ANALISIS DATA
SMT 310
retnosubekti@uny.ac.id
Motivasi
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Memahami analisis eksplorasi dan konfirmasi
Landasan statistika deskriptif dan inferensi
Bersinergi dengan komputasi statistik untuk mengupgrade kemampuan analisis data
Deskripsi
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Penyusunan dan rangkuman data numerik
Penyajian data univariat
Transformasi data
Sampel acak
Statistika konfirmasi
Analisis variansi
Hubungan antara dua variabel
Analisis data kategorik
Referensi
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Erickson, Bonnie H & Nosanchuk. 1987. Memahami
Data : Statistika untuk Ilmu Sosial. (terjemahan RK.
Sembiring & Manase Malo). Jakarta: LP3ES
Griffiths D., Stirling W.D, Weldon K.L . 1998.
Understanding Data : Principles and Practice of
Statistics. Brisbane : John Willey & Sons
Kontrak
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Penilaian
Bobot :
Tugas
Kuis
Usip
Uas
: 20%
: 15%
: 25%
: 40%
REVIEW
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STATISTIKA ?
STATISTIK ?
STATISTIKA DESKRIPTIF ?
Statistika inferensi
Populasi
Sampel
Parameter
Statistik
Data
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Nilai ujian metode stastistik 20 orang mahasiswa
adalah :
91
50
73
74
55
86
70
43
47
80
40
85
64
61
58
95
52
67
83
92
Misalkan diketahui nilai ujian komputasi statistika 50
mahasiswa
44,7
59,8
67,1
57,1
58,2
69,5
60,6
44,2
76
51,2
48,4
63,9
67,8
56,2
60
68,2
48,5
46
72,6
52
42,5
57,2
70,2
57
62,2
70,3
50
76,8
74
65,1
49,1
64,7
74,6
63,6
63
72,2
75,3
75
55,4
67,7
43,1
76,5
68,7
59,9
63,5
72,6
77
73,5
56,3
77,3
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Banyaknya penjualan HP di suatu toko :
Merek HP
Penjualan
Nokia
56
SE
45
Samsung
32
LG
22
Lain
45
Skala pengukuran
Nominal :
 Ordinal
:
 Interval
:
 Rasio
:
Contoh:
 Nominal: jenis pekerjaan, warna
 Ordinal: kepangkatan, tingkat pendidikan
 Interval: tahun kalender (Masehi, Hijriyah), temperatur
 (Celcius, Fahrenheit)
 Rasio: berat, panjang, isi

Statistika deskriptif
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Metode atau cara-cara yang digunakan untuk
meringkas dan menyajikan data dalam bentuk
tabel, grafik atau ringkasan numerik data.
Grafik Stem-and-leaf
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Untuk menunjukkan bentuk distribusi data
Data berupa angka dengan minimal dua digit
Contoh (Data penghasilan buruh):
439
511555689
602334445556777889
7122344558
8349
92
Stem= 10, Leaf = 1
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Intro…
Why study statistics?
Make decision without complete informations
Understanding population, sample
Parameter, statistic
Descriptive and inferential statistics
glossary
A population is the collection of all items of interest or under
investigation
N represents the population size
A sample is an observed subset of the population
n represents the sample size
A parameter is a specific characteristic of a population
Mean, Variance, Standard Deviation, Proportion, etc.
A statistic is a specific characteristic of a sample
Mean, Variance, Standard Deviation, Proportion, etc.
Population vs. Sample
Population
a b
Sample
cd
b
ef gh i jk l m n
gi
o p q rs t u v w
x y
z
c
o
n
r
u
y
Values calculated using
population data are called
parameters
Values computed from sample
data are called statistics
Examples of Populations
Incomes of all families living in yogyakarta
All women with pregnancy problem.
Grade point averages of all the students in your
university
…
Random sampling
Simple random sampling is a procedure in which
each member of the population is chosen strictly by
chance,
each member of the population is equally likely to
be chosen, and
every possible sample of n objects is equally likely
to be chosen
The resulting sample is called a random sample
Descriptive and Inferential Statistics
Two branches of statistics:
Descriptive statistics
Collecting, summarizing, and processing data to
transform data into information
Inferential statistics
Provide the bases for predictions, forecasts, and
estimates that are used to transform information into
knowledge and decision
Descriptive Statistics
Collect data
e.g., Survey
Present data
e.g., Tables and graphs
Summarize data
e.g., Sample mean =
X
n
i
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 pounds
Inference is the process of drawing conclusions or making decisions about a
population based on sample results
The Decision Making Process
Decision
Knowledge
Experience, Theory,
Literature, Inferential
Statistics, Computers
Information
Descriptive Statistics,
Probability, Computers
Begin Here:
Identify the
Problem
Data