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Old Wednesday, April 16, 2008
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Default Statistics and Probability

here are some imp stuff in stat for you:


Statistics and Probability



WHAT IS STATISTICS?

• That science which enables us to draw conclusions about various phenomena on the basis of real data collected on sample-basis

• A tool for data-based research

• Also known as Quantitative Analysis
• A lot of application in a wide variety of disciplines … Agriculture, Anthropology, Astronomy, Biology, Economic, Engineering, Environment, Geology, Genetics, Medicine, Physics, Psychology, Sociology, Zoology …. Virtually every single subject from Anthropology to Zoology …. A to Z!

• Any scientific enquiry in which you would like to base your conclusions and decisions on real-life data, you need to employ statistical techniques!

• Now a day, in the developed countries of the world, there is an active movement for of Statistical Literacy.

THE NATURE OF THIS DISCIPLINE:

DESCRIPTIVE STATISTICS

PROBABILITY

INFERENTIAL STATISTICS

MEANINGS OF ‘STATISTICS’:

The word “Statistics” which comes from the Latin words status, meaning a political state, originally meant information useful to the state, for example, information about the sizes of population sand armed forces. But this word has now acquired different meanings.

• In the first place, the word statistics refers to “numerical facts systematically arranged”. In this sense, the word statistics is always used in plural. We have, for instance, statistics of prices, statistics of road accidents, statistics of crimes, statistics of births, statistics of educational institutions, etc. In all these examples, the word statistics denotes a set of numerical data in the respective fields. This is the meaning the man in the street gives to the word Statistics and most people usually use the word data instead.

• In the second place, the word statistics is defined as a discipline that includes procedures and techniques used to collect, process and analyze numerical data to make inferences and to research decisions in the face of uncertainty. It should of course be borne in mind that uncertainty does not imply ignorance but it refers to the incompleteness and the instability of data available. In this sense, the word statistics is used in the singular. As it embodies more of less all stages of the general process of learning, sometimes called scientific method, statistics is characterized as a science. Thus the word statistics used in the plural refers to a set of numerical information and in the singular, denotes the science of basing decision on numerical data. It should be noted that statistics as a subject is mathematical in character.

• Thirdly, the word statistics are numerical quantities calculated from sample observations; a single quantity that has been so collected is called a statistic. The mean of a sample for instance is a statistic. The word statistics is plural when used in this sense.

CHARACTERISTICS OF THE SCIENCE OF STATISTICS:

Statistics is a discipline in its own right. It would therefore be desirable to know the characteristic features of statistics in order to appreciate and understand its general nature. Some of its important characteristics are given below:

i) Statistics deals with the behaviour of aggregates or large groups of data. It has nothing to do with what is happening to a particular individual or object of the aggregate.

ii) Statistics deals with aggregates of observations of the same kind rather than isolated figures.

iii) Statistics deals with variability that obscures underlying patterns. No two objects in this universe are exactly alike. If they were, there would have been no statistical problem.

iv) Statistics deals with uncertainties as every process of getting observations whether controlled or uncontrolled, involves deficiencies or chance variation. That is why we have to talk in terms of probability.

v) Statistics deals with those characteristics or aspects of things which can be described numerically either by counts or by measurements.

vi) Statistics deals with those aggregates which are subject to a number of random causes, e.g. the heights of persons are subject to a number of causes such as race, ancestry, age, diet, habits, climate and so forth.

vii) Statistical laws are valid on the average or in the long run. There is n guarantee that a certain law will hold in all cases. Statistical inference is therefore made in the face of uncertainty.

viii) Statistical results might be misleading the incorrect if sufficient care in collecting, processing and interpreting the data is not exercised or if the statistical data are handled by a person who is not well versed in the subject mater of statistics.

THE WAY IN WHICH STATISTICS WORKS:

As it is such an important area of knowledge, it is definitely useful to have a fairly good idea about the way in which it works, and this is exactly the purpose of this introductory course.
The following points indicate some of the main functions of this science:

• Statistics assists in summarizing the larger set of data in a form that is easily understandable.

• Statistics assists in the efficient design of laboratory and field experiments as well as surveys.

• Statistics assists in a sound and effective planning in any field of inquiry.

• Statistics assists in drawing general conclusions and in making predictions of how much of a thing will happen under given conditions.

IMPORTANCE OF STATISTICS IN VARIOUS FIELDS

As stated earlier, Statistics is a discipline that has finds application in the most diverse fields of activity. It is perhaps a subject that should be used by everybody. Statistical techniques being powerful tools for analyzing numerical data are used in almost every branch of learning. In all areas, statistical techniques are being increasingly used, and are developing very rapidly.

• A modern administrator whether in public or private sector leans on statistical data to provide a factual basis for decision.

• A politician uses statistics advantageously to lend support and credence to his arguments while elucidating the problems he handles.

• A businessman, an industrial and a research worker all employ statistical methods in their work. Banks, Insurance companies and Government all have their statistics departments.

• A social scientist uses statistical methods in various areas of socio-economic life a nation. It is sometimes said that “a social scientist without an adequate understanding of statistics, is often like the blind man groping in a dark room for a black cat that is not there”.

The Meaning of Data:

The word “data” appears in many contexts and frequently is used in ordinary conversation. Although the word carries something of an aura of scientific mystique, its meaning is quite simple and mundane. It is Latin for “those that are given” (the singular form is “datum”). Data may therefore be thought of as the results of observation.

Examples of Date:

Data are collected in many aspects of everyday life.

• Statements given to a police officer or physician or psychologist during an interview are data.

• So are the correct and incorrect answers given by a student on a final examination.

• Almost any athletic event produces data.

• The time required by a runner to complete a marathon,

• The number of errors committed by a baseball team in nine innings of play.

And, of course, data are obtained in the course of scientific inquiry:

• the positions of artifacts and fossils in an archaeological site,

• The number of interactions between two members of an animal colony during a period of observation,

• The spectral composition of light emitted by a star.

OBSERVATIONS AND VARIABLES:

In statistics, an observation often means any sort of numerical recording of information, whether it is a physical measurement such as height or weight; a classification such as heads or tails, or an answer to a question such as yes or no.

Variables:

A characteristic that varies with an individual or an object, is called a variable. For example, age is a variable as it varies from person to person. A variable can assume a number of values. The given set of all possible values from which the variable takes on a value is called its Domain. If for a given problem, the domain of a variable contains only one value, then the variable is referred to as a constant.

QUANTITATIVE AND QUALITATIVE VARIABLES:

Variables may be classified into quantitative and qualitative according to the form of the characteristic of interest.

A variable is called a quantitative variable when a characteristic can be expressed numerically such as age, weight, income or number of children. On the other hand, if the characteristic is non-numerical such as education, sex, eye-colour, quality, intelligence, poverty, satisfaction, etc. the variable is referred to as a qualitative variable. A qualitative characteristic is also called an attribute. An individual or an object with such a characteristic can be counted or enumerated after having been assigned to one of the several mutually exclusive classes or categories.

