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# Probability and Statistics 1

This is the study programme for 2019/2020. It is subject to change.

The course gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Further the course gives an introduction for hypothesis testing, confidence intervals and basic methods for statistical analysis of data.

### Learning outcome

After having completed the course one should:
• Be able to use basic methods for analysis and presentation of data.
• Be able to do basic probability calculations
• Know what a random variable, probability distribution, expectation and variance is
• Be able to calculate expectation, variance and probabilities for random variables and simple functions of random variables
• Be able to use basic probability distributions like binomial, poission, hypergeometric, exponential and normal
• Be able to use the central limit theorem
• Be able to find estimators and calculate confidence intervals for some important parameters in probability distributions
• Have a basic understanding of hypothesis testing and be able to perform hypothesis testing in some simple situations
• Know the theory for, and be able to use correlation, regression analysis and simple analysis of variance.
• Know the assumptions for the various methods and be able to judge whether the assumptions are fulfilled.
• Be able to use chi square tests
• Be able to use some basic methods for statistical process control.

### Contents

The course gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Introductory theory for estimation and for statistical hypothesis testing in the most common situations is presented. Emphasis is made on both theoretical understanding and applications.
Topics covered: Introduction to basic probability theory, included conditional probability, expectation, variance and the most common probability distributions like binomial, hypergeometric, poisson, exponential and normal. An introduction to point estimation, confidence intervals and hypothesis testing in situations with one and two samples. An introduction to correlation, linear regression, analysis of variance, chi squre tests and statistical process control. Use of software (R).

None.

### Recommended previous knowledge

BØK135 Mathematical analysis for economists, MAT100 Mathematical Methods 1

### Exam

Weight Duration Marks Aid
Written exam1/14 hoursA - FNo printed or written materials are allowed. Approved basic calculator allowed.

### Coursework requirements

Five compulsory assignments
Compulsory exercises have to be approved in order to take an examination.

### Course teacher(s)

Course coordinator
Jan Terje Kvaløy
Course teacher
Bjørn Henrik Auestad , Jan Terje Kvaløy

### Method of work

Six hours lectures and two hours problem solving per week. Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.

### Overlapping courses

Course Reduction (SP)
Statistics and social science methodology (BØK104_1) 8
Introduction to probability and statistics 1 (BMF100_1) 10
Introduction to probability and statistics (TE0199_2) 6
Introduction to Probability and Statistics (ÅMA110_1) 5
Statistical inference 1 (MOT310_1) 5
Statistics for economists (ØK0061_1) 4
Mathematics and statistics (BØK160_1) 4
Introduction to probability and statistics (TE0199_1) 5
Introduction to probability and statistics (TE0199_A) 6
Statistics (BØK145_1) 5

### Open to

Mathematics - One Year Foundation Programme at the Faculty of Science and Technology.

Bachelor studies at the Faculty of Science and Technology.

Master studies at the Faculty of Science and Technology.

### Course assessment

Form and/or discussion

### Literature

Gunnar Løvås: Statistikk for universiteter og høgskoler. Latest edition. Chapter: 1 - 10.

This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 18.01.2020