Silabus-S2-STATISTIKA 2009-2014

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FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM FACULTY OF MATHEMATICS AND NATURAL SCIENCE Program Studi Department

STATISTIKA Statistics

Jenjang Pendidikan Programme

Magister Sains [Master of Science]

Kompetensi Lulusan

� [diisi maksimum 5 kompetensi lulusan yang utama dan ditulis dengan bullet] � Mampu melakukan penelitian pengembangan

dan penerapan statistika yang berkontribusi pada penyelesaian masalah riil di masyarakat

Graduate Competence

� [ditulis terjemahan bahasa inggris dengan cetak miring] � Able to do development research in Statistics methods and to

applly Statistics that have contribution to solve on real problem

STRUKTUR KURIKULUM/COURSE STRUCTURE

No. Kode MK Code

Nama Mata Kuliah (MK) Course Title

Sks Credits

SEMESTER I 1 SS09 2301 Teori Probabilitas

Probability Theory 3

2 Mata Kuliah Pilihan 1 Optional Subjects/Course 1

3


3


3

Jumlah sks/Total of credits 12

MATA KULIAH PILIHAN SEMESTER I/ Optional Subjects/Course Semester I 1

SS09 2211 Analisis Statistika Statistical Analysis

3

2 SS09 2212 Desain Eksperimen

Design of Experiment 3

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3 SS09 2213 Model Linear

Linear Models

3

4 SS09 2221 Riset Operasi

Operation Research

3

5 SS09 2222 PPIC

Product Planing an Inventory Control

3

6 SS09 2223 Stat. Pros. Control

Statistical Process control 3

7 SS09 2231 Teknik Simulasi

Simulation Technique

3

8 SS09 2232 Metode Resampling

Resampling methods

3

9 SS09 2241 Studi Kependudukan

Demographic Study

3

10 SS09 2242 Riset Pemasaran

Marketing Research

3

11 SS09 2243 Statistik Ofisial

Official Statistics

3

SEMESTER II

1 SS09 2302 Statistik Inferensi Inference Statistics

3


3


3


3

Jumlah sks/Total of credits 12 MATA KULIAH PILIHAN SEMESTER II/ Optional Subjects/Course Semester II

1. SS09 2214 Analisis Multivariat Multivariate Analysis

3

2. SS09 2215 An. Data Kualitatif Qualitative data Analysis

3

3. SS09 2216 Statistik Spasial Spatial Statistics

3

4. SS09 2217 Reg. Nonparametrik Nonparametric regression

3

5. SS09 2218 An. Deret Waktu Time Series Analysis

3

6. SS09 2219 Proses Stokastik Stocastic Process

3

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7. SS09 2223 Stat. Pros. Control Statistical Process control

3

8. SS09 2224 Teori Antrian Queueing Theory

3

9. SS09 2225 Peranc. Kualitas Quality design

3

10. SS09 2226 Analisis Realibilitas Reliability Analysis

3

11. SS09 2233 Analisis Bayesian Bayesian Analysis

3

12. SS09 2234 Neural Network Neural Network

3

13. SS09 2235 Data Mining Data Mining

3

14. SS09 2243 Statistik Ofisial Official Statistics

3

15. SS09 2244 Ekonometrika Econometrics

3

16. SS09 2245 Aktuaria Actuaria

3

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SEMESTER III 1 SS09 2302 Analisis Data

Data Analysis 3


3

Jumlah sks/Total of credits 6 MATA KULIAH PILIHAN SEMESTER III/ Optional Subjects/Course Semester III

1. SS09 2216 Statistik Spasial Spatial Statistics

3

2. SS09 2217 Reg. Nonparametrik Nonparametric regression

3

3. SS09 2218 An. Deret Waktu Time Series Analysis

3

4. SS09 2219 Proses Stokastik Stocastic Process

3

5. SS09 2225 Peranc. Kualitas Quality design

3

6. SS09 2226 Analisis Realibilitas Reliability Analysis

3

7. SS09 2233 Analisis Bayesian Analisis Bayesian

3

8. SS09 2234 Neural Network Neural Network

3

9. SS09 2235 Data Mining Data Mining

3

10. SS09 2236

Stat. Komp. Intensif Intensive Computational statistics

3

11. SS09 2244 Ekonometrika Econometrics

3

12. SS09 2245 Aktuaria Actuaria

3

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SILABUS KURIKULUM/COURSE SYLLABUS

MATA KULIAH/ COURSE TITLE

SS09 2301: Teori Probabilitas SS09 2301: Probability Theory Credits: tiga/three Semester: I

TUJUAN PEMBELAJARAN/ LEARNING OBJECTIVES

Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]

KOMPETENSI/ COMPETENCY

� Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random

� [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]

POKOK BAHASAN/ SUBJECTS

� Variabel random, ruang probabilitas, fungsi distribusi, ekspektasi dan momen, konvergensi variabel random, fungsi karakteristik, distribusi bersyarat dan kebebasan stokastik, hukum bilangan besar, distribusi khusus, distribusi fungsi variabel random, distribusi limit. Pengantar teori peluang. Transformasi variabel random dan statistik berurut. Fungsi pembangkit momen

� [Random variable, probability space, distribution function, expectation and moment, convergence of random variables, characteristic function, conditional distribution and stochastic independence, Law of Large Numbers, special distribution, distribution of random variable function, limit distribution. Introduction to probability theory. Transformation of random variables and order statistics. Moment generating function]

PUSTAKA UTAMA/ REFERENCES

1. Bartoszynski, R., 1996, Probability and Statistical Inference, John Wiley & Sons, New York.

2. Bhat, B.R., 1981, Modern Probability Theory, John Wiley & Sons, New York.

3. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical Inference; Macmillan Publishing Co., New York .

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SS09 2302: Statistik Inferensi SS09 2302: Inference Statistics Credits: tiga/three Semester: II


Mampu memahami konsep penaksiran, metode penentuan penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan. Keluarga eksponensial, ketidakbiasan, equivariance, uniformly most powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier [Able to understand concept of estimation, methods of finding estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]


� Mampu memahami konsep penaksiran, metode penentuan penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan. Keluarga eksponensial, ketidakbiasan, equivariance, uniformly most powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier

� [Able to understand concept of estimation, methods of finding estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]


� Penaksiran, meliputi penaksiran titik, penaksiran interval. Statistik kecukupan, ketakbiasan, penaksir efisien, penguji hipotesis. UMPT. Uji hipotesis pada sampling distribusi normal. Uji Chi-square, hipotesis linear, dan hipotesis multivariate linier

� Estimation, covers point estimation, interval estimation. Sufficiency, unbiasedness, efficient estimators, hypothesis testing, UMPT, hypothesis testing of sampling normal distribution, Chi-square test, linier hypothesis, and linier multivariate hypothesis


1. Bartoszynski, R., 1996, Probability and Statistical Inference, John Wiley & Sons, New York.

2. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical Inference; Macmillan Publishing Co., New York.

