introduction to statistic
TRANSCRIPT
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CHAPTER 1
INTRODUCTION
OBJECTIVES
After completing this chapter, students should be able to:
1. Describe the difference between descriptive andinferential statistics.
2. Identify and interpret the relationships between sampleand population, and statistics and parameter.
3. Identify and describe the different types of variables.
4. Identify and describe the different types of data.
5. Differentiate and identify the techniques of datacollection.
6. Identify and interpret the measurement scales.
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What is Statistics?
The word statistics derives from classical Latin roots,
status which means state.
Statistics has become the universal language of the
sciences.
As potential users of statistics, we need to master both
the science and the art of using statistical
methodology correctly.
These method include:
Carefully defining the situation
Gathering data
Accurately summarizing the data
Deriving and communicating meaningful conclusions
Specific definition:
Statistics is a collection of procedures and principles for
gathering data and analyzing information to help people
make decisions when faced with uncertainty.
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Nowadays statistics is used in almost all fields of human
effort such as:
education
health business agriculture..etc.
Example applications of Statistics
1. Sport=> A statistician may keeps records of the number of hits
a baseball player gets in a season.
2. Financial=> Financial advisor uses several statistic information to
make reliable predictions in investment.
3. Public Health=> An administrator would be concerned with the
number of residents who contract a new strain of flu
virus during a certain year.
4. Others
=>
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Statistics has Two Aspects
1) Theoretical / Mathematical Statistics
2) Applied Statistics
1) Theoretical / Mathematical Statistics
=> Deals with the development, derivation and proof of
statistical theorems, formulas, rules and laws.
2) Applied Statistics=> Involves the applications of those theorems,
formulas, rules and laws to solve real world problems.
** Applied Statistics can be divided into two main areas,
depending on how data are used.
(1) Descriptive statistics (2) Inferential statistics
What most people think of whenthey hear the wordstatistics
Includes the collection, presentation,and description of sample data.
Using graphs, charts and tables toshow data.
Refers to the technique of
interpreting the valuesresulting from the descriptive
techniques and making
decisions and drawingconclusions about the
o ulation
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Example 1Determine which of the following statements is descriptive
in nature and which is inferential.
a. Of all U.S kindergarten teachers, 32% say that knowing
the alphabet is an essential skill.Inferential
b. Of the 800 U.S kindergarten teachers polled, 32% say
that knowing the alphabet is an essential skill.
descriptive
ASPECTS OF STATISTICS
Theoretical/MathematicalStatistics Applied Statistics
Inferential Statistics
Deals with the development, derivationand proof of statistical theorems,
formulas, rules and laws.
Descriptive Statistics
Involves the applications of thosetheorems, formulas, rules and
laws to solve real world problems.
Consist of method for collecting,organizing, displaying and
summarizing data
Consist of methods that use resultsobtained from sample to make decisions
or conclusions about a population
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Why do we have to study statistics?
To read and understand various statistical studies in
related field.
To communicate and explain the results of study in
related field using our own words.
To become better consumers and citizens.
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Basic Terms of Statistics
1. Population versus Sample
Population=> a collection of all individuals about which
information is desired.
-individuals are usually people but could also be schools,
cities, pet dogs, agriculture fields, etc.
=> there are two kinds of population:
i. When the membership of a population can be (orcould be) physically listed.
- finite population:- e.g. the books in library.
ii. When the membership is unlimited.- infinite population:- e.g. the population of all people
who might use aspirin.
Sample=> a subset of the population.
2. Parameter versus Statistic
Parameter
=> a numerical value summarizing all the data of an entirepopulation.
- often a Greek letter is used to symbolize the name of
parameter.
e.g. the average age at time of admission for all students
who have ever attended our college.
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Statistics
=> a numerical value summarizing the sample data.
- english alphabet is used to symbolize the name of statistic
e.g. the average height, found by using the set of 25heights.
3. Variable
=>a characteristics of interest about each individual element
of a population or sample.
e.g. a students age at entrance into college, the color of
students hair, etc.
4. Data value
=> the value of variable associated with one element of a
population or sample. This value may be a number, a word,
or a symbol.
e.g. Farah entered college at age 23, her hair is brown,etc.
5. Data
=> the set of values collected from the variable from each of
the elements that belong to sample.
e.g. the set of 25 heights collected from 25 students.
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Example 2
A statistics student is interested in finding out something
about the average ringgit value of cars owned by the faculty
members of our university. Each of the seven terms just
describe can be identified in this situation.
i) population: the collection of all cars owned by all faculty
members at our university.
ii) sample: any subset of that population. For example, the
cars owned by members the statistics department.
iii) variable: the ringgit value of each individual car.
iv) data value: one data value is the ringgit value of a
particular car. Alis car, for example, is value at RM 45
000.
v) data: the set of values that correspond to the sample
obtained (45,000; 55,000; 34,0000;).
vi) parameter: which we are seeking information is the
average value of all cars in the population.
vii) statistic: will be found is the average value of the cars
in the sample.