Discrete and Continuous Variables:

A quantitative variable may be classified as discrete or continuous. A discrete variable is one that can take only a discrete set of integers or whole numbers, which is the values are taken by jumps or breaks. A discrete variable represents count data such as the number of persons in a family, the number of rooms in a house, the number of deaths in an accident, the income of an individual, etc.

A variable is called a continuous variable if it can take on any value-fractional or integral––within a given interval, i.e. its domain is an interval with all possible values without gaps. A continuous variable represents measurement data such as the age of a person, the height of a plant, the weight of a commodity, the temperature at a place, etc.

A variable whether countable or measurable, is generally denoted by some symbol such as X or Y and Xi or Xj represents the ith or jth value of the variable. The subscript i or j is replaced by a number such as 1,2,3, … when referred to a particular value.

Measurement Scales:

By measurement, we usually mean the assigning of number to observations or objects and scaling is a process of measuring. The four scales of measurements are briefly mentioned below:

NOMINAL SCALE:

The classification or grouping of the observations into mutually exclusive qualitative categories or classes is said to constitute a nominal scale. For example, students are classified as male and female. Number 1 and 2 may also be used to identify these two categories. Similarly, rainfall may be classified as heavy moderate and light. We may use number 1, 2 and 3 to denote the three classes of rainfall. The numbers when they are used only to identify the categories of the given scale, carry no numerical significance and there is no particular order for the grouping.

ORDINAL OR RANKING SCALE:

It includes the characteristic of a nominal scale and in addition has the property of ordering or ranking of measurements. For example, the performance of students (or players) is rated as excellent, good fair or poor, etc. Number 1, 2, 3, 4 etc. are also used to indicate ranks. The only relation that holds between any pair of categories is that of “greater than” (or more preferred).

INTERVAL SCALE:

A measurement scale possessing a constant interval size (distance) but not a true zero point, is called an interval scale. Temperature measured on either the Celsius or the Fahrenheit scale is an outstanding example of interval scale because the same difference exists between 20o C (68o F) and 30o C (86o F) as between 5o C (41o F) and 15o C (59o F). It cannot be said that a temperature of 40 degrees is twice as hot as a temperature of 20 degree, i.e. the ratio 40/20 has no meaning. The arithmetic operation of addition, subtraction, etc. is meaningful.

RATIO SCALE:

It is a special kind of an interval scale where the sale of measurement has a true zero point as its origin. The ratio scale is used to measure weight, volume, distance, money, etc. The, key to differentiating interval and ratio scale is that the zero point is meaningful for ratio scale.

ERRORS OF MEASUREMENT:

Experience has shown that a continuous variable can never be measured with perfect fineness because of certain habits and practices, methods of measurements, instruments used, etc. the measurements are thus always recorded correct to the nearest units and hence are of limited accuracy. The actual or true values are, however, assumed to exist. For example, if a student’s weight is recorded as 60 kg (correct to the nearest kilogram), his true weight in fact lies between 59.5 kg and 60.5 kg, whereas a weight recorded as 60.00 kg means the true weight is known to lie between 59.995 and 60.005 kg. Thus there is a difference, however small it may be between the measured value and the true value. This sort of departure from the true value is technically known as the error of measurement. In other words, if the observed value and the true value of a variable are denoted by x and x +  respectively, then the difference (x + ) – x, i.e.  is the error. This error involves the unit of measurement of x and is therefore called an absolute error. An absolute error divided by the true value is called the relative error. Thus the relative error , which when multiplied by 100, is percentage error. These errors are independent of the units of measurement of x. It ought to be noted that an error has both magnitude and direction and that the word error in statistics does not mean mistake which is a chance inaccuracy.

BIASED AND RANDOM ERRORS:

An error is said to be biased when the observed value is consistently and constantly higher or lower than the true value. Biased errors arise from the personal limitations of the observer, the imperfection in the instruments used or some other conditions which control the measurements. These errors are not revealed by repeating the measurements. They are cumulative in nature, that is, the greater the number of measurements, the greater would be the magnitude of error. They are thus more troublesome. These errors are also called cumulative or systematic errors.
An error, on the other hand, is said to be unbiased when the deviations, i.e. the excesses and defects, from the true value tend to occur equally often. Unbiased errors and revealed when measurements are repeated and they tend to cancel out in the long run. These errors are therefore compensating and are also known as random errors or accidental errors.

Lecture No.2:

Steps involved in a Statistical Research-Project

• Collection of Data:
 Primary Data
 Secondary Data

• Sampling:
 Concept of Sampling
 Non-Random Versus Random Sampling
 Simple Random Sampling
 Other Types of Random Sampling

STEPS INVOLVED IN ANY STATISTICAL RESEARCH:

1. Topic and significance of the study

2. Objective of your study

3. Methodology for data-collection

• Source of your data
• Sampling methodology
• Instrument for collecting data

As far as the objectives of your research are concerned, they should be stated in such a way that you are absolutely clear about the goal of your study --- EXACTLY WHAT IT IS THAT YOU ARE TRYING TO FIND OUT?
As far as the methodology for DATA-COLLECTION is concerned, you need to consider:

• Source of your data (the statistical population)

• Sampling Methodology

• Instrument for collecting data

COLLECTION OF DATA:

The most important part of statistical work is perhaps the collection of data.
Statistical data are collected either by a COMPLETE enumeration of the whole field, called CENSUS, which in many cases would be too costly and too time consuming as it requires large number of enumerators and supervisory staff, or by a PARTIAL enumeration associated with a SAMPLE which saves much time and money.

PRIMARY AND SECONDARY DATA:

Data that have been originally collected (raw data) and have not undergone any sort of statistical treatment, are called PRIMARY data. Data that have undergone any sort of treatment by statistical methods at least ONCE, i.e. the data that have been collected, classified, tabulated or presented in some form for a certain purpose, are called SECONDARY data.

COLLECTION OF PRIMARY DATA:

One or more of the following methods are employed to collect primary data:

i) Direct Personal Investigation.
ii) Indirect Investigation.
iii) Collection through Questionnaires.
iv) Collection through Enumerators.
v) Collection through Local Sources.

DIRECT PERSONAL INVESTIGATION:

In this method, an investigator collects the information personally from the individuals concerned. Since he interviews the informants himself, the information collected is generally considered quite accurate and complete.
This method may prove very costly and time-consuming when the area to be covered is vast.

However, it is useful for laboratory experiments or localized inquiries. Errors are likely to enter the results due to personal bias of the investigator.

INDIRECT INVESTIGATION:

Sometimes the direct sources do not exist or the informants hesitate to respond for some reason or other. In such a case, third parties or witnesses having information are interviewed.

Moreover, due allowance is to be made for the personal bias. This method is useful when the information desired is complex or there is reluctance or indifference on the part of the informants. It can be adopted for extensive inquiries.

COLLECTION THROUGH QUESTIONNAIRES:

A questionnaire is an inquiry form comprising of a number of pertinent questions with space for entering information asked.

The questionnaires are usually sent by mail, and the informants are requested to return the questionnaires to the investigator after doing the needful within a certain period.