3. Lehman, E.L. 1983, Theory of Point Estimation, John Wiley & Sons: New York.

4. Lehmann, E.L., 1986, Testing Statistical Hypothesis, John Wiley &

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Sons: New York.


SS09 2303: Analisis Data SS09 2303: Data Analysis Credits: tiga/three Semester: III


Mampu memahami penggunaan paket program Statistik, khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode resampling. Mampu membuat suatu laporan ilmiah hasil analisis suatu permasalahan real [Able to understand usage of statistical program packages, especially MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis, qualitative data analysis, nonparametric regression, time series analysis, and resampling method. Able to produce a scientific report based on a real problemsalahan real]


� Mampu memahami penggunaan paket program Statistik, khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode resampling. Mampu membuat suatu laporan ilmiah hasil analisis suatu permasalahan real

� [Able to understand usage of statistical program packages, especially MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis, qualitative data analysis, nonparametric regression, time series analysis, and resampling method. Able to produce a scientific report based on a real problemsalahan real]


� Bahasa pemrograman paket program statistika, yang meliputi telaah terhadap program-program komputer (khususnya MINITAB, SPSS, SAS, dan R) dan penerapan model-model statistika. Studi kasus real dengan penerapan beberapa metode statistik lanjut, yaitu analisis mulivariate, analisis data kualitatif, Generalized Linear Models, regresi nonparametrik, regresi nonlinear (uji nonlinearitas), analisis deret waktu, nonlinear time series, dan resampling methods

� Language of statistical program packages, which covers discussion

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on computer programs (especially MINITAB, SPSS, SAS, and R) and statistical models application. Study of real cases using some advance statistics method application, which are multivariate analysis, qualitative data analysis, Generalized linier models, nonparametric regression, nonlinier regression (nonlinier test), time series analysis, nonlinier time series and resampling method


1. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2006) “Multivariate Data Analysis”, 6th edition, Prentice Hall International: UK.

2. Sharma, S. (1996). “Applied Multivariate Techniques”, New-York: John Wiley & Sons, Inc.

3. Johnson, N. and Wichern, D. (1998). “Applied Multivariate Statistical Analysis”, Prentice-Hall, Englewood Cliffs, N.J

4. McCullagh P. and Nelder, J.A. (1989) Generalized Linear Models. London: Chapman and Hall.

5. Hosmer, D.W. and Lemeshow, S. (2000). Applied Logistic Regression. 2nd Edition, New-York: John Wiley & Sons.

6. Wand, M. P. and Joes, M. C. (1995). Kernel Smoothing. Chapman and Hall, London .

7. Heckman, N. and Ramsay, J. O. (1996). Spline smoothing with model based penalties. McGill University, unpublished manuscript.

8. Shumway, R.H. and Stoffer, D.S. (2006). Time Series Analysis and Its Applications with R Examples. Second edition, Springer: New York, USA.

9. Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods. Second edition, Addison-Wesley Publishing Co., USA.

10. Box, G.E.P, Jenkins, G.E., and Reinsel, H. (1994). Time Series Analysis.

11. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

12. Tong, H. (1994). Nonlinear Time Series. John Wiley & Sons. 13. Manual SAS, SPSS, MINITAB, dan R. 14. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics

with S. Fourth edition. Springer


SS09 2304: Tesis SS09 2304: Thesis Credits: enam/six Semester: IV

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Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial, atau Komputasi, dengan metode statistika terbaru dan membandingkan dengan metode sebelumnya, serta dapat mempublikasikan hasil kajiannya dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal) [Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in a national or international scientific forum (seminar and/or journal)


� Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial, atau Komputasi, dengan metode statistika terbaru dan membandingkan dengan metode sebelumnya, serta dapat mempublikasikan hasil kajiannya dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal)

� [Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in a national or international scientific forum (seminar and/or journal)]


� Kegiatan penelitian mandiri dimulai dari pembuatan proposal penelitian, seminar proposal dan pelaksanaan penelitian. Hasil penelitian harus diseminarkan dan dipertanggungjawabkan dihadapan penguji dalam ujian tesis, serta dipublikasikan dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal)

� [Independent research activities starting from producing a research proposal, proposal seminar, and research implementation. Result of the research should be presented in a seminar and can be accountabled in front of examiners during thesis examination, also should be published in a national or international scientific forum (seminar and/or journal)]


Manual of how to write proposal, thesis and dissertation report based on quality standard of PPS-ITS


SS09 2211: Analisis Statistika SS09 2211: Statistical Analysis Credits: tiga/three Semester: I

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Mampu memahami teori dan metode statistika dasar. Mampu menganalisis hasil metode statisika dasar, dan memberikan interpretasi hasil suatu analisis data dengan metode statistika dasar [Able to understand theory and method of basic statistics. Able to analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]


� Mampu memahami teori dan metode statistika dasar. Mampu menganalisis hasil metode statisika dasar, dan memberikan interpretasi hasil suatu analisis data dengan metode statistika dasar

� [Able to understand theory and method of basic statistics. Able to analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]


� Pengantar Probabilitas. Estimasi parameter, meliputi estimasi titik dan interval. Uji hipotesis tentang rata-rata, proporsi, dan varians pada satu dan dua populasi. Analisis korelasi, regresi sederhana dan berganda. Uji independensi dan analisis nonparametrik dasar

� Introduction to probability. Parameter estimation, covers point and interval estimation. Hypothesis testing of mean, proportion, and varians of one and two populations. Correlation analysis, simple regression and multiregression. Independent test and basic nonparametric analysis


1. Dowdy, S., Weardon, S., and Chilko, D., 2004, Statistics for Research, 3rd Edition, John Wiley & Sons: New York.

2. Lefebvre, M., 2006, Applied Probability and Statistics, Springer Verlag: New York.


SS09 2212: Desain Eksperimen SS09 2212: Design of Experiment Credits: tiga/three Semester: I


