Some satistical notions needed for biological studies - Vivien Rossi

with cjj the element (j, j) of the matrix (X X)−1. We can test the hypothesis of the nullity of the parameter and build confidence intervals: ICα(βj ) = [bj ± tn−p−1;α/2s.
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reminders

LM

GLM

Some satistical notions needed for biological studies Vivien Rossi

CIRAD - UMR Ecologie des forêts de Guyane [email protected]

Master 2 - Ecologie des Forêts Tropicale AgroParisTech - Université Antilles-Guyane Kourou, novembre 2010

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

estimation

objectives of the course

Give your own overview of statistical tools though Generalized Linear Model framework 1

reminders about some useful stuffs (usual probability distribution, data transformation . . . )

2

reminders about Linear Model

3

insights of the GLM formalism

4

Parameter estimation

Vivien Rossi

Stat for Biology

reminders

LM

GLM

outlines

1

some reminders

2

Linear Model

3

Generalized Linear Model

4

Parameter estimation

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

1

some reminders

2

Linear Model

3

Generalized Linear Model

4

Parameter estimation

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

the normal distribution names: normal, gaussian, Laplace-Gauss

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

the normal distribution names: normal, gaussian, Laplace-Gauss continuous probability distribution, with density   1 (x − µ)2 f (x) = √ exp − 2σ 2 2πσ 2 the corner stone of the statistic:

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

the normal distribution names: normal, gaussian, Laplace-Gauss continuous probability distribution, with density   1 (x − µ)2 f (x) = √ exp − 2σ 2 2πσ 2 the corner stone of the statistic: CLT Let (x1 , . . . , xn ) iid random variables, having mean µ and variance σ 2 then n √ 1X  d n xi − µ − → N (0, σ 2 ) n i=1

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

the normal distribution

link with other distributions The binomial distribution B(n, p) ≈ N (np, np(1 − p)) for large n and for p not too close to zero or one. The Poisson distribution P(λ) ≈ N (λ, λ) for large values of λ. The chi-squared distribution χ2 (k ) ≈ N (k , 2k ) for large ks. The Student distribution T (ν) ≈ N (0, 1) when ν is large.

Vivien Rossi

Stat for Biology

estimation

reminders

LM

GLM

estimation

data transformation logarithm IR+ x

→ IR 7→ log x

sd proportional to the mean or the effects are multiplicatives usual for growth or multiplicative processes if some data are small (