Statistical Analysis and Data visualisation

• Apply the principles of probability theory to solve a given problem.
• Create summaries and visualizations of a given dataset.
• Estimate properties of a population and quantify the uncertainty of the estimate using confidence intervals.
• Perform a one-sample Student’s t test on a given dataset.
• Perform Welch’s test on a given dataset.
• Perform estimation of single and multiple proportions.
• Test for linear dependence of data using linear regression.
• Apply Student’s t test to a data in a multiple linear regression setting.
• Perform analysis and prediction for time series data on a given dataset.
• Perform statistical tests on data that do not have normality assumptions.
• Compute the joint, marginal and conditional probabilities for a given problem.

– Basics of probability: random experiments, events, sample space, probabilities, rules for computation of probabilities, Bayes’ theorem, independence and dependence.

– Distributions: random variables- discrete and continuous, distribution functions, properties of distributions- mean and variance, quantiles.

– Discrete distributions: Bernoulli, binomial, Poisson, and uniform distributions.

– Continuous distributions: uniform, exponential, Poisson, normal, chi-squared, Student’s t and F distributions.

– Multidimensional random variables: joint, marginal, and conditional distributions.

– Data analysis: descriptive statistics, measuring relationship using covariance and correlation.

– The estimation problem: sample, population, confidence intervals, central limit theorem.

– Statistical hypothesis testing: test statistic, level of significance, p value.

– Linear regression: simple and multiple, fitting, prediction.

– Time series analysis: additive and multiplicative models, trends and seasonality, forecasting.

– R: analyzing and visualizing data using R, computing, performing hypothesis testing, fitting and prediction.

• 36 – 49 (Fri 8-12)
• 6 – 20 (Tues 13-17)

01911/01922, Mathematical Analysis and Modelling, Linear Algebra and Modelling

The course consists of lectures and exercise sessions with an emphasis on using R. There is a mandatory project that can be done in groups of 4-5 persons.

Section of Engineering Education Research
Mobilitet, Transport og Logistik: 2nd semester
Proces og Innovation: 3rd semester
Healthcare Technology: 4th semester
Global Business Engineering