Memahami berbagai konsep rancangan percobaan, yang meliputi faktorial design, nested design, fraksional faktorial design, split-plot design, confounding, blok tak lengkap, analisis kovariansi, dan metode Taguchi [Understanding various concepts of experiment design, which covers factorial design, nested design, fractional factorial design, split-plot design, confounding, incomplete blocks, covariance analysis, and

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Taguchi method]


� Memahami berbagai konsep rancangan percobaan, yang meliputi faktorial design, nested design, fraksional faktorial design, split-plot design, confounding, blok tak lengkap, analisis kovariansi, dan metode Taguchi

� [Understanding various concepts of experiment design, which covers factorial design, nested design, fractional factorial design, split-plot design, confounding, incomplete blocks, covariance analysis, and Taguchi method]


� Konsep dasar perancangan percobaan, justifikasi model linier, pengacakan, pengelompokan dan penggunaan pengamatan penyerta. Pembahasan mengenai Faktorial design, Nested design, Fraksional faktorial design, rancangan petak terbagi (split-splot design), pembauran (confounding), analisis kovarians, dan metode Taguchi

� Basic concept of experiment design, linier model justification, randomization, clustering and penyerta observation usage. Discussion on factorial design, Nested design, Fractional factorial design, split-splot design, confounding, covarians analysis, and Taguchi method


1. Hinkelmann, K. and Kemptkarne, O., 1994, Design and Analysis of Experiments, John Wiley & Sons, New York.

2. Bagchi, T., 1994, Taguchi Methods Explained Practical Steps to Robust Design, John Wiley & Sons, New York.

3. Montgomery, D.C., 1997, Design and Analysis of Experiment, John Wiley & Sons, New York.

4. Gardiner, W.P. Gettinby, 1998, Experimental Design Techniques in Statistical Practice : A Practical Software-base approach, Horwood Publishing Limited.


SS09 2213: Model Linier SS09 2213: Linear Model Credits: tiga/three Semester: I

TUJUAN PEMBELAJARAN/ LEARNING

Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model dasar, penggolongan silang, dwi arah, komponen ragam. Mampu mengem-bangkan model-model linier untuk regresi, baik dengan rank penuh ataupun tidak

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OBJECTIVES [Understanding various distributions of quadratic forms, basic model, cross classification, two way, component style. Able to develop linier models for regression, both by full rank or not of full rank]


� Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model dasar, penggolongan silang, dwi arah, komponen ragam. Mampu mengem-bangkan model-model linier untuk regresi, baik dengan rank penuh ataupun tidak

� [Understanding various distributions of quadratic forms, basic model, cross classification, two way, component style. Able to develop linier models for regression, both by full rank or not of full rank]


� Pendugaan dan pengujian hipotesis beberapa model linear. Model klasifikasi satu-arah dan dwi-arah. Perluasan model-model sel rataan. Model dengan peubah penyerta. Model pengaruh-pengaruh campuran dan pendugaan komponen ragam, serta fungsi estimabel

� Estimation and hypothesis test for some linier models. One way and two way classification models. Extension of means cell models. Models with dependent variables. Mixed influence models and variance component estimation and also estimabel functions


1. Bowerman, B.L. and R.T. O’Connel, 1990, Linear Statistical Models an Applied Approach, PWS-KENT Publication Company, Boston.

2. Hocking, R.R., 1996, Methods and Applications of Linear Models Regression and analysis of Variance, John Willey & Sons Inc., New York.

3. Rao, C.R., 1973, Linear Statistical Inference and Its Applications, 2nd Edition, Eastern Private Limited, New Delhi.

4. Searle, S.R., 1987, Linear Models for Unbalanced data, John Wiley & Sons Inc., New York.

5. Myers, R.H. and Milton, J.S., 1991, A First Subjects/Course in the Theory of Linear Statistical Models, PWS-KENT Publication Company, Boston.


SS09 2214: Analisis Multivariat SS09 2214: Multivariate Analysis Credits: tiga/three Semester: II

TUJUAN Mampu membedakan dan menginterpretasikan data univariat, data

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PEMBELAJARAN/ LEARNING OBJECTIVES

multivariat, analisis eksplorasi, pereduksi dimensi, pengujian hipotesis data multivariat, metode multisampel dan analisis diskriminan [Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of hypothesis of multivariate data, multisample method, and discriminant analysis]


� Mampu membedakan dan menginterpretasikan data univariat, data multivariat, analisis eksplorasi, pereduksi dimensi, pengujian hipotesis data multivariat, metode multisampel dan analisis diskriminan

� [Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of hypothesis of multivariate data, multisample method, and discriminant analysis]


� Review tentang aljabar linier, dan fungsi distribusi multivariat, yaitu distribusi Multinormal, Wishart, dan T2 Hotelling. Analisis eksplorasi yang meliputi Biplot, analisis korespondensi, PCA, analisis faktor, analisis cluster, multidimensional scaling dan analisis konjoin. Analisis konfirmasi, terdiri atas pengujian satu mean dan taksiran interval, serta pengujian dua mean dan taksiran interval. MANOVA, meliputi one-way, two-way, dan faktorial diskriminan linier

� Reviewing linier algebra, and function of multivariate distributions which are Multinormal, Wishart, and T2 Hotelling. Exploration analysis which covers Biplot, corespondence analysis, PCA, factor analysis, multidimensional scaling and conjoint analysis. Confirmatory analysis, consists of one mean test and interval estimation. MANOVA, consist of one-way, two-way, and linier discriminant factorial


1. Timm, N.H., 2002, Applied Multivariate Analysis, Springer-Verlag: New York.

2. Rencher, A.C., 2002, Method of Multivariate Analysis, John Wiley & Sons : Canada.

3. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 2006, Multivariate Data Analysis, 6th edition, Prentice Hall International: UK.

