Course unit title

Advanced Statistics

Course unit code

MAS801

Type of course unit (compulsory, optional)

Compulsory

Level of course units (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)
  1. Prof. I Made Narsa, Dr., SE., M.Si., Ak.
  2. Zaenal Fanani, Dr., SE., MSA., Ak.
  3. Rudi Purwono, SE., MSc. Dr.
  4. 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
  1. Introduction to Statistics
  2. Data driven and theory driven
  3. Hypothesis testing
  4. Simple linear correlation and regression analysis
  5. Multiple regression analysis
  6. Dummy variables and Diagnostic tests
  7. Analysis of Variance (ANOVA)
  8. Multivariate analysis 
  9. Bayesian analysis of linear models
  10. Bayesian analysis using Win BUGS 1.4
  11. Introduction to Stochastic Processes and Time series, and Exponential Smoothing modelling
  12. ARIMA modeling

Recommended or required

reading and other learning resources/tools
  1. Hair, JF, Black, WC, Babin, BJ, and Anderson, RE (2006) “Multivariate Data Analysis”, 7th Edition, Pearson Education,.
  2. Weisberg, S (2005), “Applied Regression Models”, John Wiley & Sons, New Jersey.
  3. Hosmer, D. W. and Lemeshow, S. (2000) Applied Logistics Regression, John-Wiley & Sons, Toronto.
  4. Hosmer, DW and Lemeshow, S. (1999) Applied survival analysis: regression modeling of time to event data, John-Wiley & Sons, Toronto.
  5. Wei, WWS(1990) Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley Publishing Co., USA
  6. Dey, DK, Ghosh, SK, andMallick, BK, (2000) Generalized linear models : a Bayesian perspective, Marcel Dekker, Inc.
  7. Ntzoufras, I. (2009), Bayesian modeling using WinBUGS, John Wiley & Sons, New Jersey, USA
  8. Gelman, A., Carlin, JB, Stern, HS, Rubin, D., B., (1995) Ba¬yesian Data Analysis, 2nd edition, Chapman & Hall, Washington, USA
  9. Robert L. Mason, RL, Gunst, RF, Hess, JL, (2003) Statistical Design and Analysis of Experiments With Applications to Engineering and Science, 2nd Edition, John Wiley & Sons, New Jersey, USA
  10. Draper, N.R. and Smith, H., Applied Regression Analysis, (3rdEd. Wiley), 1998.
  11. Johnson, R. and Wynchern, Applied Multivariate Statistical Analysis, (Prentice-Hall), 2002.
  12. Montegomery D.C, Peck, EA and Vining GG, Introduction to Linear Regression Analysis, (3rd Ed. Wiley), 2003.
  13. Hanke, JE and Reitsch, AG (1995 & 2001) Business Forecasting, 5th and 7th edition, Prentice Hall.
  14. Bowerman, BL and O'Connell, RT (1993) Forecasting and Time Series: An Applied Approach, 3rd edition, Duxbury Press: USA.
  15. Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998) Forecasting: Methods and Applications, New York: Wiley & Sons. 
  16. Cryer, J.D. (1986) Time Series Analysis, Boston: PWS-KENT Publishing Company.
  17. Zellner, A., (1996) An Introduction to Bayesian Inference in Econometrics, Wiley Classics Library Edition, Canada.

Planned learning activities and teaching methods

Lecturer 

Structural assignments

Individual assignments

Language of instruction

Indonesian 

Assessment methods and criteria
  1. Midterm exam (50%)
  2. Final exam (50%)