METODE STATISTIKA
description
Transcript of METODE STATISTIKA
METODE STATISTIKA
Kode Matakuliah: STK211, 3(2-3)
Tujuan Instruksional Umum:
Setelah mengikuti mata kuliah ini selama satu semester, mahasiswa akan dapat menjelaskan prinsip-prinsip dasar metode statistika, dan mampu mengerjakan beberapa analisis statistika sederhana.
Pokok BahasanMinggu Ke Pokok Bahasan
I Pendahuluan
II Deskripsi Data
III Konsep Dasar Peluang
IV-V Konsep Peubah Acak dan Sebaran Peluang Acak
VI Sebaran Penarikan Contoh
VII Ujian Tengah Semester
VIII-IX Pendugaan Parameter
X-XI Pengujian Hipotesis
XII Analisis Korelasi dan Regresi Linear Sederhana
XIII Analisis Data Kategori
XIV Topik Khusus I
XV Topik Khusus II
XVI Ujian Akhir Semester
Kepustakaan
1. Sukandar D. 2007. Metode Statistika2. Sukandar D. 2007. Mengolah Data dengan
SAS3. Sukandar D. 2007. Mengolah Data dengan
SPSS4. Koopmans, L.H. 1987. Introduction to
Contemporary Statistical Methods 2nd ed. Duxbury, Press. Boston.
Evaluasi
UTS 35 % ; Mencontek, bekerjasama UAS 35 % berbuat curang diberi Tugas 30 % nilai 0 Kriteria Penilaian : A:80-100; B:65-7; C:55-64 ; D:45-54
E: 0-44; BL: Belum lengkap
PENDAHULUAN
Apa itu statistika? Statistika berasal dari kata statistik
penduga parameter Ilmu yang mempelajari dan mengusahakan
agar data menjadi informasi yang bermakna
Statistika Populasi
Contoh
Sampling Pendugaan
Tingkat Keyakinan
Ilmu PeluangStatistika Deskriptif
vs Statistika Inferensia
Deskriptif
Langkah-langkah Analisis StatistikaStudying a problem through the use of statistical data analysis usually involves four basic steps.
Defining the problem Collecting the data Analyzing the data Reporting the results
Defining the Problem
An exact definition of the problem is imperative in order to obtain accurate data about it.
It is extremely difficult to gather data without a clear definition of the problem.
Collecting the Data
Designing ways to collect data is an important job in statistical data analysis.
Two important aspects of a statistical study are: Population - a set of all the elements of interest in a study Sample - a subset of the population Statistical inference is refer to extending your knowledge obtain
from a random sample from a population to the whole population.
The purpose of statistical inference is to obtain information about a population form information contained in a sample. It is just not feasible to test the entire population, so a sample is the only realistic way to obtain data because of the time and cost constraints.
Data can be either quantitative or qualitative. Qualitative data are labels or names used to identify an attribute of each element. Quantitative data are always numeric and indicate either how much or how many.
Data can be collected from existing sources or obtained through observation and experimental studies designed to obtain new data. In an experimental study, the variable of interest is identified.
Then one or more factors in the study are controlled so that data can be obtained about how the factors influence the variables.
In observational studies, no attempt is made to control or influence the variables of interest. A survey is perhaps the most common type of observational study.
Analyzing the Data
Statistical data analysis divides the methods for analyzing data into two categories: exploratory methods
Exploratory methods are used to discover what the data seems to be saying by using simple arithmetic and easy-to-draw pictures to summarize data
confirmatory methods Confirmatory methods use ideas from probability theory in the
attempt to answer specific questions. Probability is important in decision making because it provides a mechanism for measuring, expressing, and analyzing the uncertainties associated with future events.
Reporting the Results
Through inferences, an estimate or test claims about the characteristics of a population can be obtained from a sample.
The results may be reported in the form of a table, a graph or a set of percentages. Because only a small collection (sample) has been examined and not an entire population, the reported results must reflect the uncertainty through the use of probability statements and intervals of values.
To conclude, a critical aspect of managing any organization is planning for the future. Statistical data analysis helps us to forecast and predict future aspects of a business operation.
The most successful leader and decision makers are the ones who can understand the information and use it effectively.
Perkembangan Analisis Statistika
Statistik DeskriptifAnalisis statistika yang bertujuan untuk menyajikan (tabel dan grafik) dan meringkas (ukuran pemusatan dan penyebaran) data sehingga data menjadi informasi yang mudah dipahami.