4. Sharma, S., 1996, Applied Multivariate Techniques, New-York: John Wiley & Sons, Inc.

5. Dillon, W.K. and Matthew, G., 1984, Multivariate Analysis, Methods and Application, John Wiley & Sons, New York.

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SS09 2215: Analisis Data Kualitatif SS09 2215: Qualitative Data Analysis Credits: tiga/three Semester: II


Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk, rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model logistik regresi, dan model logistik regresi dengan strata [Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk, rxkxl, Log linier model, rxk, rxkxl category tables, logistic regression model, and logistic regression model with stratum]


� Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk, rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model logistik regresi, dan model logistik regresi dengan strata

� [Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk, rxkxl, Log linier model, rxk, rxkxl category tables, logistic regression model, and logistic regression model with stratum]


� Metode-metode analisis tabel kontingensi berdimensi banyak. Metode jumlah kuadrat tertimbang, model log-linier dan pendekatan regresi logistik untuk analisis data kategori. Pendugaan parameter dan besaran asosiasi, pemilihan model, dan pengujian kesesuaian model. Penerapan praktis untuk penyelesaian permasalahan real dengan penggunaan paket komputer statistik, khususnya SPSS dan R

� Some methods of multidimension contingency tables. Weighted sum square method, log-linier model and logistic regression approach for categoric data analysis. Parameter estimation and association value, model selection, and fitting model test. Practical application for solving real problem using statistical computer package, especially SPSS and R


1. Agresti, A., 2002, Categorical Data Analysis, 2nd Edition, John Wiley & Sons: New York.

2. Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W., 2007, Discrete Multivariate Analysis: Theory and Practice, Springer: New York.

3. Greenacre, M.J., 1984, Theory and Applications of Correspondence Analysis, Academic Proses, Inc., New York.

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SS09 2216: Statistik Spasial SS09 2216: Spatial Statistics Credits: tiga/three Semester: III


Memahami konsep dasar data spasial, struktur data spasial, pendugaan dan pemodelan korelasi spasial, prediksi dan interpolasi, mapping pola, regresi spasial dan pemodelan spatio-temporal

[Understanding the basical concept of spatial data, spatial data structure, estimating and modelling spatial correlation, prediction and interpolation, pattern mapping,spatial regression, Spatial-temporal modelling.]



� [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]


Pengertian statistik spasial, Struktur data spasial (titik, area (lattices), dan spasial), isotropic dan stasioner. Pendugaan dan pemodelan korelasi spasial (estimasi variogram, MLE, fitting parametric models). Prediksi dan interpolasi (ordinary kriging, cokriging). Mapping pola titik, Regresi spasial (SAR, GWR) dan neighborhood analysis. Pemodelan spatio-temporal.

[Spatial statistics concepts, Spatial data structure (point, lattices and spatial), isotropic and stationarity. Estimating

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and modelling spatial correlation (variogram estimation, MLE, fitting parametric models). Prediction and interpolation (ordinary kriging, cokriging). Point pattern Mapping, Spatial Regression (SAR, GWR) and neighborhood analysis. Spatio-temporal modelling.]


1. Cressie, N., 1993, Statistics for Spatial Data, John Wiley & Sons.

2. Wackernagel, H., 1995, Multivariate Geostatistics, An Introduction with Applications, Springer-Verlag.

3. Sandra L.A., 1996, Practical handbook of Spatial Statistics. CRC Press. Inc. USA. Isaaks, E.H. and Srivastava, R.H., 1989, Applied Geostatistics, Oxford University Press.

4. Isaaks, E.H. and Srivastava, R.H., 1989, Applied Geostatistics, Oxford University Press


SS09 2217: Regresi Nonparametrik SS09 2217: Nonparametrics Regression Credits: tiga/three Semester: III


Mengetahui beberapa model regresi nonparametrik, khususnya peran dan sifat-sifatnya. Dapat memodelkan perilaku data berdasarkan pendekatan regresi nonparametrik.

[To know and understand various nonparametric regression models, especially the uses and its characteristics. Capable to modelling data behaviours based on nonparametric regression approach.]

KOMPETENSI/ � Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi,

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COMPETENCY ekspektasi, konvergensi variabel random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random



� Konsep dasar regresi nonparametrik dan perbedaan dengan regresi parametrik. Estimasi densitas dengan pendekatan histogram dan kernel. Estimasi kurva regresi nonparametrik dengan pendekatan kernel, deret ortogonal, spline, deret Fourier dan Wavelets. Pemilihan bandwith dalam regresi kernel, dan knot pada regresi spine.

� [Basical concept of nonparametric regression and the differences betwen nonparametric and parmetric regression. Density estimation problems with histogram and kernel approach, orthogonal series, spline, Fourier series and wavelets. Bandwich selection in kernel regression, knot in spline regression]


1. Enbank, R.L., 1988, Spline Smoothing and Nonparametric Regression, Marcel Dekker Ins, New York.

2. Green, P.J. and Silverman, B.W., 1994, Nonparametric Regression and Generalized Linear Models, Chapman and Hall, London.

3. Hardle, W., 1990, Applied Nonparametric Regression, Cambridge University Press, New York.

4. Hardle, W., 1991, Smoothing Techniques With Implementation in S, Spinger Verlag, New York.

5. Prenter, P.M., 1975, Spline and Variational Methods, John Wiley and Sons, New York.

6. Schumaker, L.L., 1981, Spline Functions: Basic Theory, John Wiley and sons, new York.

7. Thompson, J.R. and Tapia, R.A., 1990, Nonparametric Function Estimation, Modelling and Simulations,

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SIAM: Philadelpia. 8. Wahka, G., 1990, Spline Models for Observational

Data, SIAM: Pensylvania.


SS09 2217: Analisis Deret Waktu SS09 2217: Time Series Analysis Credits: tiga/three Semester: III


Memahami konsep-konsep statistika dalam model time series univariat (ARIMA), time series multivariat (Model Intervensi, Fungsi Transfer, dan VARIMA), dan Nonlinear time series. Dapat memodelkan time series univariat, multivariat, dan nonlinear time series.

[To understand the statistical concepts used in univariate time series models (ARIMA), Multivariate time series models (Intervention models, Transfer funtion and VARIMA), non linear time series. Able to model univariate, multivariate and nonlinear time series.]





� Konsep proses stasioner, autokorelasi dan autokorelasi parsial. Regresi dengan error berautokorelasi (regresi time series). Model ARMA, ARIMA, dan Seasonal ARIMA. Model intervensi dan

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deteksi outlier. Fungsi transfer input tunggal dan input ganda. Model GARCH, VARIMA, dan model time series nonlinear.

� [Stationarity concept, autocorrelation and partial autocorrelation, Regression with autocrrelated error (time series regression), ARMA, ARIMA, and Seasonal ARIMA. Intervantion Model and otlier detection. Single and Multiple input transfer function. GARCH, VARIMA and nonlinear time series models.]


