Advanced Statistics Course unit title Advanced Statistics Course unit code MAS801 Type of course unit (compulsory, optional) Compulsory Level of course unit (according to EQF: first cycle Bachelor, second cycle Master) First cycle of Doctoral Year of study when the course unit is delivered (if applicable) 2021/2022 Semester/trimester when the course unit is delivered 1st semester of Doctoral Study Number of ECTS credits allocated 4,8 credits Name of lecturer(s) Prof. I Made Narsa, Dr., SE.,M.Si.,Ak. Zaenal Fanani, Dr., SE.,MSA.,Ak. Rudi Purwono, SE., MSc. Dr. Suhartono, MSc. Dr. Learning outcomes of the course unit Students are able to independently use advanced data analysis and processing techniques, as a tool in research, as well as in the field of business to support decision making. Mode of delivery (face-to-face, distance learning) Distance learning (Using AULA UNAIR) Prerequisites and co-requisites (if applicable) Course content Introduction to Statistics Data driven and theory driven Hypothesis testing Simple linear correlation and regression analysis Multiple regression analysis Dummy variable and Diagnostic test Analysis of Variance (ANOVA) Multivariate analysis Bayesian analysis of linear models Bayesian analysis using Win BUGS 1.4 Introduction to Stochastic Processes and Time series, and Exponential Smoothing modeling ARIMA modeling Recommended or required reading and other learning resources/tools Hair,J. F., Black, W. C., Babin, B. J., dan Anderson, R. E. (2006) “Multivariate Data Analysis”, 7th Edition, Pearson Education,. Weisberg, S (2005), “Applied Regression Model”, John Wiley & Sons, New Jersey. Hosmer, D.W danLemeshow, S. (2000) Applied Logistics Regression, John-Wiley & Sons, Toronto. Hosmer, D.W danLemeshow, S. (1999) Applied survival analysis: regression modeling of time to event data, John-Wiley & Sons, Toronto. Wei,W.W.S.(1990) Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley Publishing Co., USA Dey, D. K., Ghosh, S. K., danMallick, B. K., (2000) Generalized linear models : a Bayesian perspective, Marcel Dekker, Inc. Ntzoufras, I. (2009), Bayesian modeling using WinBUGS, John Wiley & Sons, New Jersey, USA Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D., B., (1995) Ba¬yesian Data Analysis, 2nd edition, Chapman & Hall, Washington, USA Robert L. Mason, R. L., Gunst, R. F., Hess, J. L., (2003) Statistical Design and Analysis of ExperimentsWith Applications to Engineering and Science, 2nd Edition, John Wiley & Sons, New Jersey, USA Draper, N.R.dan Smith, H., Applied Regression Analysis, (3rdEd.Wiley), 1998. Johnson, R. danWynchern, Applied Multivariate Statistical Analysis, (Prentice-Hall), 2002. MontegomeryD.C,Peck, E.A dan Vining G.G., Introduction to Linear Regression Analysis, (3rd Ed. Wiley), 2003. Hanke, J.E. danReitsch, A.G. (1995 & 2001) Business Forecasting, 5th and 7th edition, Prentice Hall. Bowerman,B.L. dan O’Connell, R.T. (1993) Forecasting and Time Series: An Applied Approach, 3rd edition, Duxbury Press: USA. Makridakis, S., Wheelwright, S. C. dan Hyndman, R. J. (1998) Forecasting: Method and Applications, New York: Wiley & Sons. Cryer, J.D. (1986) Time Series Analysis, Boston: PWS-KENT Publishing Company. Zellner, A., (1996) An Introduction to Bayesian Inference in Econometrics, Wiley Classics Library Edition, Canada. Planned learning activities and teaching methods Lecturer Structural assignment Individual assignment Language of instruction Indonesian Assessment methods and criteria Midterm exam (50%) Final exam (50%)