daily production are: Machine I - units and Machine II - units. An item is drawn at from the day's production and is found to be defective.

Dayal company estimates the net profit on a new product it is launching to be Rs. The company assigning the probabilities to the first year prospects for the product are: Successful - 0. What are the expected profit and standard deviation of first year net profit for his product. Profit 0. A systematic sample of passes was taken from the concise Oxford Dictionary and the observed frequency distribution of foreign words per page was found to be as follows: Also calculate the variance of fitted distribution.

Income of a group of persons were found to be normally distributed with mean Rs. Of third group, about 95 per cent had income exceeding Rs. What was the lowest income among the richest Chance, W. Gopikuttam, G. Gupta, S. Levin, R.. Inferring valid conclusions for making decision needs the study of statistics and application of statistical methods almost in every field of human activity.

Statistics, therefore, is regarded as the science of decision making. The statisticians can commonly categorise the techniques of statistics which are of so diverse into a descriptive statistics and b inferential statistics or inductive statistics. The former describes the characteristics of numerical data while the latter describes the judgment based on the statistical analysis.

In other words, the former is process of analysis. In other words, the former is process of analysis whereas the latter is that of scientific device of inferring conclusions.

Both are the systematic methods of drawing satisfactory valid conclusions about the totality i. The process of studying the sample and then generalising the results to the population needs a scientific investigation searching for truth.

Population and Sample The word population is technical term in statistics, not necessarily referring to people. It is totality of objects under consideration. In other words, it refers to a number of objects or items which are to be selected for investigation. This term as sometimes called the universe. Figure 1. A population containing a finite number of objects say the students in a college, is called finite population.

A population having an infinite number of objects say, heights or weights or ages of people in the country, stars in the sky etc. Having concrete objects say, the number of books in a library, the number of buses or scooters in a district, etc. If the population consists of imaginary objects say, throw of die or coin in infinite numbers of times is referred to hypothetical population.

For social scientist, it is often difficult, in fact impossible to collect information from all the objects or units of a population. He, therefore, interested to get sample data. Selection of a few objects or units forming true representative of the population is termed as sampling and the objects or units selected are termed as sample.

On the analysis being derived from the sample data, he generalises to the entire population from which the sample is drawn. The sampling has two objectives which are: Parameter and Statistics The statistical constants of the population such as population size N , population mean m , population variance 2 , population correlation coefficient p , etc are called parameters. In other words, the values that are derived using population data are known as parameters.

Similarly, the values that are derived using sample data are termed as statistics not to be confused with the word statistics meaning data or the science of statistics. The examples for statistics are sample mean x , sample variance S 2 , sample correlation coefficient r , sample size n , etc Obviously 1 statistics are quotients of the sample data whereas parameters are function of the 1 population data.

In brief the population constant is called parameter while the 1 sample constant is known as statistics. Random Sample Sampling refers to the method of selecting a sub-set of the population for investigation.

Selection of objects or units in such a way that each and every object or unit in the population has the chance of being selected is called random sampling. The number of objects or units in the sample is termed as sample size. This size should neither be too big nor too small but should be optimum. Over the census method, the sample method has distinct merits, which R. Fisher sums up thus: Speed, economy, adaptability and scientific.

This note explores successful approaches to delivering healthcare in challenging settings. It analyses organizations to find why some fall short while others grow in size and contribute to the health of the people they serve, and explore promising business models and social enterprise innovations.

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