1. Brockwell, P.J. and Davis, R.A., 1991, Time Series: Theory and Methods, 2nd Edition, Springer-Verlag: New York.

2. Box, G.E.P., Jenkins, G.M., and Reinsel, D., 1994, Time Series Analysis : Forecasting and Control; 2nd Edition, Holden Day: San Fransisco.

3. Christensen, R., 1991, Linear Models for Multivariate, Time Series and Spatial Data, Springer-Verlag, New York.

4. Priestley, M.B., 1981, Spectral Analysis and Time Series, Academic Press: London.


SS09 2219: Proses Stokastik SS09 2219: Stochastics Process Credits: tiga/three Semester: III


Memahami konsep-konsep probabilitas yang banyak digunakan dalam proses stokastik, rantai markov, proses input-output, perbedaan proses renewal dengan input-output, dan Brownian motion.

[To understand the probability concepts used in stocastic process, markov chain, input-output process, Renewal

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process difference with input-output, Brownian motion.]





� Review probabilitas bersyarat dan hukum probabilitas total. Klasifikasi proses stokastik, rantai Markov, probabilitas transisi, klasifikasi ruang keadaan, dan distribusi seimbang. Proses Poisson, sifat-sifat proses Poisson, dan proses Poisson nonhomogen. Proses input-output (birth-death processes), proses renewal, martingales, random walk, Browman motion, proses difusi, dan penerapannya.

� [Reviewing conditional probability and total probability law. Classification of stochastic process. Markov chain,transition probability, classification of condition space and balanced distribution. Poisson process and its properties. Non homogeny Poisson process. Input-Output process (birth-death processes), renewal process, martingales, random walk, Browman motion, diffusion process ant its application.]


1. Heyman, D.D. and Sobel, M.J., 1996, Stochastic Models in Operations Research, Vol. 1, McGrraw-Hill, New York.

2. Kulkarni, V.G., 1998, Modeling, Analysis, Design, and Control of Stochastic System, Springer.

3. Lawler, G.F., 2006, Introduction to Stochastic Process, Chapman and Hall.

4. Puderma, N.L., 2005, Markov decision Process:

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Discrete Stochastic Dynamic Programming, John Wiley & Sons.

5. Ross, S.N., 1996, Stochastic Processes, John Wiley & Sons, New York.

6. Rolsky, T., Schmidt, H., Schmidt, V., and Tengels, J., 1999, Stochastic Process for Insurance and Finance, John Wiley & Sons.

7. Lyuer, Y.D., 2002, Financial Engineering and Computation, Cambridge Univ. Press.


SS09 2221: Riset Operasi SS09 2221: Operation Research Credits: tiga/three Semester: I


Memahami berbagai metode kuantitatif dalam riset operasi dan memiliki ketrampilan menerapkannya dalam dunia praktis.

[To understand miscelanous Quantitative methodsin Operation research and have an ability to apply it on pratice.].




POKOK BAHASAN/

� Analisis Jaringan, Ruang lingkup riset operasi, program linier: formulasi masalah, metode simplek primal, dual, revisi, analisis pascal optimal, sensitifitas, tafsiran

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SUBJECTS ekonomik. Program bilangan bulat: teknik penca-bangan dan pembatasan, program bilangan campuran, program bilangan biner. Program dinamik: deterministik, probabilistik. Goal programing: single dan multiple. Teori permainan: strategi murni, campuran. Sistem antrian: antrian non poisson, antrian dengan disiplin prioritas, antrian dua phase. Program Nonlinear.

� [Network Analysis, Coverage of operation Research, Linear programming: problems formulation, Prime Simlex Methods, Dual, Revition, Pascal Opyimum analysis, sensitivity, economic’s interpretation. Integer Number’s program: Boundary and Branchery problems, mixed numbers program, binary numbers program. Dynamic program: deterministic and probabilistic. Goal programing: single dan multiple. Game Theory: Pure Strategy, mixed. Queueing System: Non Poisson Queueing, Queueing with Disipline Priority, Two Phase queuein. Nonlinar programming.]


8. Hiller, F. and Lieberman, G.J., 1990, Introduction to Operation Research, 5th edition, McGrraw-Hill, New York.

9. Taha, H.A., 1973, Operation Research: An Introduction, Prentice Hall.


SS09 2223: Statistik Proses Kontrol SS09 2223: Statistical Process Control Credits: tiga/three Semester: II

TUJUAN PEMBELAJARAN/ LEARNING

Mampu melakukan pengontrolan proses multivariat dan memiliki potensi untuk mengembangkan metode baru

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OBJECTIVES dalam pengontrolan proses.

[Capable to control multivariate process and have potential capability to develop a new methods on controlling process.].





� Pengantar Statistical Process Control. Diagram kontrol sederhana untuk atribut dan variabel. Diagram kontrol multivariat untuk atribut, target, dan variabilitas. Indeks kemampuan proses: univariat dan multivariat. Diagram kontrol lain: CuSum, EWMA, Multiple Stream, Short Run, MCuSum, MEWMA, Systematic Pattern.

� [Introduction to Statistical Process Control. Simple control chart for atributes and variables. Multivariate control chart for attributes, targets and variability. Proces capacity Index: univariate and multivariate. Ohers control charts suh as: CuSum, EWMA , Multiple Stream, Short Run, MCuSum, MEWMA, Systematic Pattern.]


1. Montgomery, D.C., 2005, Introduction to Statistical Quality Control 5ed, John Wiley and Sons, USA.

2. Fuch, C., Kennet, S.R., 1998, Multivariate Quality Control, Theory and Application, Marcel Dekker Inc., New York

3. Lenz, H.J., Wilrich, P.T., 2004, Frontier in Statistical Quality Control 7, A Springer Verlag Co., Berlin.

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4. Keats, J.B., Hubele, N.F., 1989, Statistical Process Control in Automated Manufacturing, Marcel Dekker Inc., New York.

5. Quesenberry, C.P., 1997, SPC Methods For Quality Improvement, John Wiley and Sons, USA.

6. Journal of Quality Technology, Journal of Quality Engineering, Tecnometrics.


SS09 2224: Teori Antrian SS09 2224: Queueing Theory Credits: tiga/three Semester: II


Memahami konsep proses Markov dan kaitannya dengan teori antrian, sistem antrian, sistem antrian Markov, sistem antrian Semi Markov, sistem antrian jaringan terbuka, sistem antrian jaringan tertutup, dan Markov Modulated Arrival Process.

