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FMSN40: Linear and Logistic Regression with Data Gathering, 9hp
ClimBEco: Linear Regression using R, 2.5hp

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General information

FMSN40 is compulsory in the Mathematical Modelling profile in the third year of the Industrial Engineering and Management programme.
ClimBEco is reserved for ClimBEco PhD students and not given 2019.

This course is taught jointly with FMSN30/MASM22 Linear and Logistic regression, 7.5hp.

The main lectures are joint while the exercise and lab groups are separate (for scheduling reasons).

Parts that are identical in FMSN40 and FMSN30/MASM22

See FMSN30/MASM22 Linear and Logistic regression for reading instructions, handouts, etc.

Differences between FMSN40 and FMSN30/MASM22

See this page for information concerning these. Marked in red below.

Additional contents in FMSN40 compared to FMSN30/MASM22

R

We will use the statistical program R which can be downloaded from http://ftp.acc.umu.se/mirror/CRAN/ free of charge for all major platforms. It is a good idea to install it on your own computer, if you have one. Also, a good programming practice is to consider an appropriate editor for writing and executing R programs; therefore I have set a page for Rstudio (for Windows/Linux/MacOS).

Notice that this course is about Statistics and is not an in-depth course about R. We will discuss the commands needed to produce the desired output and answer the relevant statistical questions. However we will not consider tips-and-tricks, good programming practice or any advanced use of such powerful computer language. R has a large and friendly user community and you will be able to find plenty of good guides, tutorials and answered questions by a simple Google search. Here follow some of the many guides freely available on the web:

Course specific help:

Computer Labs

You will have the chance to book specific computer labs sessions. That is you do not have to attend all labs reported in the schedule below, only the ones you book. Special attention should be devoted to mandatory labs denoted in bold: you MUST attend one of those each week for the first three weeks.

