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Summary Mathematical Statistics 214 Part 1

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These notes cover the first half of the syllabus for the Mathematical Statistics 214 module at Stellenbosch university. The notes include all theory from textbook and lectures as well as worked examples for each section.

Institution
Course

Content preview

MathsStats Summary


ChapterFour



Continues Variable i take on any value in a given interval

Probability densityfunction


pay

A
Discrete Variable I Take on finite number of Values

Probability massfunction


pod

x


Cummulative DistributionFunction
stepfunction
Discrete


y PYay for Ryan
CDF

Properties FC o o continuous s Fal
Flo I
FLY isnondecreasing

, Probability density function continuesvariable I

PDF



fly
PDF
ddy
Fly Fly
co
2
Fly
g
f flyldy



ofPDF ffflyldy
Property PDFdoesnot havetobe continuous



Quartiles of distribution s

Thepthquantile of Y P isthesmallest valuesuchthat



Forcontinuous variable s PCYedp FyOp P



Theorem
i b
Playab fafeyldy




ExpectedValues




Discrete variable ELY dyPly
PDF

continuous variable ELY f yflyldy

,Function of a randomvariable s EEgly
f glylfoyldy



VLy and Ely


ECO C Vly EL ut
ly
EL cgey C EEgly
Vig ELY CELYN
EEgilgit grilyn ELgicy.pt tEIgneyn Vlaytb a var y


Uniform Distribution t



Y Unit0,02 E Y VCU andfell are onformulasheet



Normal Distribution 1




Y N M04 FLY VCU andfell are onformulasheet



Wecan alwaystransform a normal random variable to a standard normal

Variable usingthe following relationship

2 Y

,Gamma Distribution



Y gammalaB FLY VCU andfell are onformulasheet



Gammafunction a
f y
é dy

Chisquare Gamma Distribution




Y is chisquare if it is a gamma random
variable with a 2 and13 2
degrees of freedom

ELY VCU andfell are onformulasheet



Wecan convert a gamma variable to chi
square andthen usethetablesto find probability



Y gamma a B I 4 43 Kaa


Exponential Distribution




Y exp B FLY VCU andfell are onformulasheet


Beta Distribution s

FLY VCU andfell are onformulasheet
Y Beta QB

Bla 13 CH B
Betafunction 2 13

,Momentsandmoment functions
generating




TheKthnon centralmomentof a random variableY I firstnoncentral Mi M E Y

Mk ELY


The Kth central moment of a random variableY I first central ELYMI EY n o

My EL Y a I Second central Varcy



If two variables havethesamemomentsthen they havethe same PDFand Cbt


Momentgeneratingfunctions




Mct E et k Mt ECety
ety.pe

continuous met ECety JetYfogdy
Mdtmet to
Mk

Moment functionof a function of a variable
generating




If Yis a random variableand
galis a function of Ythen the moment generating function

for y is
g
Let.ge
f et.ge
fogdy

, TchebysheffsTheorem


Let Y be a random variable withfinitemean u and variance o then
for any K10


P Y M L KO I K2 OR PC Y MI KO f k


Gives probability that is ko unitsfrom mean true for distribution
y any



ChapterFive Bivariatedistributions




Distributions with two random variables




Discrete variables 1

equivalentto pmf
Joint bivariate probability function Ply ya PYagi Ya ya

Bivariate CDF
Fly ya Ey Ily PCtita
NB 1
Paige 421yd Flyya

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Uploaded on
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Number of pages
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Written in
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Dux scholar at St Johns College, Johannesburg 2019. Co-founder of Lockdown Learning. Attained a 94% average in final examinations with six subjects in the top 1%, and a further 2 subjects in the top 5%. I used these notes to prepare for prelims and finals.

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