[To understand the concept of Markov’s process and its relationship with Queueing Theory , Queueing System, Markov’s queueing system, Semi markov’s queueing system, Opened network’s queueing system, closed network’s queueing system, Markov Modulated Arrival Process.]



� [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers,

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Central Limit Theorem, and function of random variable.]


� Review proses Markov diskrit dan kontinyu. Momen klaster sistem antrian, notasi Kendall, teorema little, traffic intensity, dan hukum aliran konservasi. Sistem antrian Markov jalur tunggal dan ganda. Sistem antrian semi-Markov. Sistem antrian dengan prioritas. Sistem antrian M/G/I, dan G/M/I. Sistem antrian jaringan terbuka, teorema Burke, antrian jaringan Jackson, antrian jaringan tertutup, algoritma konvalensi, mean value analysis, Markov-modulated Poisson Process, Markov-modulated Bernoulli Process, dan Markov-modulated Fluid Flow

� [Reviewing continue and discrete, Clustered moment of queing system, Kendall’s notation, Little Theorem, Traffic intensity, Law of conservation flow. Single and Multiple tracks of Markov Queueing system. Semi markov queueing system. Queueing system with priority. M/G/I and G/M/I queueing system . Opened network’s queueing system, Burke’s theorem. Jackson’s network’s queueing system. closed network’s queueing system. Convalention Algorithmmean value analysis, Markov-modulated Poisson Process, Markov-modulated Bernoulli Process, dan Markov-modulated Fluid Flow.]


7. Breuer, L. And Baum, D., 2005, An Introduction to Queueing Theory and Matrix-Analytic Methods, Springer: Netherlands.

8. Tijms, H.C., 2003, A First Subjects/Course in Stochastic Models, John Wiley & Sons: England.


SS09 2225: Perancangan Kualitas SS09 2225: Quality Design Credits: tiga/three Semester: II

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Mampu mendesain kualitas yang kokoh dan mengoptimumkan respon.

[Orthogonal Arrays, Loss function, S/N ratio optimization for static and dynamic quality characteristic, optimization of single and multiple respons.]





� Orthogonal Arrays, Loss function, Optimasi S/N ratio untuk karakteristik kualitas yang statis dan dinamis, Optimasi respon tunggal dan ganda

� [Orthogonal Arrays, Loss function, S/N ratio optimization for static and dynamic quality characteristic, optimization of single and multiple respons.]


1. Park, S.H., 1996, Robust Design and analysis for Quality Engineering, Chapman Hall.

2. Peace, G.S., 1993, Taguchi Methods, Addison Wesley.


SS09 2226: Analisis Realibilitas SS09 2226: Reliability Analysis Credits: tiga/three Semester: III

TUJUAN PEMBELAJARAN/

Memahami konsep-konsep Statistik yang banyak

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LEARNING OBJECTIVES

digunakan dalam analisis reliabilitas, distribusi probabilitas dalam analisis reliabilitas, model regresi untuk data reliabilitas, proportional Hazard Model, dan model Bayes.

[To understand statistical concepts that have been used in reliability analysis, pobability density in reliability analysis, regression models for reability data, proportional Hazard Model, and Bayes Models.]





� Konsep laju kerusakan dan reliabilitas. Model eksponensial, gamma, weibull, normal, log normal, nilai ekstrim, dan model gabungan. Penaksiran parameter dan fungsi reliabilitas untuk sampel lengkap dan tersensor. Uji hipotesis, plot q-q, reliabilitas sistem pendekatan proses Markov, dan availiabilitas. Model regresi parametrik dan non parametrik, model multivariate dan stokastik, serta metode Bayes.

� [The concept of failure velocity and reliability. Exponential model, gamma, weibull, normal, log normal, extreme value, and joint models. Parameter estimation and reliability function for both small and large samples and cencored samples. Statistical hyphotesis, q-q plot, Markov proces approximation reliability systems and aviability. Parametric and non parametric regression, multivariate models and stocastic models. And bayes methods.]

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1. Gertzbalck, I.B., 1989, Statistical Reliability Theory, Marcell Decker, New York.

2. Lawless, J.F., 1982, Statistical Models and Methods for Life Time Data, John Wiley & Sons: New York.

3. Sinha, S.K. and Kale, B.K., 1980, Life Testing and Reliability Estimation, Wiley Eastern LTD: New Delhi.


SS09 2231: Teknik Simulasi SS09 2231: Simulation Techniques Credits: tiga/three Semester: I


Mampu membangun algoritma pembangkit data statistik yang berdistribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas maupun model statistika.

[Capable to construct the algorithm of generating statistical data which heve univariate or multivariate distribution and statistical models using stocasticcaly simulation. Capable to use stocastic simulation to estimate the density or statistical models.]



� [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers,

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Central Limit Theorem, and function of random variable.]


� Pembangkit bilangan acak dan variabel acak berdistribusi. Simulasi steady-state, Integrasi Monte Carlo, Simulasi Markov Chain, Markov Chain Monte Carlo (Algoritma Gibbs sampler dan Metropolis-Hastings). Teknik reduksi varians.

� [Random number and distributed random variable generator. Steady-state simulation, Monte Carlo Integration, Markov Chain Simulation, Markov Chain Monte Carlo (Gibbs sampler algorithm dan Metropolis-Hastings). Variance reduction technique.]


1. Asmussen, S. and Glynn, P.W., 2007, Stochastic Simulation: Algorthms and Analysis.

2. Law, A. And Kelton, C., 2000, Simulation Modelling and Analysis, McGraw-Hill.

3. Trivedi, K.S., 1982, Probability and Statistics with Reliability, Queueing and Computer Science Application, Addison Wesley.


SS09 2232: Metode Resampling SS09 2232: Resampling Methods Credits: tiga/three Semester: I


Mampu membangun algoritma perbanyakan data yang terbatas dengan resampling, baik data univariat maupun multivariat, serta baik secara uniform maupun secara terbobot dengan suatu densitas. [Capable to construct the algorithm of generating a finite numbers of data using resampling, univariatly or multivariatly, uniformly or weightly with a fixed density]


� Mampu membangun algoritma perbanyakan data yang terbatas dengan resampling, baik data univariat maupun multivariat, serta baik secara uniform maupun secara terbobot dengan suatu densitas

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� [Capable to construct the algorithm of generating a finite numbers of data using resampling, univariatly or multivariatly, uniformly or weightly with a fixed density]


� Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance Rejection, Iterasi Full Conditional Distribution, Algoritma Ekspektasi-Maksimisasi (EM).