Census: a survey includes every element in the population.
Sample survey: a survey includes every element in selected
sample only.
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Types of Variables
1. Quantitative (numerical) Variables
A variable that quantifies an element of a population.
- e.g. the total cost of textbooks purchased by eachstudent for this semesters classes.
Arithmetic operations such as addition and averaging
are meaningful for data that result from a quantitative
variable.
Can be subdivided into two classification: discrete
variables and continuous variables.
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Discrete Variables
A quantitative variable that can assume a countable
number of values.
Can assume any values corresponding to isolated
points along a line interval. That is, there is a gap
between any two values.
Example 3
Number of courses for which you are currently registered.
Continuous Variables
A quantitative variable that can assume an
uncountable number of values.
Can assume any value along a line interval, including
every possible value between any two values.
Example 4
Weight of books and supplies you are carrying as you
attend class today.
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2. Qualitative (attribute, categorical) variables
A variable that describes or categorizes an element of
a population.
Example 5
A sample of four hair-salon customers was surveyed for
their hair color, hometown and level of
satisfaction.
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Exercise 1
1. Of the adult U.S. population, 36% has an allergy. A
sample of 1200 randomly selected adults resulted in
33.2% reporting an allergy.
a. Describe the population.b. What is sample?c. Describe the variable.d. Identify the statistics and give its value.e. Identify the parameter and give its value.
2. The faculty members at Universiti Utara Malaysia were
surveyed on the question How satisfied were you with
this semester schedule? Their responses were to be
categorized as very satisfied, somewhat satisfied,
neither satisfied nor dissatisfied, somewhat
dissatisfied, or very dissatisfied.
a. Name the variable interest.b. Identify the type of variable.
3. A study was conducted by Aventis Pharmaceuticals Inc.
to measure the adverse side effects of Allegra, a drug
used for treatment of seasonal allergies. A sample of 679
allergy sufferers in the United States was given 60 mg of
the drug twice a day. The patients were to report whether
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they experienced relief from their allergies as well as any
adverse side effects (viral infection, nausea, drowsiness,
etc)
a. What is the population being studied?b. What is the sample?c. What are the characteristics of interest about each
element in the population?
d. Are the data being collected qualitative orquantitative?
4. Identify each of the following as an example of (1)
attribute (qualitative) or (2) numerical (quantitative)
variables.
a. The breaking strength of a given type of stringb. The hair color of children auditioning for the musical
Annie.
c. The number of stop signs in town of less than 500people.
d. Whether or not a faucet is defective.e. The number of questions answered correctly on a
standardized test.
f. The length of time required to answer a telephonecall at a certain real estate office.
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DATA
The set of values collected from the variable from each
of the elements that belong to sample.
e.g. the set of 25 heights collected from 25 students.
From a survey or an experiment.
Two types of data:
Primary data:
necessary data obtainedthrough survey
conducted by researcher
Secondary data:
data obtained from
published material by
governmental, industrial
or individual sources
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1. PRIMARY DATA
Primary Data Collection Techniques
Data is collected by researcher
Data is obtained from respondent
(i) Face to face interview
- Two ways communication.
- Researcher(s) asks question directly to
respondent(s).
Advantages:
Precise answer.
Appropriate for research that requires huge datacollection.
Increase the number of answered questions.
Disadvantages:
Expensive.
Interviewer might influence respondents responses.
Respondent refuse to answer sensitive or personalquestion.
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(ii) Telephone interview
Advantages:
Quick.
Less costly.
Wider respondent coverage.
Disadvantages:
Information obtained might not represent the
whole population.
Limited interview duration.
Not appropriate for long and contemplate
question.
Demonstration cannot be performing.
Telephone is not answered.
(iii) Postal questionnaire
- A set of questions to obtain related informationof conducted study.
- Questionnaires are posted to every respondent.
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Advantages:
Wider respondent coverage.
Respondent have enough time to answer
questions.
Interviewer influences can be avoided.
Lower cost.
Disadvantages:
One way interaction.
Low response rate.
Not suitable for numerous and hard questions.
Time consuming.
Questionnaire is answered by unqualifiedrespondent.
(iv) Observation
Observing and measuring specific
characteristics without attempting to modify the
subjects being studied.
Records human behaviors, objects and situations
without contact with respondent.
- not commonly used.
- precise information.
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2. SECONDARY DATA
- Published records from governmental, industrial orindividual sources.
- Historical data.
- Various resources.
- Experiment is not required.
Advantages:
Lower cost. Save time and energy.
