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Statistics For Business and Economics BØK356

In the increasingly data-driven business environment, it is crucial for a modern business person to know how to utilize data. This course will teach you introductory statistics for business and economics students. The knowledge you acquire from this course will equip you with statistical literacy—a necessity to navigate through the digital world we live in today. Moreover, the fundamental knowledge in statistics and programming serves as the absolute basis for a deeper understanding of concepts such as DX (digital transformation), big data, machine learning, and artificial intelligence.


Course description for study year 2021-2022. Please note that changes may occur.

Fakta
Emnekode

BØK356

Vekting (SP)

10

Antall semestre

1

Vurderingssemester

Spring

Undervisningsspråk

English

Tilbys av

UiS Business School

Learning outcome

Knowledge

On completion of the course, students will gain knowledge in:

  • Utilizing fundamental statistical concepts, methods, and data to gain insights
  • Basic programming in R

 

Skills

Upon completion of this course, students will be able to:

 

  • Describe data using summary statistics
  • Approximate real-world events with appropriate random variables and probability distributions
  • Master basic probability theory and probability models, combinatorics, sampling models, conditional probability, Bayes law, and independence of random variables.
  • Conduct hypothesis testing in binomial and multinomial models
  • Depict the relationship between and among variables using statistics, such as correlation and regression analysis
  • Import and export data into R
  • Use R to construct different measures and variables
  • Use R to conduct basic statistical analysis
Content

Typical subject areas covered are:

  • Descriptive statistics
  • Probability
  • Combinatorics
  • Conditional probability
  • Random variables, mean, and variance
  • Joint distributions
  • Basic probability distributions
  • Estimation
  • Hypothesis testing
  • Linear Regression

 

Expectations: 280 ECTS work hours divided into lectures, learning sessions, computer (R programming) sessions, out-of-class work, exercises, and independent study.

Required prerequisite knowledge
None
Eksamen / vurdering
Vurderingsform Vekting Varighet Karakter Hjelpemiddel
Written exam 1/1 4 Hours A - F

 The final grade is based on a final individual exam accounting for 100% of the grade.

Coursework requirements
Innleveringsoppgaver, Tilstedeværelse

 

  • It is required that each group receive a “pass” for at least 5 group reflection notes to be admitted to the final exam.

 

  • Students are required to attend at least 8 of the learning sessions and 8 of the R sessions.
Course teacher(s)
Course coordinator: Yuko Onozaka
Method of work
In this course, you will learn through a mixture of traditional lectures, instruction videos, learning sessions, R sessions, and individual study. Lectures provide the basic statistical concepts, while both learning and R sessions will be problem/project-based in interactive and collaborative settings. Both learning and R sessions are mandatory attendance of at least 8 sessions each. There will be exercises provided for each topic covered
Course assessment
Students will have the opportunity to give feedback on the course first in an early dialogue, and then in a written course evaluation at the end of the course.
Literature
Search for literature in Leganto