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FP531 STATISTICAL ANALYSCHAPTER 1 BASIC STATISTIC
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CLO 1
Apply general understanding on the orgaand preparation of raw data for statistica
analysis by using different types of proba
distributions to solve problems. (C3)
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CHAPTER 1 :
BASIC STATISTIC1.1 Understand statistics.
1.1.1 Define statistics.
1.1.2 State the types of statistics:a. descriptive
b. inferential
1.1.3 Differentiate population and sample.
1.1.4 Describe the types of variables used:a. quantitative
b. qualitative or categorical1.1.5 Determine the different scales of measurement:
a. nominal
b. ordinal
c. interval
d. ratio
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1.2 Organize data.
1.2.1 State examples of raw data.
1.2.2 Organize qualitative data:a. frequency distributions
b. relative frequency and percentage distributio
c. draw graphs and charts to represent data
1.2.3 Organize quantitative data:a. frequency distributions
b. relative frequency and percentage distributio
c. draw graphs and charts to represent data
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1.3 Illustrate numerical descriptive measures.
1.3.1 Illustrate Measures of Central Tendency for ungdata:
a. meanb. median
c. mode
1.3.2 Illustrate Measures of Dispersion for ungroupeda. range
b. variance and standard deviationc. coefficient of variation
1.3.3 Illustrate Measures of Central Tendency and Disfor grouped data.
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1.4 Understand probability.
1.4.1 Describe experiment, outcomes and sample
1.4.2 Calculate probability:
a. mutually exclusive events
b. independent and dependent events
c. complimentary events
d. intersection of events
e. multiplication rule
f. union of events
g. addition rule
h. Bayes theorem
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Introduction Statisticsis the science of conducting stud
tocollect,
organize,
summarize,
analyze, and
draw conclusions from data.
Bluman Chapter 1
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Discrete & Continuous varia
Discrete variable can only take individually separvalues which usually occur through the process ocounting, and not any value in between two give
For example, number of children in a family couldvalues such as 0, 1, 2, 3 etc. and thus is a discrete
Continuous variable can take any value between given values, limited only by the precision of themeasurement. For example, time taken to complcould be quoted as 5 seconds or 5.17 seconds or seconds, thus is a continuous variable.
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Descriptive and Inferential Stati
Descriptive statisticsconsists of the collection, organizatsummarization, and presentation of data. Inferential statisticsconsists of generalizing from sample
populations, performing estimations and hypothesis testdetermining relationships among variables, and makingpredictions.
Descriptive statistics deals with scientific methods of dea large mass of data that have been collected without dany conclusion or inference about a large group.
Inferential statistics deals with scientific methods of findsomething about a population, based on a sample.
Bluman Chapter 1
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Descriptive Statistics
Collect data
e.g., Survey
Present data
e.g., Tables and graphs
Summarize data
e.g., Sample mean = iXn
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Inferential Statistics
Estimation
e.g., Estimate the population
mean weight using the sample
mean weight
Hypothesis testing
e.g., Test the claim that the
population mean weight is 120
poundsInference is the process of drawing conclusions or making decisi
populationbased on sampleresults
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Descriptive and Inferential Statis
A variableis a characteristic or attribute tcan assume different values.
The values that a variable can assume aredata.
A populationconsists of all subjects (humotherwise) that are studied.
A sampleis a subset of the population.
Bluman Chapter 1
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Key Definitions A populationis the collection of all items of interest or un
investigation
N represents the population size
A sampleis an observed subset of the population
n represents the sample size
A parameteris a specific characteristic of a population
A statisticis a specific characteristic of a sample
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Population vs. Sample
a b c d
ef gh i jk l m n
o p q rs t u v w
x y z
Population Sample
b c
g i n
o r uy
Values calculated using
population data are called
parameters
Values computed f
data are called sta
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Examples of Populations
Names of all registered voters in the Ma
Incomes of all families living in Penang
Annual returns of all stocks traded on t
Lumpur Stock Exchange
Grade point averages of all the student
Polytechnic
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Random Sampling
Simple random samplingis a procedure in
each member of the population is chosen strictly by c
each member of the population is equally likely to be
every possible sample of n objects is equally likely to
The resulting sample is called a random sa
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Kata Kunci
1. Populasi (seluruh alam) semua
2. Sampel Sebahagian daripada populasi
3. Parameter Ringkasan tentang Populasi
4. Statistik Ringkasan tentang Sampel
Populasi, samParameter d
Statistik
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Variables and Types of Data
Bluman Chapter 1
Data
QualitativeCategorical
QuantitativeNumerical,
Can be ranked
DiscreteCountable
5, 29, 8000, etc.
ContinuoCan be decima
2.59, 312.1, et
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Qualitative and Quantitative var
A qualitative variable is a characteristic thitem has or does not have.