This method is cheap, fairly expeditious and good for extensive inquiries.
But the difficulty is that the majority of the respondents (i.e. persons who are required to answer the questions) do not care to fill the questionnaires in, and to return them to the investigators. Sometimes, the questionnaires are returned incomplete and full of errors. Students, in spite of these drawbacks, this method is considered as the STANDARD method for routine business and administrative inquiries.

It is important to note that the questions should be few, brief, very simple, easy for all respondents to answer, clearly worded and not offensive to certain respondents.

COLLECTION THROUGH ENUMERATORS:

Under this method, the information is gathered by employing trained enumerators who assist the informants in making the entries in the schedules or questionnaires correctly.

This method gives the most reliable information if the enumerator is well-trained, experienced and tactful.

Students, it is considered the BEST method when a large-scale governmental inquiry is to be conducted. This method can generally not be adopted by a private individual or institution as its cost would be prohibitive to them.

COLLECTION THROUGH LOCAL SOURCES:

In this method, there is no formal collection of data but the agents or local correspondents are directed to collect and send the required information, using their own judgment as to the best way of obtaining it.
This method is cheap and expeditious, but gives only the estimates.

COLLECTION OF SECONDARY DATA:

The secondary data may be obtained from the following sources:

i) Official, e.g. the publications of the Statistical Division, Ministry of Finance, the Federal and Provincial Bureaus of Statistics, Ministries of Food, Agriculture, Industry, Labour, etc.

ii) Semi-Official, e.g., State Bank of Pakistan, Railway Board, Central Cotton Committee, Boards of Economic Inquiry, District Councils, Municipalities, etc.

iii) Publications of Trade Associations, Chambers of Commerce, etc.

iv) Technical and Trade Journals and Newspapers.

v) Research Organizations such as universities, and other institutions.

Let us now consider the POPULATION from which we will be collecting our data. In this context, the first important question is:

Why do we have to resort to Sampling?

The answer is that:If we have available to us every value of the variable under study, then that would be an ideal and a perfect situation. But, the problem is that this ideal situation is very rarely available --- very rarely do we have access to the entire population. The census is an exercise in which an attempt is made to cover the entire population. But, as you might be knowing, even the most developed countries of the world cannot afford to conduct such a huge exercise on an annual basis! More often than not, we have to conduct our research study on a sample basis. In fact, the goal of the science of Statistics is to draw conclusions about large populations on the basis of information contained in small samples.

Let us now define some terms in a formal way:

'POPULATION':

A statistical population is the collection of every member of a group possessing the same basic and defined characteristic, but varying in amount or quality from one member to another.

Examples:

• Finite population:
 IQ’s of all children in a school.

• Infinite population:
 Barometric pressure:
(There are an indefinitely large number of points on the surface of the earth).
 A flight of migrating ducks in Canada
(Many finite pops are so large that they can be treated as effectively infinite.)

The examples that we have just considered are those of existent populations.

A hypothetical population can be defined as the aggregate of all the conceivable ways in which a specified event can happen.

For Example:

1)All the possible outcomes from the throw of a die – however long we throw the die and record the results, we could always continue to do so far a still longer period in a theoretical concept – one which has no existence in reality.

2) The No. of ways in which a football team of 11 players can be selected from the 16 possible members named by the Club Manager.

We also need to differentiate between the sampled population and the target population. Sampled population is that from which a sample is chosen whereas the population about which information is sought is called the target population Thus our population will consist of the total no. of students in all the colleges in the Punjab.

Suppose on account of shortage of resources or of time, we are able to conduct such a survey on only 5 colleges scattered throughout the province. In this case, the students of all the colleges will constitute the target pop whereas the students of those 5 colleges from which the sample of students will be selected will constitute the sampled population.

I am sure that from the above discussion regarding the population, you must have realized how important it is to have a very well-defined population.

The next question is: How will we draw a sample from our population?

In order to draw a random sample from a finite population, the first thing that we need is the complete list of all the elements in our population. This list is technically called the FRAME.

SAMPLING FRAME:

A sampling frame is a complete list of all the elements in the population.

For example:

• The complete list of the BCS students of Virtual University of Pakistan on February 15, 2003.

Speaking of the sampling frame, it must be kept in mind that, as far as possible, our frame should be free from various types of defects:

• does not contain inaccurate elements
• is not incomplete
• is free from duplication, and
• Is not out of date.

Next, let’s talk about the sample that we are going to draw from this population.

As you all know, a sample is only a part of a statistical population, and hence it can represent the population to only to some extent. Of course, it is intuitively logical that the larger the sample, the more likely it is to represent the population.

Obviously, the limiting case is that: when the sample size tends to the population size, the sample will tend to be identical to the population. But, of course, in general, the sample is much smaller than the population.
The point is that, in general, statistical sampling seeks to determine how accurate a description of the population the sample and its properties will provide. We may have to compromise on accuracy, but there are certain such advantages of sampling because of which it has an extremely important place in data-based research studies.

ADVANTAGES OF SAMPLING :

1. Savings in time and money.

• Although cost per unit in a sample is greater than in a complete investigation, the total cost will be less (because the sample will be so much smaller than the statistical population from which it has been drawn).

• A sample survey can be completed faster than a full investigation so that variations from sample unit to sample unit over time will largely be eliminated.

• Also, the results can be processed and analyzed with increased speed and precision because there are fewer of them.

2. More detailed information may be obtained from each sample unit.

3. Possibility of follow-up:

(After detailed checking, queries and omissions can be followed up --- a procedure which might prove impossible in a complete survey).

4. Sampling is the only feasible possibility where tests to destruction are undertaken or where the population is effectively infinite.

The next two important concepts that need to be considered are those of sampling and non-sampling errors.

Sampling & Non-Sampling Errors:

1. Sampling Error:

The difference between the estimate derived from the sample (i.e. the statistic) and the true population value (i.e. the parameter) is technically called the sampling error. For example, Sampling error arises due to the fact that a sample cannot exactly represent the pop, even if it is drawn in a correct manner

2. Non-Sampling Error:

Besides sampling errors, there are certain errors which are not attributable to sampling but arise in the process of data collection, even if a complete count is carried out.

Main sources of non sampling errors are:

1. The defect in the sampling frame.
2. Faulty reporting of facts due to personal preferences.
3. Negligence or indifference of the investigators
4. Non-response to mail questionnaires.

These (non-sampling) errors can be avoided through
1. Following up the non-response,
2. Proper training of the investigators.
3. Correct manipulation of the collected information, etc.

Let us now consider exactly what is meant by ‘sampling error’:

We can say that there are two types of non-response --- partial non-response and total non-response. ‘Partial non-response’ implies that the respondent refuses to answer some of the questions. On the other hand, ‘total non-response’ implies that the respondent refuses to answer any of the questions. Of course, the problem of late returns and non-response of the kind that I have just mentioned occurs in the case of HUMAN populations.
Although refusal of sample units to cooperate is encountered in interview surveys, it is far more of a problem in mail surveys. It is not uncommon to find the response rate to mail questionnaires as low as 15 or 20%.The provision of INFORMATION ABOUT THE PURPOSE OF THE SURVEY helps in stimulating interest, thus increasing the chances of greater response. Particularly if it can be shown that the work will be to the ADVANTAGE of the respondent IN THE LONG RUN.

