METODE STATISTIKA n Kode Matakuliah STK 211 32

METODE STATISTIKA n Kode Matakuliah: STK 211, 3(2 -3) n 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 Bahasan Minggu 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. 2. 3. 4. Sukandar D. 2007. Metode Statistika Sukandar D. 2007. Mengolah Data dengan SAS Sukandar D. 2007. Mengolah Data dengan SPSS Koopmans, L. H. 1987. Introduction to Contemporary Statistical Methods 2 nd ed. Duxbury, Press. Boston.

Evaluasi UTS 35 % ; Mencontek, bekerjasama n UAS 35 % berbuat curang diberi n Tugas 30 % nilai 0 n Kriteria Penilaian : n A: 80 -100; B: 65 -7; C: 55 -64 ; D: 45 -54 E: 0 -44; BL: Belum lengkap n

PENDAHULUAN n Apa itu statistika? ¨ Statistika berasal dari kata statistik penduga parameter ¨ Ilmu yang mempelajari dan mengusahakan agar data menjadi informasi yang bermakna

Statistika Populasi Sampling Pendugaan Contoh Deskriptif Statistika Deskriptif vs Statistika Inferensia Tingkat Keyakinan Ilmu Peluang

Langkah-langkah Analisis Statistika Studying 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 n n 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.

¨ n 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.

n 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 n Statistical data analysis divides the methods for analyzing data into two categories: ¨ exploratory methods n ¨ 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 n 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 n n 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 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: Statistik Deskriptif Analisis statistika yang bertujuan untuk menyajikan (tabel dan grafik) dan meringkas (ukuran pemusatan dan penyebaran) data sehingga data menjadi informasi yang mudah dipahami.

Ilustrasi Stem-and-Leaf Display: Volume Stem-and-leaf of Volume N = 31 Leaf 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 n Perbandingan Rataan Populasi ¨ Satu populasi Uji t atau uji z ¨ Dua populasi Uji t atau uji z ¨ Lebih dari dua populasi anova n Hubungan antar variabel ¨ Hubungan Regresi dua arah Analisis Korelasi satu arah (sebab akibat) Analisis

Ilustrasi Hubungan antar peubah Analisis Korelasi & Regresi Linier

Ilustrasi Hubungan antar peubah Correlations: x 1, x 2, Y 1 x 2 Y 1 x 1 -0. 016 0. 948 x 2 0. 891 0. 000 0. 391 0. 088 Regression Analysis: Y 1 versus x 1, x 2 The regression equation is Y 1 = 2. 20 + 2. 46 x 1 + 0. 565 x 2 Predictor Constant x 1 x 2 Coef 2. 200 2. 4621 0. 56531 S = 1. 02180 SE Coef 1. 416 0. 1353 0. 06884 R-Sq = 95. 9% T 1. 55 18. 19 8. 21 P 0. 139 0. 000 R-Sq(adj) = 95. 4% Analysis of Variance Source Regression Residual Error Total DF 2 17 19 SS 411. 21 17. 75 428. 96 MS 205. 61 1. 04 F 196. 93 P 0. 000

Ilustrasi Hubungan antar peubah Analisis Regresi Logistik Binary Logistic Regression: Y 2 versus x 1, x 2 Link Function: Logit Response Information Variable Y 2 Value 1 0 Total Count 12 8 20 (Event) Logistic Regression Table Predictor Constant x 1 x 2 Coef 3. 87448 -0. 516801 0. 396576 SE Coef 3. 38365 0. 357665 0. 211489 Z 1. 15 -1. 44 1. 88 P 0. 252 0. 148 0. 061 Odds Ratio 0. 60 1. 49 95% CI Lower Upper 0. 30 0. 98 1. 20 2. 25

Log-Likelihood = -10. 017 Test that all slopes are zero: G = 6. 886, DF = 2, P -Value = 0. 032 Goodness-of-Fit Tests Method Pearson Deviance Hosmer-Lemeshow Chi-Square 21. 7994 20. 0347 14. 8216 DF 17 17 8 P 0. 193 0. 272 0. 063

Analisis Data Lanjutan Analisis Multivariate n Manova n Analisis Komponen Utama n Analisis Faktor n Analisis Cluster n Analisis Diskriminan n Analisis Korelasi Kanonik n Analisis Biplot

Analisis data time series n n n 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 (Auto. Regressive Integrated Moving Average) Fungsi Transfer (Bivariate ARIMA) MARIMA (Multivariate ARIMA)

Pola Data Time Series

Ilustrasi: Forecasting dengan Metode Smoothing Moving Average Formula:

Ilustrasi: Forecasting dengan Metode Smoothing Eksponensial n Bentuk umum:

Ilustrasi Metode Winter (Kasus data musiman) Xt = b 1+b 2 t + ct + t Xt = (b 1+b 2 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. n 1. Apa populasi dari masalah ini? n 2. Data apa saja yang perlu anda kumpulkan? n 3. Bagaimana anda mengumpulkan data ? n 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 soal 60 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