A quantitative variable is a characteristic
item whose values can be expressed as
numerical quantities. For example, the he
a group of students is a quantitative varia
each student will have a measurable heig
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Types of DataData sets can consist of two types of data: qualit
data andquantitative data.Data
Qualitative
Data
Quantitative
Data
Consists of attributes,
labels, or nonnumerical
entries.
Consists of
numerical
measurements o
counts.
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Levels of MeasurementThe level of measurement determines which sta
calculations are meaningful. The four levels ofmeasurement are: nominal,ordinal,interval,an
Levels of
Measurement
Nominal
OrdinalInterval
Ratio
Lowe
high
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Nominal Level of MeasuremData at the nominallevel of measurement are
qualitative only.
Levels of
Measurement
NominalCalculated using names, labels
qualities. No mathematical
computations can be made at t
Colors in
the US flag
Names of students
in your class
Textbook
using this
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Ordinal Level of MeasuremeData at the ordinallevel of measurement are qu
or quantitative.Levels of
MeasurementArranged in order, but differen
between data entries are not
meaningful.
Class standings:
freshman,
sophomore, junior,
senior
Numbers on the back
of each players shirt
Ordinal
Top 50 son
on the
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Interval Level of MeasuremData at the intervallevel of measurement are
quantitative. A zero entry simply represents a pa scale; the entry is not an inherent zero.
Levels of
MeasurementArranged in order, the differen
entries can be calculated.
Temperatures Years on a timeline
Interval
Atlanta Bra
Series v
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Ratio Level of MeasuremeData at the ratiolevel of measurement are simi
interval level, but a zero entry is meaningful.
Levels of
Measurement
A ratio of two data values can be
data value can be expressed as a
Ages Grade point averages
Ratio
Wei
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Summary of Levels of Measurem
NoNoYesNominal
NoYesYesOrdinal
YesYesYesInterval
YesYesYesRatio
Determ
valueSubtract datavalues
Arrange
data inorder
Put data incategoriesLevel ofmeasurement
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Taraf Pengukuran Data
Nominal Taraf terendah pengukuran
Ordinal
Interval
Ratio Taraf tertinggi pengukuran
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Taraf data Nominal Angka yang diguna untuk mengkelas atau
mengkategoriContoh: Kelasifikasi Pekerjaan
1 untuk guru
2 untuk pekerja binaan
3 untuk pekerja perkilangan
Contoh: Ethnik 1 untuk Melayu
2 untuk Cina
3 untuk India
4 untuk lain-lain
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Taraf Data Ordinal
Angka yang diguna untuk pemeringkatan atau susunan Magnitud angka relatif adalah bermakna Perbezaan di antara angka tidak boleh dibanding
Contoh: Pemeringkatan produktiviti pekerjaContoh: Pemeringkatan ujian rasa untuk tiga jenis minuman rContoh: Kedudukan dalam organisasi
1 untuk Presiden 2 untuk Timbalan President 3 untuk Pengurus Kilang 4 untuk Penyelia Jabatan 5 untuk Pekerja
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Contoh Pengukuran Ordin
f
i
n
i
s
h
1
2
3
4
5
6
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Cara pengajaran pensyarah amat baik
1 2 3 4
StronglyAgree
Agree DisagreeNeutral
Data Ordinal
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Data Taraf Interval
Jarak antara dua integer adalah sama Magnitud angka relatif adalah bermakna
Perbezaan antara dua nombor boleh dibandin
Kedudukan origin, sifar, adalah arbitrari
Pintasan menegak unti pengukuran fungsitransformasi adalah tidak sifar
Contoh: Fahrenheit Temperature
Contoh: Calendar Time
Contoh: Monetary Units
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Data Taraf Ratio Pengukuran taraf tertinggi
Magnitud angka relatif adalah bermakna Perbezaan antara dua nombor boleh dibandingka
Kedudukan origin, sifar, adalah mutlak (natural)
Pintasan menegak unti pengukuran fungsi transfosifar
Contoh: Tinggi, berat dan volumContoh: Monetary Variables, such as Profit and Loss,
and Expenses
Contoh: Financial ratios, such as P/E Ratio, Inventory Tand Quick Ratio.
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Potensi Kegunaan berbagai Taraf
NominalOrdinalInterval
Ratio
Taraf Data, Operasi,
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Taraf Data, Operasi,
dan Kaedah StatistikTaraf Data Operasi Kaedah S
Nominal Pengelasan dan
Menghitung
Tidak Berpa
Ordinal Semua diatas dan
Pemeringkatan
Tidak berpa
Interval Semua diatas danmenambah, menolak,
mendharab dan
membahagi
Berparam
Ratio Semua diatas Berparam