Disadvantages:
Obsolete information.
Data accuracy is not confirmed.
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Data also can be classified by how they are categorized,
counted or measured.
This type of classification uses measurement scales with
4 common types of scales: nominal, ordinal, interval
andratio.
Nominal Level of Measurement
A qualitative variable that characterizes (or
describes/names) an element of a population.
Arithmetic operations not meaningful for data.
Order cannot be assigned to the categories.
Example: - Survey responses:- yes, no, undecided,
- Gender:- male, female
Ordinal Level of Measurement
A qualitative variable that incorporates and orderedposition, or ranking.
Differences between data values either cannot be
determined or are meaningless.
Example: - Level of satisfaction:- very satisfied,satisfied, somewhat satisfied, etc.
- Course grades:- A, B, C, D, or F
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Interval Level of Measurement
Involve a quantitative variable.
A scale where distances between data are meaningful.
Differences make sense, but ratios do not (e.g., 30-
20=20-10, but 20/10 is not twice as hot!).
No natural zero
Example:
- Temperature scales are interval data with 25oC warmer
than 20oC and a 5oC difference has some physical
meaning. Note that 0oC is arbitrary, so that it does not
make sense to say that 20oC is twice as hot as 10oC.
- The year 0 is arbitrary and it is not sensible to say that
the year 2000 is twice as old as the year 1000.
Ratio Level of Measurement
A scale in which both intervals between values and ratios
of values are meaningful.
A real zero point.
Example:
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- Temperature measured in degrees Kelvin is a ratio
scale because we know a meaningful zero point (absolute
zero).
- Physical measurements of height, weight, length are
typically ratio variables. It is now meaningful to say that
10 m is twice as long as 5 m. This is because there is a
natural zero.
Levels of Measurement
Nominal - categories only
Ordinal - categories with some order
Interval - differences but no natural starting point
Ratio - differences and a natural starting point
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Exercise 2
1) Classify each as nominal-level, ordinal-level, interval-
level or ratio-level.
2) Data obtained from a nominal scale
a. must be alphabetic.
b. can be either numeric or nonnumeric.
c. must be numeric.
d. must rank order the data.
3) The set of measurements collected for a particular
element is (are) called
a. variables.
b. observations.c. samples.
d. none of the above answers is correct.
a. Ratings of newscasts in Malaysia.
(poor, fair, good, excellent)
b. Temperature of automatic popcorn poppers.
c. Marital status of respondents to a survey on
saving accounts.
d. Age of students enrolled in a marital arts course.
e. Salaries of cashiers of C-Mart stores.
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4) The scale of measurement that is simply a label for the
purpose of identifying the attribute of an element is the
a. ratio scale.
b. nominal scale.
c. ordinal scale.d. interval scale.
5) Some hotels ask their guests to rate the hotels services
as excellent, very good, good, and poor. This is an
example of the
a. ordinal scale.
b. ratio scale.c. nominal scale.
d. interval scale.
6) The ratio scale of measurement has the properties of
a. only the ordinal scale.
b. only the nominal scale.
c. the rank scale.
d. the interval scale.
7) Arithmetic operations are inappropriate for
a. the ratio scale.
b. the interval scale.
c. both the ratio and interval scales.
d. the nominal scale.
8) A characteristic of interest for the elements is called a(n)
a. sample.
b. data set.
c. variable.
d. none of the above answers is correct.
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9) In a questionnaire, respondents are asked to mark their
gender as male or female. Gender is an example of a
a. qualitative variable.
b. quantitative variable.
c. qualitative or quantitative variable, depending onhow the respondents answered the question.
d. none of the above answers is correct.
10) The summaries of data, which may be tabular, graphical,
or numerical, are referred to as
a. inferential statistics.
b. descriptive statistics.c. statistical inference.
d. report generation.
11) Statistical inference
a. refers to the process of drawing inferences about the
sample based on the characteristics of the population.
b. is the same as descriptive statistics.
c. is the process of drawing inferences about the
population based on the information taken from the
sample.
d. is the same as a census.
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Answer Exercise 1
1) a. all adults of U.S. population
b. 1200 randomly selected from adultsc. allergy
d. 33.2% effected by allergy
e. 36.0% has an allergy
2) a. satisfaction
b. ordinal
3) a. all allergy sufferers in the U.S.
b. 679 allergy sufferers in the U.S.
c. to measure the adverse side effects of allergy
d. qualitative
4) a. quantitative
b. qualitative
c. quantitative
d. qualitative
e. quantitative
f. quantitative
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Answer Exercise 2
1) a. Ordinal b. Interval c. Nominal
a. Ratio e. ratio
2) b 3) c
4) b 5) a
6) d 7) d
8) c 9) a
10) b 11) c