Virtual University of Pakistan Lecture No. 1 Statistics and Probability Miss Saleha Naghmi Habibullah
To inculcate in you an attitude of Statistical and Probabilistic thinking.
To give you some very basic techniques in order to apply Statistical analysis to real-world situations/problems.
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
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 days, in the developed countries of the world, there is an active movement for of Statistical Literacy .
A lot of application in a wide variety of
Agriculture, Anthropology, Astronomy,
Biology, Economics, Engineering,
Environment, Geology, Genetics, Medicine,
Physics, Psychology, Sociology, Zoology ….
Virtually every single subject from
Anthropology to Zoology …. A to Z!
THE NATURE OF DISCIPLINE DESCRIPTIVE STATISTICS STATISTICS INFERENTIAL STATISTICS
Text and Reference Material The primary text-book for the course is Introduction to Statistical Theory (Sixth Edition) by Sher Muhammad Chaudhry and Shahid Kamal published by Ilmi Kitab Khana, Lahore. Reference books for the course are: 1. “ “ by Afzal Beg & Miraj Din Mirza. 2. “ “ by Mohammad Rauf Chaudhry (Polymer Publications, Urdu Bazar, Lahore). 3. “Statistics” by James T. McClave & Frank H. Dietrich, II (Dellen Publishing Company, California, U.S.A). 4. “Introducing Statistics” by K.A. Yeomans (Penguin Books Ltd., England). 5. “Applied Statistics” by K.A. Yeomans (Penguin Books Ltd., England). 6. “Business Statistics for Management & Economics” by Wayne W. Daniel and James C. Terrell (Houghton Mifflin Company, U.S.A.). 7. “Basic Business Statistics” by Berenson & Levine ( )
ORGANIZATION OF THIS COURSE IN ACCORDANCE WITH THE ABOVE-MENTIONED STRUCTURE, THE ORGANIZATION OF THIS COURSE IS AS FOLLOWS: FINAL EXAM 11 TO 15 INFERENTIAL STATISTICS 31 TO 45 11 TO 15 MID-TERM-II 6 TO 10 PROBABILITY 16 TO 30 6 TO 10 MID-TERM-I 1 TO 5 DESCRIPTIVE STATISTICS 1 TO 15 1 TO 5 EXAMS HOME- WORK ASSIGN-MENTS AREA TO BE COVERED LEC-TURES WEEKS
Upon completion of the first segment, you will be able to:
Appreciate the nature of statistical data .
Understand various methods of collecting statistical data.
Appreciate the importance of a proper sampling procedure.
Utilize various methods of summarizing and describing collected data.
Employ statistical techniques to understand the nature of relationship between two quantitative variables.
Upon completion of the second segment, you will be able to:
Understand the basic concepts of probability theory (which is the foundation of statistical inference). Understand the concept of discrete probability distributions and their mathematical properties.
Understand the concept of continuous probability distributions and their mathematical properties.
Get acquainted with some of the most commonly encountered and important discrete and continuous probability distributions such as the binomial and the normal distribution.
Upon completion of the third segment, you will be able to: Understand and employ various techniques of e stimation and hypothesis-testing in order to draw reliable conclusions necessary for decision-making in various fields of human activity. Through this segment, you will be able to appreciate the purpose and the goal of the subject of Statistics.
GRADING There will be two term exams and one final exam. In addition, there will be 15 homework assignments. The final examination will be comprehensive in nature. (Approximately 25-30% of the final exam paper will be on the course covered upto the Mid-Term-II Exam.) These will contribute the following percentages to the final grade: Mid-Term-I: 20% Mid-Term-II: 20% Final Exam: 30% Homework Assignments: 30%
Meaning of Statistics Statistics Meanings STATUS Political State Information useful for the State
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 DATA
Data are collected in many aspects of everyday life.
Statements given to a police officer or physician or
psychologist during an interview are data.
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.
EXAMPLES OF DATA
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.
Types of Data Data Quantitative (Numeric) Qualitative (Non - Numeric)
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. Variable: 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 & 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 Variable Discrete Variable Counting e.g. No. of sisters Gaps, Jumps
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, that 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.
MEASUREMENT SCALES (Cont.)
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).
MEASUREMENT SCALES (Cont.) 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 Celcius 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. are 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.
Chemical and manufacturing plants
sometimes discharge toxic-waste materials
such as DDT into nearby rivers and streams
These toxins can adversely affect the plants
and animals inhabiting the river and the river
A study of fish was conducted in the Tennessee River in Alabama and its three tributary creeks: Flint creek, Limestone creek and Spring creek. A total of 144 fish were captured, and the following variable measured for each one:
River/Creek from where fish was captured
Species of fish (Channel fish, Largemouth bass or smallmouth buffalo fish)
Length of fish (Centimeters)
Weight of fish (grams)
DDT concentration in the bodily system of the fish (parts per million)
Classify each of the five variables measured
as quantitative or qualitative .
Also, identify the types of measurement
scales for each of the five variables.
The variables Length, weight and DDT
concentration are quantitative variables
because each is measured on a nominal
scale (Length is centimeters, Weight is
grams and DDT in parts per million).