� [Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance Rejection, Full Conditional Distribution Iteration, Expectation-maximisation Algorithm (EM)]


1. Efron, B. and Tibhsirani, C., 1993, Bootstrap and Jacknife Method, John Wiley & Sons: New York.

2. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 1995, Bayesian Data Analysis, Chapman & Hall, London.

3. Tanner, M.A., 1996, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition, Springer-Verlag: New York.


SS09 2233: Analisis Bayesian SS09 2233: Bayesian Analysis Credits: tiga/three Semester: II/III


Mahasiswa mengerti, memahami dan menguasai teori Bayesian dan Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-lahan real. [The student can understand and menguasai the Bayesian theory and Empirical Bayes and capable to apply in to real-life problems.]


� Mahasiswa mengerti, memahami dan menguasai teori Bayesian dan Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-lahan real.

� [The student can understand the Bayesian theory and Empirical Bayes and capable to apply in to real-life problems]


� Teorema Bayes, Bayesian inference, Data augmentation, Single-parameter model, Multi-parameter model, Hirarchical model, Jenis prior, prior odds, posterior, posterior odds, Bayes faktor, Bayesian non-Normal dan neo-Normal model, Bayesian Reliability, Mixture densitas, Mixture regresi, Mixture of mixture, Pemilihan model terbaik dengan Bayesian, Struktur Perkalian Distribusi, MCMC.

� [Bayes’s theorem, Bayesian inference, Data augmentation, Single-parameter model, Multi-parameter model, Hirarchical model, types of prior, prior odds, posterior, posterior odds, Bayes

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faktor, Bayesian non-Normal dan neo-Normal model, Bayesian Reliability, Mixture density, Mixture regressioni, Mixture of mixture, Best model selection using bayesian, Distribution multiplicative structure , MCMC]


1. Box, G. E. P. and Tiao, G. C., 1973, Bayesian Inference in Statistical Analysis, Reading, MA, Addison-Wesley.

2. Carlin, B. P. and Louis, T. A., 1996, Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall, London.

3. Casella, G. and Berger, R. L., 1990, Statistical Inference, Duxbury, Bellmont California, USA.

4. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B., 1995, Bayesian Data Analysis, Chapman & Hall, London.

5. Martz, H.F. and Waller, R. A., 1982, Bayesian Reliability Analysis, John Wiley & Sons, New York.

6. McLachlan, G. and Basford, K., 1988, Mixture models: inference and application to clustering, Marcel and Decker Inc.

7. Tanner, M. A., 1996, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition, Springer-Verlag: New York.

8. Titterington, M., Makov, G., and Smith A.F.M., 1985, Statistical analysis of finite mixtures, John Wiley & Sons, UK.

9. Zellner, A., 1971, An Introduction to Bayesian Inference in Econometrics, Wiley, New York.

10. Software: WinBUGS 1.4, Weibull++6, MixS.


SS09 2235: Data Mining SS09 2235: Data Mining Credits: tiga/three Semester: II/III

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Mampu membangun algoritma pembangkit data statistik yang ber-distribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas maupun model statistika. [Capable to develop the algorithm for generating statistical data whic have uivariate and also multivariate distribution and statistical models with stocastically simulation. Capable to apply stocastic simulation to estimate the density and statsitical model]


� Mampu membangun algoritma pembangkit data statistik yang ber-distribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas maupun model statistika.

� [Capable to develop the algorithm for generating statistical data whic have uivariate and also multivariate distribution and statistical models with stocastically simulation. Capable to apply stocastic simulation to estimate the density and statsitical model]


� Machine Learning dan Data Mining, Knowledge Preparation and Representation, Clustering dan Classification (Basic methods, Decision Trees, CART), Targeted Marketing and Customer Modeling, Genomic Microarray Data Analysis, web mining, text mining, multi-media data mining.

� [Machine Learning and Data Mining, Knowledge Preparation and Representation, Clustering dan Classification (Basic methods, Decision Trees, CART), Targeted Marketing and Customer Modeling, Genomic Microarray Data Analysis, web mining, text mining, multi-media data mining]


1. Witten, I. and Frank, E., 1999, Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann.

2. Perner, P. And Rosenfeld, A., 2003, Machine Learning and Data Mining in Pattern Recognition, Springer: Berlin, Germany.


SS09 2241: Studi Kependudukan SS09 2241: Demography Study Credits: tiga/three Semester: I

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Mengetahui dan membandingkan konsep dan fungsi demografi, sumber-sumber data demografi, analisis konsep demografi (studi kasus data BPS), teori penduduk dan teori transisi demografi, beberapa ukuran-ukuran dasar Teknik Demografi, Mortalitas dan Fertilitas, tabel kematian, aplikasi tabel kematian, mobilitas penduduk, ketenagakerjaan, dan kualitas penduduk. [To understand and compare: the concept dan demografic’s function, Sources of demografic’s data, demografic analysis on BPS data’s, population theory and demografic’s transition, some indicators used in demografic techniques, mortality and fertility, life tables, employment and Population’s quality]


� Mengetahui dan membandingkan konsep dan fungsi demografi, sumber-sumber data demografi, analisis konsep demografi (studi kasus data BPS), teori penduduk dan teori transisi demografi, beberapa ukuran-ukuran dasar Teknik Demografi, Mortalitas dan Fertilitas, tabel kematian, aplikasi tabel kematian, mobilitas penduduk, ketenagakerjaan, dan kualitas penduduk.

� [To understand and compare: the concept dan demografic’s function, Sources of demografic’s data, demografic analysis on BPS data’s, population theory and demografic’s transition, some indicators used in demografic techniques, mortality and fertility, life tables, employment and Population’s quality]


� Pengalian demografi dan studi kependudukan, sumber-sumber data demografi, ukuran-ukuran dasar teknik demografi. Pengertian fertilitas, mortalitas, tabel kematian dan aplikasinya. Mobilitas penduduk, kualitas penduduk dan aplikasi metode statistik pada masalah-masalah studi kependudukan.

