Module Handbook


Module Name:

Lab. Session of Statistics

Module Level:


Abbreviation, if applicable:

MAS 115

Sub-heading, if applicable:


Courses included in the

module, if applicable:




Module coordinator(s):

Drs. Suliyanto, M.Si


Drs. Suliyanto, M.Si

Drs. Eko Tjahjono, M.Si

Ir. Elly Ana, M.Si

Drs. Sediono, M.Si

Dr. Nur Chamidah, M.Si

Marisa Rifada, S.Si., M.Si





Bahasa Indonesia

Classification within the


Compulsory Course / Elective Studies

Teaching format / class

hours per week during semester:

2hours lectures (50 min / hour)


2hours lectures,2hour structural activities, 2hours individual study,

14 week per semester, and total 84 hours per semester 2.6 ECTS

Credit Points:




Learning goals/competencies:

General Competence (Knowledge)

Students can analyze the data simply with the help of Software Minitab and can interpret its output.


Specific Competence :

With MINITAB software properly, students are considered :

1. Being able to make presentation of data in tables and diagrams

2. Being able to calculate measures of central tendency, layout and dissemination of data to a single data and group

3. Being able to calculate the probability of random variable that distributed discrete and continuous

4. Being able to calculate the probability of random variable which has the probability of a random variable that has an average sampling distribution, proportion, and variance

5. Being able to calculate the confidence interval for average parameters, proportion, and variance

6. Being able to do test hypothesis for average parameters, proportion, and variance of population

7. Being able to perform a test of freedom between the two factors and multiple proportions

8. Being able to test the normality of data with methods Kolmogorov-Smirnov test and homogeneity of variance test several populations with Bartlet

9. Being able to test the similarity of the average of a completely randomized design, randomized complete block design and factorial analysis

10. Being able to perform simple linear regression model analysis


This course is delivered by means of laboratory experiments computer to deepen the students' ability to process and interpret data using MINITAB software for material descriptive statistics, measures of central tendency and dissemination of data, the probability distribution of discrete and continuous, sampling distributions, confidence intervals, hypothesis testing, test freedom and some proportion, test of normality and homogeneity of variance, analysis of variance, simple linear regression, and regression model fit test.

Soft skill attribute

Honest, Discipline, Expression of Ideas, Cooperate, and Active

Study/exam achievements:

Students are considered to be competent and pass if at least get 40of Final score.

Final score (NA) is calculated as follow:

10% score of softskill + 20% assignment+ 35% UTS+ 35% UAS


Final index is defined as follow: A : 75 100

AB : 70 - 74.99

B : 65 - 69.99

BC : 60 - 64.99

C : 55 - 59.99

D : 40 - 54.99

E : 0 - 39.99

Forms of Media:

Computer, LCD projectorsandwhiteboards

Learning Methods



1. Dowdy S, Weardon S, and Chilko D, 2004. Statistics For Research, Third Edition, John Wiley & Sons, Inc, Canada.

2. Steel, Robert G.D and Torrie, James H ( Penerjemah Sumantri, Bambang), 1980, Prinsip Dan Prosedur Statistik Suatu Pendekatan Biometrika, Penerbit PT Gramedia Pustaka Utama, Jakarta.

3. Walpole, RE,1995, Pengantar Statistika, Edisi III, Gramedia, Jakarta.

4. Walpole, RE & Myers, RH,1995, Ilmu Peluang dan Statistika untuk Insinyur dan Ilmuwan, Penerjemah : Sembiring, RK, Edisi IV, Penerbit ITB, Bandung.