All three of these variables are being
measured on the Ratio Scale.
Whenever we speak about the weight of an
object, obviously, if our measuring instrument
reads ‘zero’, this means that the object being
measured has zero weight --- and, in this sense,
the ‘zero’ would be a true zero.
An exactly similar argument holds for the length of
As far as DDT concentration in the bodily
system of the fish is concerned, obviously, if
there is absolutely no DDT in the fish, then
the DDT concentration reads zero --- and,
this particular ‘zero’ reading will be true zero.
As, explained above, the three variables length of fish, weight of fish and DDT concentration in the bodily system of the fish are quantitative variables measures on the ratio scale.
Data on River/Creek from which the fish
were captured, and the species of fish are
Both of these variables are measured on
The river/creek from which the fish
were captured, and the species of fish are
qualitative data because these can not be
measured quantitatively, they can only be
classified into categories.
(i.e. Channel fish, Largemouth bass or
smallmouth buffalo fish for the species and Tennessee
River, Flint creek, Limestone creek and Spring
The Statistical methods for describing, reporting and analyzing data depend on the type of data measured (i.e. whether data are quantitative or qualitative).
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.
Errors of Measurements Errors of Measurements Biased Errors Cumulative Errors Systematic Errors Random Errors Compensating Errors Accidental Errors
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 .
A Statistical Inference in an estimate or
prediction or some other generalization
about a population based on information
contained in sample.
That is, we use information contained in
sample to learn about the larger population.
Population and Sample
The collection of all individuals, items or
data under consideration in a statistical
That part of the population from which
information is collected.
Population and Sample Population Sample
Five Elements of an Inferencial Statistical Problem:
One or more variables of interest
A measure of Reliability
In order of understand the concept of
Reliability, a very important point to be
understood is that making an inference
about population from the sample is only
part of the story.
We also need to know its reliability --- that is,
how good our inference is.
Measure of Reliability
A measure of reliability is a statement
(usually quantified) about the degree of
uncertainty associated with a statistical
The point to be noted is that the only way we can be certain that an inference about population is correct is to include the entire population in our sample.
However, because of resource constraints, (i.e. Insufficient time and/ or money). We usually can not work with whole population, so we base our inference on just a portion of population (i.e. Sample)
Consequently, whenever possible, it is
important to determine and report the
reliability of each inference made.
As such, reliability is the fifth element of
statistical inferencial problems.
A large paint retailer has had numerous complaints from customers about under-filled paint cans.
As, a result retailer has begun inspecting incoming shipments of paint from suppliers.
Shipments with under-filled problems will be sent back to supplier.
A recent shipment contained 2,440 gallon-size cans.
The retailer sampled 50 cans and weighted each on a scale capable of measuring weight to four decimal places.
Properly filled cans weigh 10 pounds.
Describe a population
Describe a variable of interest
Describe a sample
Describe the Inference
Describe a measure of uncertainty of our inference.
The population is the set of units of interests to the retailer, which is the shipment of 2,440 cans of paint.
The weight of paint cans is the variable, the retailer wishes to evaluate.
The sample is the subset of population. In this case, it is the 50 cans of paint selected by the retailer.
The inference of interest involves the
generalization of the information contained in
the sample of paint cans to the population of
In particular, Retailer wants to learn about
the content of under-filled problem (if any)
In the population.
This might be accomplished by finding the
average weight of the cans in the sample,
and using it to estimate the average weight
of the cans of population.
As far as the measure of reliability of our
inference is concerned, the point to be
noted is that, using statistical methods,
we can determine a bound on the
Bound on the Estimation Error
This bound is simply a number that our
estimation error (i.e. the difference between
the average weight of sample and average
weight of population of cans) is not likely to
This bound is a measure of the uncertainty
of our inference, or, in other words, the
reliability of statistical inference.
The crux of the matter is that an inference is
incomplete without a measure of its reliability
When the weights of 50 paint cans are used
to estimate the average weight of all the
cans, the estimate will not exactly mirror the
If the sample of 50 cans yields a mean
weight of 9 pounds, it does not follow (nor is
it likely) that the mean weight of population
of can is also exactly 9 pounds.
Nevertheless, we can use sound statistical
reasoning to ensure that our sampling
procedure will generate estimate that is
almost certainly within a specified limit of the
true mean weight of all the cans.
For example such reasoning might assure us that
the estimate of the population from the sample is
almost certainly within 1 pound of the actual
The implication is that the actual mean weight of
the entire population of the cans is between
9 – 1=8 pounds and 9 +1=10 pounds --- that is,
(9 ± 1 ) pounds.
This interval represents the a measure of reliability
for the inference.
IN TODAY’S LECTURE, YOU LEARNT:
The nature of the science of Statistics
The importance of Statistics in various fields
Some technical concepts such as
The meaning of “ data ”
Various types of variables
Various types of measurement scales
The concept of errors of measurement
IN THE NEXT LECTURE, YOU WILL LEARN:
Concept of sampling
Random verses non-random sampling
Simple random sampling
A brief introduction to other types of random sampling
Methods of data collection
In other words, you will begin your journey in a subject with reference to which it has been said that “statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”.