1.0 Basic Statistic 1.1

8/21/2019 1.0 Basic Statistic 1.1

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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

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1.0 Basic Statistic 1.1