Module/Course Title: Statistics

Module course code

KOMS120303

Student Workload
119 hours

Credits

3 / 4.5 ETCS

Semester

3

Frequency

Odd Semester

Duration

16

1

Type of course

Core Study Courses

Contact hours


37.50 hours of face-to-face (theoretical) class activity
8.50 hours of lab activities

Independent Study


45 hours of independent activity
45 hours of structured activities

Class Size

30

2

Prerequisites for participation (if applicable)

-

3

Learning Outcomes

  1. Students can demonstrate systematic thinking in analyzing problems according to their scientific field
  2. Students can apply effective problem-solving methods
  3. Students can design solutions to existing problems according to their scientific field
  4. Students can explain statistics
  5. Students can explain statistical classification
  6. Students can use measurement scales
  7. Students can compare descriptive and inferential statistics
  8. Students can make hypothesis
  9. Students can apply parametric and non-parametric statistics in real life

4

Subject aims/Content

This course provides knowledge about the meaning of statistics, statistical classification, measurement scales, and descriptive statistics, research hypotheses, as well as parametric statistics, and non-parametric statistics.

Study Material

Introduction:

  • Definition of statistics
  • Classification of statistics
  • Measurement scale

Descriptive statistics:

  • Descriptive statistics
  • Frequency distribution
  • Central tendency

Research Hypothesis:

  • The meaning of hypothesis
  • Types of hypothesis 
  • Formulation of hypothesis statement 

Parametric statistics:

  • Definition of parametric statistics
  • Test requirements analysis

Parametric statistics:

  • Testing the normality of the data using Chi-Square
  • Testing the normality of the data using  Lilliefors 

Parametric Statistics:

  • Advanced data normality program coding
  • Test the homogeneity of the data with the F-test

Parametric Statistics:

  • Advanced data normality program coding
  • Product moment correlation
  • Biserial point correlation
  • Project Task 1

MIDTERM EXAM

Parametric Statistics :

  • Mean difference test (t-test)

Parametric Statistics:

  • A difference test with one-way analysis of variance (1-way ANOVA)
  • Project Task 2

Non-Parametric Statistics:

  • Chi-square technique

Non-parametric statistics:

  • Biserial Point Technique

Non-parametric statistics:

  • Mann-Whitney Test Technique

Non-parametric statistics:

  • Sign Test method

Non-parametric statistics:

  • The Wilcoxon Test method

FINAL EXAM

5

Teaching methods

Lecture, discussion, question and answer

6

Assesment Methods

Activeness in discussion, and question and answer

7

This module/course is used in the following study programme/s as well

Computer Science Study Programme

8

Responsibility for module/course

  • Dr. Komang Setemen, S.Si., M.T
  • NIDN : 0015037601

9

Other Information

  1. Anderson, T.W., An Introductin to Multivariate Statistical Analysis, John Wiley & Sons, Inc., New York, 1958.
  2. Guilford, J.P. and fruchter, B., Fundamental Statistics in Psycholoy and Education, New York: McGraw-Hill Ltd, 1978.
  3. Kerlinger, F.N. and Pedhazur, E.J., Multiple Regression in Behavioral Research, New York: Holt Rinehart and Winston, Inc., 1973.
  4. Koyan, I.W., Statistik Pendidikan, Singaraja: Undiksha Press., 2011.
  5. Sutrisno Hadi, Statistik, Jilid 2, 3, Yogyakarta: UGM, 1986.
  6. Sutrisno Hadi, Analisis Regresi, Yoyakarta: UGM, 1986.
  7. Sudjana, Metoda Statistika, Bandung: Tarsito, 1992.
  8. Sudjana, Teknik Analisis Regresi dan Korelasi bagi Para Peneliti, Penerbit “Tarsito”, Bandung, 1992.
  9. Sugiyono, Statistika untuk Penelitian,  Bandung: Penerbit CV Alfabeta, 2012.
  10. https://youtu.be/zlfwdsEDC4Q
  11. https://youtu.be/8Iklj-lf1fY
  12. https://youtu.be/rT9o2c11Epg
  13. https://youtu.be/KLAEwukvuZs
  14. https://youtu.be/tFRXsngz4UQ