Literature

Teacher

Anna Lindgren, anna@maths.lth.se, tel: 046-222 42 76, MH:136

Course schedule 2018

Week Day Time Place Contents Material
w13 Mon 25/3 13-15 MH:Riesz Lecture 1: Introduction; Review of simple linear regression, linear relationships, linear models and basic assumptions (normality, homoscedasticity, linearity, independence), least squares estimation, basic properties of expectation, variance and covariance; mean and variance of least squares estimators. Rawlings Ch.1;
lecture1_vt19.pdf;
lecture1_vt19.R
Wed 27/3 8-10 MH:Riesz Lecture 2: Continuation of simple linear regression; distribution of least squares estimators; prediction; confidence intervals; hypothesis testing, p-values, quantiles. Rawlings Ch.1;
lecture2_vt19.pdf;
lecture2_vt19.R
Thu 28/3 10-12 MH:230 Compulsory lab 1 lab1_vt19.pdf;
lab1_vt19_solutions.pdf;
lab1_vt19_solutions.R
lab1_vt19_solutions.Rmd
Fri 29/3 13-15 MH:230 Work on project 1 project1_vt19.pdf (updated!);
oslo.txt (updated!)
w14 Mon 1/4 13-15 MH:Riesz Lecture 3. Multiple Regression: matrix notation, properties of least squares estimators for multiple regression; confidence intervals for multiple regression; critical requirements; ill-ranked design matrices, lack of invertibility; categorical variables. Rawlings Ch.3, 4, 6.5;
lecture3_vt19.pdf;
lecture3_vt19.R
Wed 3/4 8-10 MH:Riesz Lecture 4. Analysis of variance: variability decomposition. Global F-test. ANOVA tables. Partial F-test. Rawlings Ch.9;
lecture4_vt19.pdf;
lecture4_vt19.R
Thu 4/4 10-12 MH:230 Compulsory lab 2 lab2_vt19.pdf;
sleep.txt;
lab2_vt19_solutions.pdf;
lab2_vt19_solutions.R;
lab2_vt19_solutions.Rmd
Fri 5/4 13-15 MH:230 Work on project 1
w15 Mon 8/4 13-15 MH:Riesz Lecture 5. R-squared, Adjusted-R-squared. AIC & BIC, automatic selection methods Rawlings Ch.7
lecture5_vt19.pdf
lecture5_vt19.R
Tue 9/4 13-15 MH:227 ...with Data Gathering
Lecture X.: Design of experiments, questionaire contruction and introduction to Project 3.
fmsn40_lecture_vt19.pdf;
project3_FMSN40_vt19.pdf;
project3plan_FMSN40_vt19.pdf
Wed 10/4 8-10 MH:Riesz Lecture 6. Problem areas in least squares; Regression diagnostics: outliers w.r.t. X (leverage), distribution of residuals, standardised and studentised residuals; graphical tools for residual analysis. Influential observations (Cook's distance, DFBETAS) Rawlings Ch.10-11;
lecture6_vt19.pdf;
lecture6_vt19.R;
f6data.txt
Thu 11/4 10-12 MH:230 Compulsory lab 3 lab3_vt19.pdf;
CDI.txt;
lab3_vt19_solutions.pdf;
lab3_vt19_solutions.R;
lab3_vt19_solutions.Rmd
Fri 12/4 13-15 MH:230 Work on project 1
w16 Mon 15/4 13-15 MH:Riesz 13.15-14.00: peer assessment project 1
14:15-15: Wrapping up linear regression
Tue 16/4 8-10 MH:Riesz Lecture 7. Binary data, Bernoulli and binomial distributions, odds ratios and started talking of Logistic regression Agresti Ch. 1, sec 1.2.1, sec 2.3;
lecture7_vt19.pdf;
lecture7_vt19.R
10-12 MH:230 ...with Data Gathering: Work on project 1 and the plan for project 3.
Wed 17/4 10-12 MH:230 Finish project 1 and the plan for project 3. Start on project 2. project2_vt19.pdf (Updated 7/5);
CDI.txt (same data as for lab 3)
16.00 Final deadline project 1. Send report as pdf to fmsn40@matstat.lu.se by 16.00. Subject: "Project1 by studid1 and studid2"
16.00 ...with Data Gathering Send the plan as pdf to fmsn40@matstat.lu.se by 16.00.
Subject: "Project3a plan by studid1 and studid2"
w17 Re-exam period
w18
w19 Mon 6/5 13-15 MH:Riesz Lecture 8. Maximum likelihood estimation, Newton-Raphson, properties, deviance and likelihood ratio tests. Akaike (again), Pseudo-R2. Agresti: 1.3.1, 1.4.1, 2.3.1-2.3.3; several topics scattered in chapter 4, particularly sections 4.1-4.2.
lecture8_vt19.pdf;
lecture8_vt19.R
Wed 8/5 8-10 MH:Riesz Lecture 9. Residuals and model validation in logistic regression. lecture9_vt19.pdf;
lecture9_vt19.R
13-15 MH:230 Project work
Thu 9/510-12 MH:230 Project work
w20 Mon 13/5 13-15 MH:Riesz Lecture 10. Poisson distribution and Poisson regression; Negative binomial regression. Quantile regression Agresti: several sections in Chapter 3.
QuantileRegression.pdf;
lecture10_vt19.pdf;
lecture10_vt19.R
negbin_data.txt
Wed 15/5 8-10 MH:Riesz 8.15-9.00: peer assessment project 2
9.15-10.00: Wrapping up.
Thu 16/5 10-12 MH:230 Project work
Fri 17/5 13-15 MH:230 Project work.
16.00 Final deadline project 2
Send as pdf to fmsn40@matstat.lu.se by 16.00
Subject: "Project2 by studid1 and studid2"
w21 Tue 21/5 10-12 MH:230 ...with Data Gathering: work on Project 3 including help with revised project plan, choice of regression type, and getting data readable. Start analyzing.
16.00 ...with Data Gathering: Send revised plan (pdf) and data (RData) to fmsn40@matstat.lu.se by 16.00.
Subject: "Project3b data by studid1 and studid2"
Thu 23/5 10-12 MH:230 ...with Data Gathering: work on Project 3
Fri 24/5 13-15 MH:230 ...with Data Gathering: work on Project 3
w22 Mon 27/5 13-15 MH:Sigma ...with Data Gathering Oral presentations of project 3: Johan+William, Jackie+Hjalmar, Fredrik+Erik, Erik+Jon
Tue 28/5 13-15 ...with Data Gathering Oral presentations of project 3: Carl+Joel+Tofig, Daniel+Artur, Erik+Robin, John+Axel
Wed 29/5 10-12 ...with Data Gathering Oral presentations of project 3: Sven+Filip, Jacob+Simon, Tilde+Elsa, Markus+Oscar
Fri 31/5 8-17 MH:227-8 Individual oral exams.
Send Project 3 presentation to fmsn40@matstat.lu.se before your oral exam. Subject: "Project3c pres by studid1 and studid2".
Sign-up
oral_vt19.pdf
w23 Mon 3/6 8-17 MH:227
Thu 4/6 8-17 MH:227
Wed 5/6 8-17 MH:227
Fri 7/6 8-17 MH:227-8
w24 Mon 10/6 8-17 MH:227
Tue 11/6 8-17 MH:227
Wed 12/6 8-17 MH:227
Fri 14/6 8-17 MH:227-8
w25 Mon 17/6 8-17 MH:227
Tue 18/6 8-17 MH:227
Wed 19/6 8-17 MH:227
Thu 20/6 8-17 MH:227
w26+ SUMMER VACATION!