Similarly, the respondent will be encouraged to reply if a pre-paid and addressed ENVELOPE is sent out with the questionnaire. But in spite of these ways of reducing non-response, we are bound to have some amount of non-response. Hence, a decision has to be taken about how many RECALLS should be made. The term ‘recall’ implies that we approach the respondent more than once in order to persuade him to respond to our queries.

Another point worth considering is:

How long the process of data collection should be continued?

Obviously, no such process can be carried out for an indefinite period of time!
In fact, the longer the time period over which the survey is conducted, the greater will be the potential VARIATIONS in attitudes and opinions of the respondents. Hence, a well-defined cut-off date generally needs to be established.

Let us now look at the various ways in which we can select a sample from our population. We begin by looking at the difference between non-random and RANDOM sampling. First of all, what do we mean by non-random sampling?

Nonrandom Sampling:

'Nonrandom sampling' implies that kind of sampling in which the population units are drawn into the sample by using one’s personal judgment.
This type of sampling is also known as purposive sampling. Within this category, one very important type of sampling is known as Quota Sampling.

QUOTA SAMPLING:

In this type of sampling, the selection of the sampling unit from the population is no longer dictated by chance. A sampling frame is not used at all, and the choice of the actual sample units to be interviewed is left to the discretion of the interviewer. However, the interviewer is restricted by quota controls. For example, one particular interviewer may be told to interview ten married women between thirty and forty years of age living in town X, whose husbands are professional workers, and five unmarried professional women of the same age living in the same town.

Quota sampling is often used in commercial surveys such as consumer market-research. Also, it is often used in public opinion polls.

ADVANTAGES OF QUOTA SAMPLING:

1) There is no need to construct a frame.
2) It is a very quick form of investigation.
3) Cost reduction.

DISADVANTAGES:

1) It is a subjective method. One has to choose between objectivity and convenience.

2) If random sampling is not employed, it is no longer theoretically possible to evaluate the sampling error. (Since the selection of the elements is not based on probability theory but on the personal judgment of the interviewer, hence the precision and the reliability of the estimates can not be determined objectively i.e. in terms of probability.)

3) Although the purpose of implementing quota controls is to reduce bias, bias creeps in due to the fact that the interviewer is FREE to select particular individuals within the quotas. (Interviewers usually look for persons who either agree with their points of view or are personally known to them or can easily be contacted.)

4) Even if the above is not the case, the interviewer may still be making unsuitable selection of sample units. (Although he may put some qualifying questions to a potential respondent in order to determine whether he or she is of the type prescribed by the quota controls, some features must necessarily be decided arbitrarily by the interviewer, the most difficult of these being social class.)

If mistakes are being made, it is almost impossible for the organizers to detect these, because follow-ups are not possible unless a detailed record of the respondents’ names, addresses etc. has been kept. Falsification of returns is therefore more of a danger in quota sampling than in random sampling. In spite of the above limitations, it has been shown by F. Edwards that a well-organized quota survey with well-trained interviewers can produce quite adequate results.

Next, let us consider the concept of random sampling.

Random Sampling:

The theory of statistical sampling rests on the assumption that the selection of the sample units has been carried out in a random manner.
By random sampling we mean sampling that has been done by adopting the lottery method.

TYPES OF RANDOM SAMPLING

• Simple Random Sampling
• Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling
• Multi-stage Sampling, etc.

In this course, I will discuss with you the simplest type of random sampling i.e. simple random sampling.

SIMPLE RANDOM SAMPLING:

In this type of sampling, the chance of any one element of the parent pop being included in the sample is the same as for any other element. By extension, it follows that, in simple random sampling, the chance of any one sample appearing is the same as for any other. There exists quite a lot of misconception regarding the concept of random sampling. Many a time, haphazard selection is considered to be equivalent to simple random sampling. For example, a market research interviewer may select women shoppers to find their attitude to brand X of a product by stopping one and then another as they pass along a busy shopping area --- and he may think that he has accomplished simple random sampling! Actually, there is a strong possibility of bias as the interviewer may tend to ask his questions of young attractive women rather than older housewives, or he may stop women who have packets of brand X prominently on show in their shopping bags!. In this example, there is no suggestion of INTENTIONAL bias! From experience, it is known that the human being is a poor random selector --- one who is very subject to bias. Fundamental psychological traits prevent complete objectivity, and no amount of training or conscious effort can eradicate them. As stated earlier, random sampling is that in which population units are selected by the lottery method. As you know, the traditional method of writing people’s names on small pieces of paper, folding these pieces of paper and shuffling them is very cumbersome! A much more convenient alternative is the use of RANDOM NUMBERS TABLES.

A random number table is a page full of digits from zero to 9. These digits are printed on the page in a TOTALLY random manner i.e. there is no systematic pattern of printing these digits on the page.

ONE THOUSAND RANDOM DIGITS:

2 3 1 5 7 5 4 8 5 9 0 1 8 3 7 2 5 9 9 3 7 6 2 4 9 7 0 8 8 6 9 5 2 3 0 3 6 7 4 4
0 5 5 4 5 5 5 0 4 3 1 0 5 3 7 4 3 5 0 8 9 0 6 1 1 8 3 7 4 4 1 0 9 6 2 2 1 3 4 3
1 4 8 7 1 6 0 3 5 0 3 2 4 0 4 3 6 2 2 3 5 0 0 5 1 0 0 3 2 2 1 1 5 4 3 8 0 8 3 4
3 8 9 7 6 7 4 9 5 1 9 4 0 5 1 7 5 8 5 3 7 8 8 0 5 9 0 1 9 4 3 2 4 2 8 7 1 6 9 5
9 7 3 1 2 6 1 7 1 8 9 9 7 5 5 3 0 8 7 0 9 4 2 5 1 2 5 8 4 1 5 4 8 8 2 1 0 5 1 3
1 1 7 4 2 6 9 3 8 1 4 4 3 3 9 3 0 8 7 2 3 2 7 9 7 3 3 1 1 8 2 2 6 4 7 0 6 8 5 0
4 3 3 6 1 2 8 8 5 9 1 1 0 1 6 4 5 6 2 3 9 3 0 0 9 0 0 4 9 9 4 3 6 4 0 7 4 0 3 6
9 3 8 0 6 2 0 4 7 8 3 8 2 6 8 0 4 4 9 1 5 5 7 5 1 1 8 9 3 2 5 8 4 7 5 5 2 5 7 1
4 9 5 4 0 1 3 1 8 1 0 8 4 2 9 8 4 1 8 7 6 9 5 3 8 2 9 6 6 1 7 7 7 3 8 0 9 5 2 7
3 6 7 6 8 7 2 6 3 3 3 7 9 4 8 2 1 5 6 9 4 1 9 5 9 6 8 6 7 0 4 5 2 7 4 8 3 8 8 0
0 7 0 9 2 5 2 3 9 2 2 4 6 2 7 1 2 6 0 7 0 6 5 5 8 4 5 3 4 4 6 7 3 3 8 4 5 3 2 0
4 3 3 1 0 0 1 0 8 1 4 4 8 6 3 8 0 3 0 7 5 2 5 5 5 1 6 1 4 8 8 9 7 4 2 9 4 6 4 7
6 1 5 7 0 0 6 3 6 0 0 6 1 7 3 6 3 7 7 5 6 3 1 4 8 9 5 1 2 3 3 5 0 1 7 4 6 9 9 3
3 1 3 5 2 8 3 7 9 9 1 0 7 7 9 1 8 9 4 1 3 1 5 7 9 7 6 4 4 8 6 2 5 8 4 8 6 9 1 9
5 7 0 4 8 8 6 5 2 6 2 7 7 9 5 9 3 6 8 2 9 0 5 2 9 5 6 5 4 6 3 5 0 6 5 3 2 2 5 4
0 9 2 4 3 4 4 2 0 0 6 8 7 2 1 0 7 1 3 7 3 0 7 2 9 7 5 7 3 6 0 9 2 9 8 2 7 6 5 0
9 7 9 5 5 3 5 0 1 8 4 0 8 9 4 8 8 3 2 9 5 2 2 3 0 8 2 5 2 1 2 2 5 3 2 6 1 5 8 7
9 3 7 3 2 5 9 5 7 0 4 3 7 8 1 9 8 8 8 5 5 6 6 7 1 6 6 8 2 6 9 5 9 9 6 4 4 5 6 9
7 2 6 2 1 1 1 2 2 5 0 0 9 2 2 6 8 2 6 4 3 5 6 6 6 5 9 4 3 4 7 1 6 8 7 5 1 8 6 7
6 1 0 2 0 7 4 4 1 8 4 5 3 7 1 2 0 7 9 4 9 5 9 1 7 3 7 8 6 6 9 9 5 3 6 1 9 3 7 8
9 7 8 3 9 8 5 4 7 4 3 3 0 5 5 9 1 7 1 8 4 5 4 7 3 5 4 1 4 4 2 2 0 3 4 2 3 0 0 0
8 9 1 6 0 9 7 1 9 2 2 2 2 3 2 9 0 6 3 7 3 5 0 5 5 4 5 4 8 9 8 8 4 3 8 1 6 3 6 1
2 5 9 6 6 8 8 2 2 0 6 2 8 7 1 7 9 2 6 5 0 2 8 2 3 5 2 8 6 2 8 4 9 1 9 5 4 8 8 3
8 1 4 4 3 3 1 7 1 9 0 5 0 4 9 5 4 8 0 6 7 4 6 9 0 0 7 5 6 7 6 5 0 1 7 1 6 5 4 5
1 1 3 2 2 5 4 9 3 1 4 2 3 6 2 3 4 3 8 6 0 8 6 2 4 9 7 6 6 7 4 2 2 4 5 2 3 2 4 5

Actually, Random Number Tables are constructed according to certain mathematical principles so that each digit has the same chance of selection. Of course, nowadays randomness may be achieved electronically. Computers have all those programmes by which we can generate random numbers.

Example:

The following frequency table of distribution gives the ages of a population of 1000 teen-age college students in a particular country. Select a sample of 10 students using the random numbers table. Find the sample mean age and compare with the population mean age.

How will we proceed to select our sample of size 10 from this population of size 1000?

The first step is to allocate to each student in this population a sampling number. For this purpose, we will begin by constructing a column of cumulative frequencies.

Now that we have the cumulative frequency of each class, we are in a position to allocate the sampling numbers to all the values in a class. As the frequency as well as the cumulative frequency of the first class is 6, we allocate numbers 000 to 005 to the six students who belong to this class.

As the cumulative frequency of the second class is 67 while that of the first class was 6, therefore we allocate sampling numbers 006 to 066 to the 61 students who belong to this class.

As the cumulative frequency of the third class is 337 while that of the second class was 67, therefore we allocate sampling numbers 007 to 337 to the 270 students who belong to this class.

Proceeding in this manner, we obtain the column of sampling numbers.

The column implies that the first student of the first class has been allocated the sampling number 000, the second student has been allocated the sampling 001, and, proceeding in this fashion, the last student i.e. the 1000th student has been allocated the sampling number 999.

The question is: Why did we not allot the number 0001 to the first student and the number 1000 to the 1000th student?

The answer is that we could do that but that would have meant that every student would have been allocated a four-digit number, whereas by shifting the number backward by 1, we are able to allocate to every student a three-digit number --- which is obviously simpler.

The next step is to SELECT 10 RANDOM NUMBERS from the random number table. This is accomplished by closing one’s eyes and letting one’s finger land anywhere on the random number table.

In this example, since all our sampling numbers are three-digit numbers, hence we will read three digits that are adjacent to each other at that position where our finger landed. Suppose that we adopt this procedure and our random numbers come out to be 041, 103, 374, 171, 508, 652, 880, 066, 715, 471.

Selected Random Numbers:

041, 103, 374, 171, 508, 652, 880, 066, 715, 471.

Thus the corresponding ages are:

14, 15, 16, 15, 16, 16, 17, 15, 16, 16

Explanation:

Our first selected random number is 041 which means that we have to pick up the 42nd student. The cumulative frequency of the first class is 6 whereas the cumulative frequency of the second class is 67. This means that definitely the 42nd student does not belong to the first class but does belong to the second class.

The age of each student in this class is 14 years; hence, obviously, the age of the 42nd student is also 14 years. This is how we are able to ascertain the ages of all the students who have been selected in our sampling. You will recall that in this example, our aim was to draw a sample from the population of college students, and to compare the sample’s mean age with the population mean age. The population mean age comes out to be 15.785 years.

The population mean age is :

The above formula is a slightly modified form of the basic formula that you have done ever-since school-days i.e. the mean is equal to the sum of all the observations divided by the total number of observations.
Next, we compute the sample mean age.

Adding the 10 values and dividing by 10, we obtain:

Ages of students selected in the sample (in years):
14, 15, 16, 15, 16, 16, 17, 15, 16, 16

Hence the sample mean age is:

Comparing the sample mean age of 15.6 years with the population mean age of 15.785 years, we note that the difference is really quite slight, and hence the sampling error is equal to

And the reason for such a small error is that we have adopted the RANDOM sampling method.

The basic advantage of random sampling is that the probability is very high that the sample will be a good representative of the population from which it has been drawn, and any quantity computed from the sample will be a good estimate of the corresponding quantity computed from the population! Actually, a sample is supposed to be a MINIATURE REPLICA of the population. As stated earlier, there are various other types of random sampling.

Other Types of Random Sampling

• Stratified sampling (if the population is heterogeneous)

• Systematic sampling (practically, more convenient than simple random sampling)

• Cluster sampling (sometimes the sampling units exist in natural clusters)

• Multi-stage sampling,etc.