Analisis statistika telah banyak digunakan pada berbagai bidang. Analisis statistika yang digunakan mulai dari analisis statistika yang paling sederhana (statistika deksriptif) sampai analisis statistika lanjutan
Beberapa ilustrasi analisis statistika:
Ilustrasi
Diameter
20
15
10
807060
Height
80
70
60
201510
70
45
20
Volume
704520
Matrix Plot of Diameter, Height, Volume
Volu
me
80
70
60
50
40
30
20
10
Boxplot of Volume
Volume
Frequency
806040200
14
12
10
8
6
4
2
0
Mean 30.17StDev 16.44N 31
Histogram of VolumeNormal
Stem-and-Leaf Display: Volume
Stem-and-leaf of Volume N = 31Leaf Unit = 1.0
10 1 0005688999(9) 2 111224457 12 3 13468 7 4 2 6 5 11558 1 6 1 7 7
Statistika Inferensia Perbandingan Rataan Populasi
Satu populasi Uji t atau uji z Dua populasi Uji t atau uji z Lebih dari dua populasi anova
Hubungan antar variabel Hubungan dua arah Analisis Korelasi Hubungan satu arah (sebab akibat) Analisis
Regresi
Ilustrasi Hubungan antar peubahAnalisis Korelasi & Regresi Linier
x1
12
10
8
1050
x2
10
5
0
12108
35
30
25
Y1
353025
Matrix Plot of x1, x2, Y1
Ilustrasi Hubungan antar peubah
Correlations: x1, x2, Y1
x1 x2x2 -0.016 0.948
Y1 0.891 0.391 0.000 0.088
Regression Analysis: Y1 versus x1, x2
The regression equation isY1 = 2.20 + 2.46 x1 + 0.565 x2
Predictor Coef SE Coef T PConstant 2.200 1.416 1.55 0.139x1 2.4621 0.1353 18.19 0.000x2 0.56531 0.06884 8.21 0.000
S = 1.02180 R-Sq = 95.9% R-Sq(adj) = 95.4%
Analysis of Variance
Source DF SS MS F PRegression 2 411.21 205.61 196.93 0.000Residual Error 17 17.75 1.04Total 19 428.96
Fitted Value
Resi
dual
38363432302826242220
1
0
-1
-2
-3
Residuals Versus the Fitted Values(response is Y1)
Residual
Perc
ent
210-1-2-3
99
95
90
80
70
605040
30
20
10
5
1
Normal Probability Plot of the Residuals(response is Y1)
Ilustrasi Hubungan antar peubahAnalisis Regresi LogistikBinary Logistic Regression: Y2 versus x1, x2
Link Function: LogitResponse Information
Variable Value CountY2 1 12 (Event) 0 8 Total 20
Logistic Regression Table
Odds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant 3.87448 3.38365 1.15 0.252x1 -0.516801 0.357665 -1.44 0.148 0.60 0.30 1.20x2 0.396576 0.211489 1.88 0.061 1.49 0.98 2.25
Log-Likelihood = -10.017Test that all slopes are zero: G = 6.886, DF = 2, P-Value = 0.032
Goodness-of-Fit Tests
Method Chi-Square DF PPearson 21.7994 17 0.193Deviance 20.0347 17 0.272Hosmer-Lemeshow 14.8216 8 0.063
Analisis Data Lanjutan
Analisis Multivariate Manova Analisis Komponen Utama Analisis Faktor Analisis Cluster Analisis Diskriminan Analisis Korelasi Kanonik Analisis Biplot
Analisis data time series
Data time series merupakan data yang dikumpulkan secara sequensial menurut periode waktu tertentu.
Peranan ramalan (forecasting) data ke depan memegang peranan penting dalam menyusun kebijakan strategis perusahaan/lembaga
Metode Forecasting yang berkembang saat ini, antara lain: Metode Rataan Kumulatif Metode Pemulusan (Smoothing) ARIMA (AutoRegressive Integrated Moving Average) Fungsi Transfer (Bivariate ARIMA) MARIMA (Multivariate ARIMA)
Pola Data Time Series
Ilustrasi: Forecasting dengan Metode Smoothing Moving Average
Formula: N
XXMM NTT
TT
)(1
Bentuk umum:
Ilustrasi: Forecasting dengan Metode Smoothing Eksponensial
ttt FXF )1(1
Ilustrasi Metode WinterIlustrasi Metode Winter(Kasus data musiman)(Kasus data musiman)
Index
x
454035302520151051
1400
1200
1000
800
600
400
200
0
Time Series Plot of x
Index
x
454035302520151051
1400
1200
1000
800
600
400
200
0
Smoothing ConstantsAlpha (level) 0.2Gamma (trend) 0.2Delta (seasonal) 0.2
Accuracy MeasuresMAPE 60MAD 267MSD 101122
VariableActualSmoothed
Winters' Method Plot for xAdditive Method
Xt = b1+b2 t + ct + t Xt = (b1+b2 t) ct + t
SEKIAN DAN TERIMA KASIH
PRAKTIKUM 1
Masalah:Saat ini harga pangan mahal, daya beli rendah dan media masa sering memberikan berita gizi buruk bayi dan balita di wilayah Indonesia. Agar masalah ekonomi dan gizi lebih jelas, anda diminta mengumpulkan data berkaitan dengan hal tersebut.
1. Apa populasi dari masalah ini? 2. Data apa saja yang perlu anda kumpulkan? 3. Bagaimana anda mengumpulkan data ? 4. Apa yang akan anda laporkan berdasarkan data yang anda kumpulkan
Dikerjakan oleh kelompok yang terdiri atas 5 atau 6 mahasiswa 30 menit pertama kelompok menjawab soal60 menit penyajian hasil dan diskusi 4 kelompok terpilih @ 15 menit per kelompok (1 IKK dipilih secara acak dari kelompok IKK terbentuk dan 3 kelompok GM dipilih
secara acak dari kelompok GM terbentuk)10 menit terakhir memperbaiki jawaban lalu dikumpulkan ke dosen/asisten