� [The concept of demografic and population study, Sources of demografic’s data, some indicators used in demografic techniques. The concept of Fertility, mortality, life tables and its application. Population’s mobility, Population’s quality and applying statistical methods into population study’s problems ]


1. Gerber, H.U., 1997, Life Insurance Matthematics, 3rd Edition, John Wiley & Sons: New York.

2. Polland, A.H., Farhat, Y. and Pollard G.N., 1992, Teknik Demografi (terjemahan dari Rozy Munir), UI-Press.

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SS09 2242: Riset Pemasaran SS09 2242: Marketing Research Credits: tiga/three Semester: I


Memberikan pemahaman konsep dasar pemasaran, riset pemasaran, sistem informasi riset pemasaran, serta memberikan kamampuan untuk menerapkan metode-metode statistik dalam riset pemasaran. [To give a complete uderstanding of marketing’s concept, marketing research, the information systems f marketing research, and also to give capability for applying statiistical methods in marketing research]


� Memberikan pemahaman konsep dasar pemasaran, riset pemasaran, sistem informasi riset pemasaran, serta memberikan kamampuan untuk menerapkan metode-metode statistik dalam riset pemasaran.

� [To give a complete uderstanding of marketing’s concept, marketing research, the information systems f marketing research, and also to give capability for applying statiistical methods in marketing research]


� Konsep dasar riset pemasaran dan sistem informasi riset pemasaran. Pengumpulan data. Riset secara deskriptif dan riset eksperimental. Metode sampling dalam riset pasar. Metode analisis data pemasaran : analisis regresi, analisis multivariat, MDS, dan analisis korespondensi. Studi kasus riset pemasaran di Indonesia.

� [The basical concept and information system of marketing research. Data collection. Descriptive adn experimental research. Sampling methods used in market research. Marketing Data Analysis’s methods: regression analysis, multivariate analysis, Multi Dimensional Scalling (MDS), correspondence Analysis. The case study of Marketing Researchin Indonesia]


1. Maholtra, N.K., 2005, Market Research: Applied Approach, 4th Edition, Prentice Hall: New York.

2. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 2006, Multivariate Data Analysis, 6th edition, Prentice Hall International: UK.

3. Sharma, S., 1996, Applied Multivariate Techniques, New-York: John Wiley & Sons, Inc.

MATA KULIAH/ SS09 2243: Statistik Ofisial

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COURSE TITLE SS09 2243: Official Statistics Credits: tiga/three Semester: I


Mampu menggunakan statistik pemerintahan yang digunakan untuk perencanaan dan evaluasi pembangunan. [Capable to use official’s statistics that used for development ’s planning and evaluation]


� Mampu menggunakan statistik pemerintahan yang digunakan untuk perencanaan dan evaluasi pembangunan.

� [Capable to use official’s statistics that used for development ’s planning and evaluation]


� Statistik kependudukan, Sensus Penduduk, Supas, Potensi Desa dan Kecamatan, Daerah dalam angka, Statistik kesehatan, IPM (Indeks Pembangunan Manusia), SUSENAS, SBH (Survei Biaya Hidup), Sensus Ekonomi, Statistik Pertanian, Statistik Industri, PDRB, Pertumbuhan Ekonomi, Inflasi, Indeks Harga Konsumen (IHK), Input-Output (IO).

� [Demografic’s statistics, Population’s Cencus, SUPAS, Villages potential’s survei, Regional Statistics, Health Statistics, Human Development Index, National Social & Economic’s survei, Living cost’s survei, Economic’s Cencus, Agricultural statistics, Industrial statistics, Product Domestic Regional Bruto, Economic’s development, Inflation, Consumer’s price index, Input-Output tables]


1. Hoover, E.M., 1998, An Introduction to Regional Economics, 3rd Edition, University of Pittsburgh: New York.

2. Arsyad, L., 1992, Ekonomi Pembangunan, Edisi Ketiga, FE-UGM: Yogyakarta.

3. Profil Daerah Kabupaten dan Kota, Jilid 2, Penerbit Kompas: Jakarta.

4. BPS Jawa Timur, 2000, Perspektif Jawa Timur dari sisi Indikator Sosial, Surabaya.

5. Publikasi BPS yang lain: Sensus Pertanian, Survei Tenaga Kerja Nasional, Survei Harga Konsumen, Potensi Desa, dan lain-lain.

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SS09 2242: Ekonometrika SS09 2242: Econometrics Credits: tiga/three Semester: II/III


Mampu memahami konsep dan metodologi penelitian ekonometrika, penyimpangan asumsi, model ekonometrika, model persamaan simultan dan SUR, pemilihan teknik pendugaan, konstruksi model empirik dan aplikasi model. [Capable to understand the concept and research methodology of econometrics, violation of the classical assumption, econometrics model, Simulataneous Equation Model and SUR, the selection of estimation’s methods, empirical model’s construction and applying the model]


� Mampu memahami konsep dan metodologi penelitian ekonometrika, penyimpangan asumsi, model ekonometrika, model persamaan simultan dan SUR, pemilihan teknik pendugaan, konstruksi model empirik dan aplikasi model.

� [Capable to understand the concept and research methodology of econometrics, violation of the classical assumption, econometrics model, Simulataneous Equation Model and SUR, the selection of estimation’s methods, empirical model’s construction and applying the model]


� Ruang lingkup dan tujuan ekonometrika. Metodologi penelitian ekonometrika. Masalah dan penyimpangan asumsi model regresi klasik. Model ekonometrika: model dinamik, model persamaan simultan, metode pendugaan model persamaan simultan, dan pemodelan data panel. Konstruksi model empirik dalam analisis ekonomi mikro dan makro. Penerapan model ekonometrika: validasi model, peramalan dan simulasi model. Time series ekonometrik.

� [Coverage and aim of the lecture. Research methodology of econometrics. Problems and the assumption’s violation of classical regression model. Econometrics models such as: Dynamic model, Simultaneous Equation Model (SEM), the methods of estimation used in SEM, Model for Panel Data. The construction of Empirical Model in Micro and Macro Economics. Applying Econometrics model: model validation, forecasting and model’s simulation. Time

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Series Econometrics]


1. Gujarati, D., 2004, Basic Econometrics, Fourth Edition, McGraw-Hill Companies.

2. Greene, W., 1997, Applied Econometrics, John Wiley & Sons: New York.

3. Kmenta, G., 1986, Elemen of Econometrics, Macmillan: New York. 4. Koutsoyiannis, A., 1978, Theory of Econometrics, Harper and

Row.

Silabus-S2-STATISTIKA 2009-2014

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