| Learning objectives |
The goal is to enable the students to interpret,
understand and apply basic statistical concepts as they apply in
scientific research as well as in everyday life.
The students will become familiar with basic probability theory as
a model for randomness, concepts of statistical inference as well
as a number of concrete estimators, confidence intervals and test.
Statistical software will be introduced.
At the completion of this course the students will be able to:
- Identify key theories, models and concepts of probability and
statistics.
- Use graphical and numerical methods for exploring and
summarizing data on a single categorical or quantitative
variable.
- Describe basic probability and how probability helps us
understand randomness in our lives, as well as grasp the crucial
concept of a sampling distribution and how it relates to inference
methods.
- Choose and justify appropriate descriptive and inferential
methods for examining and analyzing data and drawing
conclusions.
- Analysis of the association between categorical, discrete, and
continuous variables, using contingency tables, correlation,
regressions, and analysis of variance.
- Communicate the conclusions of statistical analysis clearly and
effectively, i.e identify connections between basic statistics and
the real world.
|
| Examination |
|
Statistics:
|
| Exam
ECTS |
7,5 |
| Examination form |
Written sit-in exam on CBS'
computers |
| Individual or group exam |
Individual exam |
| Assignment type |
Written assignment |
| Duration |
4 hours |
| Grading scale |
7-point grading scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Autumn |
| Aids |
Limited aids, see the list below:
The student is allowed to bring - USB key for uploading of notes, books and compendiums in a
non-executable format (no applications, application fragments, IT
tools etc.)
- Any calculator
- In Paper format: Books (including translation dictionaries),
compendiums and notes
The student will have access to - Access to Canvas
- Access to the personal drive (S-drive) on CBS´ network
- Advanced IT application package
|
| Make-up exam/re-exam |
Same examination form as the ordinary exam
If the number of registered candidates for the make-up
examination/re-take examination warrants that it may most
appropriately be held as an oral examination, the programme office
will inform the students that the make-up examination/re-take
examination will be held as an oral examination
instead.
|
Description of the exam
procedure
|
|
| Course content, structure and pedagogical
approach |
|
The major goal of the statistics course is to produce
statistically educated students which mean that students should
develop statistical literacy and the ability to think and reason
statistically.
Statistics is a valuable tool in the practical application of every
other science. Emphasis is on interpretation and understanding of
simple statistical methods as applied in business, economics,
different types of companies or institutions and industries.
The topics of the curriculum are:
a)The basic laws of probability and the most important probability
distributions.
b) Descriptive statistics, both numerical and graphical.
c) Statistical inference; estimators, confidence intervals and
significance tests of hypotheses.
d) One and two sample tests for means and proportions; paired and
unpaired data.
e) Analysis of association using contingency tables and
correlation.
f) Regression analysis; simple, multiple, logistic.
g) One-way and two-way analysis of variance, analysis of
covariance.
|
| Description of the teaching methods |
| Lectures ( online), exercise classes and computer
workshops on campus |
| Feedback during the teaching period |
| There will be weekly office hours. To provide
feedback on the exam a solution will be made available. |
| Student workload |
| Lectures |
28 hours |
| Exercise classes |
18 hours |
| Computer workshops |
6 hours |
| Preparation |
156 hours |
|
| Expected literature |
|
Book: Agresti A., C. Franklin, B. Klingenberg (2017):
“Statistics: The Art and Science of Learning from Data”
(4th global ed), Prentice Hall; chapters 1-14.
Supplementary notes
Please note, minor changes might occur. The teacher will
upload the final reading list to Learn two weeks before the course
starts.
|
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