Applied Statistics and Data Analysis
Class meetings
July 28-31, 2022
9:00 am - 4:00 pm
Lecturer
Dr. Paula Moraga
E-mail: paula.moraga@kaust.edu.sa
Website: https://www.paulamoraga.com/
Teaching Assistant
Mr. André Victor Ribeiro Amaral
E-mail: andre.ribeiroamaral@kaust.edu.sa
Website: https://www.avramaral.com/
Course materials
https://www.paulamoraga.com/course-aramco
Datasets
Practicals
https://avramaral.github.io/aramco_course
Method of evaluation
35% - Homework 1
35% - Homework 2
30% - Exam
Homework 1: Set on 29th July and due on 6th August
Homework 2: Set on 31st July and due on 6th August
Exam: Monday 8th August, 5:30 pm - 7:00 pm
Course overview
Objective: This course is an introduction to the formulation and use of linear models (and generalizations) including parameter estimation and inference for such models in a variety of settings. Emphasis will be split between understanding the theoretical foundations of the models and the ability to apply the models to answer scientific questions.
The course covers a range of statistical methods to enable to understand and implement a range of techniques to analyze data using the statistical software R. The objectives are:
- Learning how to construct point and interval estimates of population parameters, and testing hypotheses using samples from the population.
Example: Using the information of a survey about chronic conditions, provide a point estimate and a 95% confidence interval for the proportion of UK adults who live with one or more chronic conditions.
- Learning how to conduct simple and multiple regression analyses to quantify the effects of explanatory variables on a response variable and make predictions.
Example: How does physical activity relate to body mass index (kg/m^2)?
- Learning how to use R (https://www.r-project.org) and the integrated development environment (IDE) called RStudio (https://www.rstudio.com/products/rstudio/download) for statistical analyses (data importing, manipulation, analysis and visualization, and interpretation of results). More information: https://paula-moraga.github.io/book-geospatial/sec-appendixsoftware.html
Course schedule
July 28: Statistical inference foundations
| 9:00 am - 10:00 am | Probability distributions |
| 10:00 am - 10:45 am | Descriptive analysis |
| 10:45 am - 11:00 am | Coffee break |
| 11:00 am - 12:00 pm | Central limit theorem |
| 12:00 pm - 1:00 pm | Lunch break |
| 1:00 pm - 2:30 pm | Confidence intervals |
| 2:30 pm - 2:45 pm | Coffee break |
| 2:45 pm - 4:00 pm | Hypothesis testing |
July 29: Multiple linear regression
| 9:00 am - 10:00 am | Simple linear regression |
| 10:00 am - 10:45 am | Multiple linear regression |
| 10:45 am - 11:00 am | Coffee break |
| 11:00 am - 12:00 pm | Model assumptions, unusual observations |
| 12:00 pm - 1:00 pm | Lunch break |
| 1:00 pm - 2:30 pm | Model selection |
| 2:30 pm - 2:45 pm | Coffee break |
| 2:45 pm - 4:00 pm | Practical |
July 30: Generalized linear models (GLMs)
| 9:00 am - 10:00 am | Logistic regression |
| 10:00 am - 10:45 am | Poisson regression |
| 10:45 am - 11:00 am | Coffee break |
| 11:00 am - 12:00 pm | Sensitivity and specificity. ROC curve |
| 12:00 pm - 1:00 pm | Lunch break |
| 1:00 pm - 2:30 pm | Practical |
| 2:30 pm - 2:45 pm | Coffee break |
| 2:45 pm - 4:00 pm | Practical |
July 31: Categorical variables and interactions. Spatial analysis
| 9:00 am - 10:00 am | Categorical variables |
| 10:00 am - 10:45 am | Interactions |
| 10:45 am - 11:00 am | Coffee break |
| 11:00 am - 12:00 pm | Practical |
| 12:00 pm - 1:00 pm | Lunch break |
| 1:00 pm - 2:30 pm | Spatial analysis |
| 2:30 pm - 2:45 pm | Coffee break |
| 2:45 pm - 3:30 pm | Spatial analysis |
| 3:30 pm - 4:00 pm | Wrap-up |
References
- Faraway (2005) Linear Models with R, Chapman & Hall/CRC
- Faraway (2006) Extending the Linear Model with R, Chapman & Hall/CRC
License
You may not copy or distribute the course materials.