• All these designs rest upon random or quasi-random sampling. They are various forms of PROBABILITY sampling --- that in which each sampling unit has a known (but not necessarily equal) probability of being selected.
Because of this knowledge, there exist methods by which the precision and the reliability of the estimates can be calculated OBJECTIVELY.

It should be realized that in practice, several sampling techniques are incorporated into each survey design, and only rarely will simple random sample be used, or a multi-stage design be employed, without stratification.
The point to remember is that whatever method be adopted, care should be exercised at every step so as to make the results as reliable as possible.


Lecture No 3:

• Tabulation
• Simple bar chart
• Component bar chart
• Multiple bar chart
• Pie chart

As indicated in the last lecture, there are two broad categories of data … qualitative data and quantitative data. A variety of methods exist for summarizing and describing these two types of data. The tree-diagram below presents an outline of the various techniques

In today’s lecture, we will be dealing with various techniques for summarizing and describing qualitative data. We will begin with the univariate situation, and will proceed to the bivariate situation.

EXAMPLE:

Suppose that we are carrying out a survey of the students of first year studying in a co-educational college of Lahore. Suppose that in all there are 1200 students of first year in this large college. We wish to determine what proportion of these students have come from Urdu medium schools and what proportion has come from English medium schools. So we will interview the students and we will inquire from each one of them about their schooling.
As a result, we will obtain a set of data as you can now see on the screen. We will have an array of observations as follows:

U, U, E, U, E, E, E, U, ……

(U : URDU MEDIUM) (E : ENGLISH MEDIUM)

Now, the question is what should we do with this data?

Obviously, the first thing that comes to mind is to count the number of students who said “Urdu medium” as well as the number of students who said “English medium”. This will result in the following table:

Medium of Institution No. of Students (f)
Urdu 719
English 481
1200

The technical term for the numbers given in the second column of this table is “frequency”. It means “how frequently something happens?”

Out of the 1200 students, 719 stated that they had come from Urdu medium schools. So in this example, the frequency of the first category of responses is 719 whereas the frequency of the second category of responses is 481.

It is evident that this information is not as useful as if we compute the proportion or percentage of students falling in each category. Dividing the cell frequencies by the total frequency and multiplying by 100 we obtain the following:

Medium of Institution f %
Urdu 719 59.9 = 60%
English 481 40.1 = 40%
1200

What we have just accomplished is an example of a univariate frequency table pertaining to qualitative data. Let us now see how we can represent this information in the form of a diagram.

One good way of representing the above information is in the form of a pie chart. A pie chart consists of a circle which is divided into two or more parts in accordance with the number of distinct categories that we have in our data.

For the example that we have just considered, the circle is divided into two sectors, the larger sector pertaining to students coming from Urdu medium schools and the smaller sector pertaining to students coming from English medium schools.

How do we decide where to cut the circle?

The answer is very simple! All we have to do is to divide the cell frequency by the total frequency and multiply by 360. This process will give us the exact value of the angle at which we should cut the circle.

PIE CHART

Medium of Institution f Angle
Urdu 719 215.70
ENGLISH 481 144.30
1200


SIMPLE BAR CHART:

The next diagram to be considered is the simple bar chart. A simple bar chart consists of horizontal or vertical bars of equal width and lengths proportional to values they represent. As the basis of comparison is one-dimensional, the widths of these bars have no mathematical significance but are taken in order to make the chart look attractive. Let us consider an example.

Suppose we have available to us information regarding the turnover of a company for 5 years as given in the table below:

Years 1965 1966 1967 1968 1969
Turnover (Rupees) 35,000 42,000 43,500 48,000 48,500

In order to represent the above information in the form of a bar chart, all we have to do is to take the year along the x-axis and construct a scale for turnover along the y-axis.

Next, against each year, we will draw vertical bars of equal width and different heights in accordance with the turn-over figures that we have in our table.

As a result we obtain a simple and attractive diagram as shown below.
When our values do not relate to time, they should be arranged in ascending or descending order before-charting.

BIVARIATE FREQUENCY TABLE:

What we have just considered was the univariate situation. In each of the two examples, we were dealing with one single variable. In the example of the first year students of a college, our lone variable of interest was ‘medium of schooling’. And in the second example, our one single variable of interest was turnover.

Now let us expand the discussion a little, and consider the bivariate situation.
Going back to the example of the first year students, suppose that alongwith the enquiry about the Medium of Institution, you are also recording the sex of the student. Suppose that our survey results in the following information:

Student No. Medium Gender
1 U F
2 U M
3 E M
4 U F
5 E M
6 E F
7 U M
8 E M
: : :
: : :

Now this is a bivariate situation; we have two variables, medium of schooling and sex of the student. In order to summarize the above information, we will construct a table containing a box head and a stub as shown below:

Sex
Med. MALE Female Total
Urdu
English
Total

The top row of this kind of a table is known as the boxhead and the first column of the table is known as stub. Next, we will count the number of students falling in each of the following four categories:

1. Male student coming from an Urdu medium school.
2. Female student coming from an Urdu medium school.
3. Male student coming from an English medium school.
4. Female student coming from an English medium school.
As a result, suppose we obtain the following figures:

Sex
Med. MALE Female Total
Urdu 202 517 719
English 350 131 481
Total 552 648 1200

What we have just accomplished is an example of a bivariate frequency table pertaining to two qualitative variables.

COMPONENT BAR CHAR:

Let us now consider how we will depict the above information diagrammatically. This can be accomplished by constructing the component bar chart (also known as the subdivided bar chart) as shown below:

In the above figure, each bar has been divided into two parts. The first bar represents the total number of male students whereas the second bar represents the total number of female students.

As far as the medium of schooling is concerned, the lower part of each bar represents the students coming from English medium schools. Whereas the upper part of each bar represents the students coming from the Urdu medium schools.The advantage of this kind of a diagram is that we are able to ascertain the situation of both the variables at a glance. We can compare the number of male students in the college with the number of female students, and at the same time we can compare the number of English medium students among the males with the number of English medium students among the females.

MULTIPLE BAR CHART:

The next diagram to be considered is the multiple bar chart. Let us consider an example.

Suppose we have information regarding the imports and exports of Pakistan for the years 1970-71 to 1974-75 as shown in the table below:

Years Imports
(Crores of Rs.) Exports
(Crores of Rs.)
1970-71 370 200
1971-72 350 337
1972-73 840 855
1973-74 1438 1016
1974-75 2092 1029
Source: State Bank of Pakistan


A multiple bar chart is a very useful and effective way of presenting this kind of information. This kind of a chart consists of a set of grouped bars, the lengths of which are proportionate to the values of our variables, and each of which is shaded or coloured differently in order to aid identification.

With reference to the above example, we obtain the multiple bar chart shown below:


Multiple Bar Chart Showing Imports & Exports of Pakistan 1970-71 to 1974-75

This is a very good device for the comparison of two different kinds of information. If, in addition to information regarding imports and exports, we also had information regarding production, we could have compared them from year to year by grouping the three bars together. The question is, what is the basic difference between a component bar chart and a multiple bar chart?

The component bar chart should be used when we have available to us information regarding totals and their components.

For example, the total number of male students out of which some are Urdu medium and some are English medium. The number of Urdu medium male students and the number of English medium male students add up to give us the total number of male students. On the contrary, in the example of exports and imports, the imports and exports do not add up to give us the totality of some one thing!

LECTURE NO 4:

In THIS Lecture, we will discuss the frequency distribution of a continuous variable & the graphical ways of representing data pertaining to a continuous variable i.e. histogram, frequency polygon and frequency curve. You will recall that in Lecture No. 1, it was mentioned that a continuous variable takes values over a continuous interval (e.g. a normal Pakistani adult male’s height may lie anywhere between 5.25 feet and 6.5 feet). Hence, in such a situation, the method of constructing a frequency distribution is somewhat different from the one that was discussed in the last lecture.

EXAMPLE:

Suppose that the Environmental Protection Agency of a developed country performs extensive tests on all new car models in order to determine their mileage rating. Suppose that the following 30 measurements are obtained by conducting such tests on a particular new car model.

There are a few steps in the construction of a frequency distribution for this type of a variable.

CONSTRUCTION OF A FREQUENCY DISTRIBUTION:

Step-1

Identify the smallest and the largest measurements in the data set. In our example:

Smallest value (X0) = 30.1,
Largest Value (Xm) = 44.9,

Step-2

Find the range which is defined as the difference between the largest value and the smallest value. In our example:

Range = Xm – X0
= 44.9 – 30.1
= 14.8

Let us now look at the graphical picture of what we have just computed.

Step-3

Decide on the number of classes into which the data are to be grouped.
(By classes, we mean small sub-intervals of the total interval which, in this example, is 14.8 units long.)There are no hard and fast rules for this purpose. The decision will depend on the size of the data. When the data are sufficiently large, the number of classes is usually taken between 10 and 20.In this example, suppose that we decide to form 5 classes (as there are only 30 observations).

Step-4

Divide the range by the chosen number of classes in order to obtain the approximate value of the class interval i.e. the width of our classes. Class interval is usually denoted by h. Hence, in this example

Class interval = h = 14.8 / 5 = 2.96

Rounding the number 2.96, we obtain 3, and hence we take h = 3. This means that our big interval will be divided into small sub-intervals, each of which will be 3 units long.

Step-5

Decide the lower class limit of the lowest class. Where should we start from?
The answer is that we should start constructing our classes from a number equal to or slightly less than the smallest value in the data. In this example,
smallest value = 30.1 So we may choose the lower class limit of the lowest class to be 30.0.

Step-6

Determine the lower class limits of the successive classes by adding h = 3 successively. Hence, we obtain the following table:

Step-7

Determine the upper class limit of every class. The upper class limit of the highest class should cover the largest value in the data. It should be noted that the upper class limits will also have a difference of h between them. Hence, we obtain the upper class limits that are visible in the third column of the following table. Hence we obtain the following classes:

The question arises: why did we not write 33 instead of 32.9?Why did we not write 36 instead of 35.9? and so on. The reason is that if we wrote 30 to 33 and then 33 to 36, we would have trouble when tallying our data into these classes. Where should I put the value 33? Should I put it in the first class, or should I put it in the second class? By writing 30.0 to 32.9 and 33.0 to 35.9, we avoid this problem. And the point to be noted is that the class interval is still 3, and not 2.9 as it appears to be. This point will be better understood when we discuss the concept of class boundaries … which will come a little later in today’s lecture.

Step-8

After forming the classes, distribute the data into the appropriate classes and find the frequency of each class. This is a simple example of the frequency distribution of a continuous or, in other words, measurable variable.

CLASS BOUNDARIES:

The true class limits of a class are known as its class boundaries. In this example:

It should be noted that the difference between the upper class boundary and the lower class boundary of any class is equal to the class interval h = 3.
32.95 minus 29.95 is equal to 3, 35.95 minus 32.95 is equal to 3, and so on.
A key point in this entire discussion is that the class boundaries should be taken up to one decimal place more than the given data. In this way, the possibility of an observation falling exactly on the boundary is avoided. (The observed value will either be greater than or less than a particular boundary and hence will conveniently fall in its appropriate class).Next, we consider the concept of the relative frequency distribution and the percentage frequency distribution. Next, we consider the concept of the relative frequency distribution and the percentage frequency distribution.

This concept has already been discussed when we considered the frequency distribution of a discrete variable. Dividing each frequency of a frequency distribution by the total number of observations, we obtain the relative frequency distribution. Multiplying each relative frequency by 100, we obtain the percentage of frequency distribution. In this way, we obtain the relative frequencies and the percentage frequencies shown below

The term ‘relative frequencies’ simply means that we are considering the frequencies of the various classes relative to the total number of observations. The advantage of constructing a relative frequency distribution is that comparison is possible between two sets of data having similar classes. For example, suppose that the Environment Protection Agency perform tests on two car models A and B, and obtains the frequency distributions shown below:

In order to be able to compare the performance of the two car models, we construct the relative frequency distributions in the percentage form:

From the table it is clear that whereas 6.7% of the cars of model A fall in the mileage group 42.0 to 44.9, as many as 16% of the cars of model B fall in this group. Other comparisons can similarly be made. Let us now turn to the visual representation of a continuous frequency distribution. In this context, we will discuss three different types of graphs i.e. the histogram, the frequency polygon, and the frequency curve.

HISTOGRAM:

A histogram consists of a set of adjacent rectangles whose bases are marked off by class boundaries along the X-axis, and whose heights are proportional to the frequencies associated with the respective classes. It will be recalled that, in the last lecture, we were considering the mileage ratings of the cars that had been inspected by the Environment Protection Agency.

Our frequency table came out as shown below:

In accordance with the procedure that I just mentioned, we need to take the class boundaries along the X axis We obtain

Now, as seen in the frequency table, the frequency of the first class is 2. As such, we will draw a rectangle of height equal to 2 units and obtain the following figure:

The frequency of the second class is 4. Hence we draw a rectangle of height equal to 4 units against the second class, and thus obtain the following situation:

The frequency of the third class is 14. Hence we draw a rectangle of height equal to 14 units against the third class, and thus obtain the following picture:

Continuing in this fashion, we obtain the following attractive diagram:

This diagram is known as the histogram, and it gives an indication of the overall pattern of our frequency distribution.

FREQUENCY POLYGON:

A frequency polygon is obtained by plotting the class frequencies against the mid-points of the classes, and connecting the points so obtained by straight line segments. In our example of the EPA mileage ratings, the classes are

These mid-points are denoted by X.

Now let us add two classes to my frequency table, one class in the very beginning, and one class at the very end.

The frequency of each of these two classes is 0, as in our data set, no value falls in these classes.

Now, in order to construct the frequency polygon, the mid-points of the classes are taken along the X-axis and the frequencies along the Y-axis, as shown below:

Next, we plot points on our graph paper according to the frequencies of the various classes, and join the points so obtained by straight line segments.
In this way, we obtain the following frequency polygon:

It is well-known that the term ‘polygon’ implies a many-sided closed figure. As such, we want our frequency polygon to be a closed figure. This is exactly the reason why we added two classes to our table, each having zero frequency. Because of the frequency being zero, the line segment touches the X-axis both at the beginning and at the end, and our figure becomes a closed figure. Had we not carried out this step, our graph would have been as follows:

And since this graph is touching the X-axis, hence it cannot be called a frequency polygon (because it is not a closed figure)!

FREQUENCY CURVE:
When the frequency polygon is smoothed, we obtain what may be called the frequency curve.




Be Praying
Obaid Khan

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@ Obaid Khan

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Dear All

this is the second post about statistics......

FREQUENCY POLYGON:
A frequency polygon is obtained by plotting the class frequencies against the mid-points of the classes, and connecting the points so obtained by straight line segments.
In our example of the EPA mileage ratings, the classes were:


And our frequency polygon came out to be:




Also, it was mentioned that, when the frequency polygon is smoothed, we obtain what may be called the FREQUENCY CURVE.
In our example:




In the above figure, the dotted line represents the frequency curve. It should be noted that it is not necessary that our frequency curve must touch all the points. The purpose of the frequency curve is simply to display the overall pattern of the distribution. Hence we draw the curve by the free-hand method, and hence it does not have to touch all the plotted points. It should be realized that the frequency curve is actually a theoretical concept.
If the class interval of a histogram is made very small, and the number of classes is very large, the rectangles of the histogram will be narrow as shown below:



The smaller the class interval and the larger the number of classes, the narrower the rectangles will be. In this way, the histogram approaches a smooth curve as shown below:


In spite of the fact that the frequency curve is a theoretical concept, it is useful in analyzing real-world problems. The reason is that very close approximations to theoretical curves are often generated in the real world so close that it is quite valid to utilize the properties of various types of mathematical curves in order to aid analysis of the real-world problem at hand.

VARIOUS TYPES OF FREQUENCY CURVES:
• the symmetrical frequency curve
• the moderately skewed frequency curve
• the extremely skewed frequency curve
• the U-shaped frequency curve
Let us discuss them one by one.

First of all, the symmetrical frequency curve is of the following shape:


If we place a vertical mirror in the centre of this graph, the left hand side will be the mirror image of the right hand side.


Next, we consider the moderately skewed frequency curve. We have the positively skewed curve and the negatively skewed curve. The positively skewed curve is that one whose right tail is longer than its left tail, as shown below


On the other hand, the negatively skewed frequency curve is the one for which the left tail is longer than the right tail.


Both of these that we have just consider are moderately positively and negatively skewed.
Sometimes, we have the extreme case when we obtain the EXTREMELY skewed frequency curve. An extremely negatively skewed curve is of the type shown below:



This is the case when the maximum frequency occurs at the end of the frequency table.
For example, if we think of the death rates of adult males of various age groups starting from age 20 and going up to age 79 years, we might obtain something like this:

This will result in a J-shaped distribution similar to the one shown above.
Similarly, the extremely positively skewed distribution is known as the REVERSE J-shaped distribution.



A relatively LESS frequently encountered frequency distribution is the U-shaped distribution.


If we consider the example of the death rates not for only the adult population but for the population of ALL the age groups, we will obtain the U-shaped distribution.
Out of all these curves, the MOST frequently encountered frequency distribution is the moderately skewed frequency distribution. There are thousands of natural and social phenomena which yield the moderately skewed frequency distribution. Suppose that we walk into a school and collect data of the weights, heights, marks, shoulder-lengths, finger-lengths or any other such variable pertaining to the children of any one class.
If we construct a frequency distribution of this data, and draw its histogram and its frequency curve, we will find that our data will generate a moderately skewed distribution. Until now, we have discussed the various possible shapes of the frequency distribution of a continuous variable.
Similar shapes are possible for the frequency distribution of a discrete variable.







Let us now consider another aspect of the frequency distribution i.e.
CUMULATIVE FREQUENCY DISTRIBUTION.
As in the case of the frequency distribution of a discrete variable, if we start adding the frequencies of our frequency table column-wise, we obtain the column of cumulative frequencies.
In our example, we obtain the cumulative frequencies shown below:



In the above table, 2+4 gives 6, 6+14 gives 20, and so on.
The question arises: “What is the purpose of making this column?”
You will recall that, when we were discussing the frequency distribution of a discrete variable, any particular cumulative frequency meant that we were counting the number of observations starting from the very first value of X and going up to THAT particular value of X against which that particular cumulative frequency was falling.
In case of a the distribution of a continuous variable, each of these cumulative frequencies represents the total frequency of a frequency distribution from the lower class boundary of the lowest class to the UPPER class boundary of THAT class whose cumulative frequency we are considering.
In the above table, the total number of cars showing mileage less than 35.95 miles per gallon is 6, the total number of car showing mileage less than 41.95 miles per gallon is 28, etc.

Such a cumulative frequency distribution is called a “less than” type of a cumulative frequency distribution. The graph of a cumulative frequency distribution is called a
CUMULATIVE FREQUENCY POLYGON or OGIVE.
A “less than” type ogive is obtained by marking off the upper class boundaries of the various classes along the X-axis and the cumulative frequencies along the y-axis, as shown below:














The cumulative frequencies are plotted on the graph paper against the upper class boundaries, and the points so obtained are joined by means of straight line segments.
Hence we obtain the cumulative frequency polygon shown below:



It should be noted that this graph is touching the X-Axis on the left-hand side. This is achieved by ADDING a class having zero frequency in the beginning of our frequency distribution, as shown below:
















Since the frequency of the first class is zero, hence the cumulative frequency of the first class will also be zero, and hence, automatically, the cumulative frequency polygon will touch the X-Axis from the left hand side. If we want our cumulative frequency polygon to be closed from the right-hand side also , we can achieve this by connecting the last point on our graph paper with the X-axis by means of a vertical line, as shown below:




In the example of EPA mileage ratings, all the data-values were correct to one decimal place.
Let us now consider another example:
















































Source: “Pizza,” Copyright 1997 by Consumers Union of United States, Inc., Yonkers, N.Y. 10703.






In order to construct the frequency distribution of the above data, the first thing to note is that, in this example, all our data values are correct to two decimal places. As such, we should construct the class limits correct to TWO decimal places, and the class boundaries correct to three decimal places.
As in the last example, first of all, let us find the maximum and the minimum values in our data, and compute the RANGE.
Minimum value X0 = 0.52
Maximum value Xm = 1.90
Hence:
Range = 1.90 - 0.52
= 1.38


Lower limit of the first class = 0.51
Hence, our successive class limits come out to be:

Stretching the class limits to the left and to the right, we obtain class boundaries as shown below:






By tallying the data-values in the appropriate classes, we will obtain a frequency distribution similar to the one that we obtained in the examples of the EPA mileage ratings.
By constructing the histogram of this data-set, we will be able to decide whether our distribution is symmetric, positively skewed or negatively skewed. This may please be attempted as an exercise.



Keep Praying..............

Regards.

Obaid Khan

Last edited by Princess Royal; Friday, February 26, 2010 at 05:42 PM.
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