Download Handwritten Notes on Data | Elementary Statistics and Probability | STAT 100 and more Study notes Probability and Statistics in PDF only on Docsity! % How to Buy Textbooks
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(00
macile®
Chapter &
Dota -
Qualitative. data (ategor al): ex blood type, Color
Quali Yi Not A +
* Numerical data: &x: shoes Sizes | annual salary
Include +s
TWO kinds:
- Discrete data, : Shoe sizez © or b;
- Continuors data: Salar | ~No gag
m me. SCabe
Political ae AAC Didara
__ Resjors a
arr Ov Frequency
‘s table
Sl SMago = 182 Qualitative data
AvO loo
1s frequency. the + of entries IN a category
> Relate frequency: frequency of a Category
tolal + of obicy vations
Pe Char+
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fareto Diagram. (bar chart)
Opposed neutral Stapport+
*~ Qualitive or Categorical Data:
- Frequency Diagram
Pie Char+
- Pareto (Rar) Diagram
werete Numerical Data
+Fof syStem Crashes per day fo 30 da \S
1S: Ow Oxlye:
AA0001a\a0
Ol ©4331 240
relative fre
q/30= 3
lol30= ,322
5120 = [67
2130 = . (‘Op
Al380 = O07
3/30 2%" OOS
1/30. =" O33
‘qorar_ | 20 97 Should be ctose tl
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Chapter 2
* Wees of Data:
~ Qualitative or Categorical
not ts
€x' blood +ype and hair color
— Numerical or measurement
Ly FS
ex: Shoe Size
Ls Discrete variables: Scale of Aistinct ss
WO gaps : ex \Shoe size
Ly Continuous : an value onan in ae
ek he ght + weieht
Support
Pie. Chart:
fareto Diagran
(Ctlative Frequently
152] 280 = . Saya
H+
Suppors-
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- Pocrete Pata
~ Frequency Distribution
f : ae Lelatwe
Malus ae not aC BO=
(
Histogram
relative {
Freaquenc "
O1
* Commuds variable
Measurements
- Dot Diagram
— Frequency Did ilazn ov
Ind min + max ya
aes of data
1S intev vats
Class Fre / vandns Ww
ntey val
Ee Freaquey asi, Kreg" is
tf
Ch 47 Ot
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Measures of Cemicy
Rx; 32, 376,43.,4S,47.5t, CO, 21.93.47
Rie Xa Kg, Rp ess.
~ Sigma Notation
oes Xo - Xa ,X4 é »>Xn are Numbers
Vili IAs n
K+ Xo +Ke tXy oH Re oe
given a Collection oF data x, Xo pkg en
4 Sample. mean:
Xt Xt X&ytanw* Kn ' =
nN
5 Sample Median
middie value when datas arianged
in. order
giver; 1.8. G:4,4-28, ©) how
1M order : Ses ‘14(E1),4 X, 10.5
median = 6
[fF he y are even talee Qvevage O
Centy anes =
3.8, G4 Gg. 4) 7.2, O.S
7. Cae, | = See
a
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Chapter 2
* Measures of Center
-“Mean +5
XytXgtike = Sum of Hoe 9 on casuremiewiss
aay ‘a
v ‘ v
+X¥a+Xo ay
Xykhkot + Xn » ox
1=\
ex: §
ae reer
“+ Is Sum. OF KX yt Xp +Xa TKyt hs
loa
=| Sat4
~ Median middle wher arcaagta snialiest to lagen
Lbodd ' middle value
even: average Of the dud naidtel| JOLUres
Example Order Smaliets 40 Mlargest
OA, EI Me, lO.
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5 = = eats
“ Q rhles + POINTS oF Aiwa inte quarters (clata)
> Percentiles imore general CaAcA{ ONS
- Calculating 100e--n e
\. ovdey Aaata
Uryyple
Yy if
corres
= 34.0 287 >a
10%) of Caffe Vast 31.7 mins
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* Measures_ of Variaton
LET pX&qpXo7r+ Ky be a_coliection of
num evaical-. Observations
X =¥2 Bessa
Ayerage Deviations : 2 (x: -x)
r) Me =
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Quartiles
=volb oe 8 ae se DON (TS
o |Stem Leal
IM alec iD
\ 2
L\O 22 at's
DE ABN CIT O4
= Sample Variance ample d
% | 1G =
War tance
ee] yf
3
fota| observed —- mean C21 0
\G
Pm 2: =
( Pé Viator 1) Seri
we
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ec\ _ 2
t wok fe
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4 QU,
LED 1@CO,7C ; [40 SO ier), tC f 3§0,
Find mean: 5? - 3 ((x x))2
adds divide ie
: #
-2.\, |
-Find Mea
~ ir -x)
~ Square c
t+ add 4
lenis
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> 6i Variate Data
- +ne sample correlation COeFAr Cient
y
v
“Be Sy
Syy* z, (x-x)(y;-¥)
Ee
—\2 |
Sma! ((iaKy)
<2 nl cS¥)
Ee (Qi-wW
- Visa = IN between FA
-1F ¥70 Man +here IF A POPiTVeRNneav
Cow ¢ation eetueen themvarvables
(AS x WwicCreaSes, MIIWOr-eascs)
\f 4 40 than there 8 A neg rlinear Corr
TIF =O there if no (INGA relatinnsn \\p
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“IF y=tl oy "=-\| then all the pants [ie
On a Straignt Jine
r=O
no opparerit relatianh ip
between the 2 variables
no as velationship
Strougnt | ine can + capture
MMe mformaton
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Ch.
Least Squares Regression
Kei OTH By eee
Mie 3s Sy ae,
* Lintar
fe pit e “pint
a = =
> equation Of aA Ine Y= MnKtb
ae incleperdant
Y= 3K+2 depevdent Navidiole
~ plugin A values furx tae
-9et 4He A For
— grophs draw line
say the best fit hae 1§ given by
= Bix + Bo
best fit line minimizes
Pei rae \2
2 Ch CB, Xj +Be)
Bi =slope = Sxy By = ¥ - Bek
5.
Bs
SS
So
ao
oO
W
an
‘
&
I
RS
5
\
C Algo
»
> plug We Xz@ ard | get ys
AvauImine
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4x +8
2
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PIA) = PHT) + POH) + P(TT)
ly s= Sample space - cortains all elementary outcomes
FS)=|
1.3 Assigning Krota bility fo events
4 coin - SSINg (2 coins) A= exactly 2 hee
hee HHH
H hate <s ete fie Shine
Ce <+ SEI outcomes
rsa THH |?
THT
Te 7TH
TSE
> UNiForm. Probability Mode
4 IP your Sample (© Contains K elementary
oH Comes anol each of them have tne Same
likelyhood OF occuring
lb If an event A contains m elementar Yi
outcomes then PpA)= & PA)=m,
A= exactly 2 heads 8 kK
s what if Pe,) # Pe. #P@s) F... F PER
M general: the probability of an eventh us
the number of af tines A_occurs
the number af trials
as the Number of trials grbturrlarge
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4.4 Event elations
Avg | A HHH |b
AVE A nee B A = there ts exactly 1 tail
Avg 4 HTH Je ae ae EAT, g Saat
AUB LA HTT B > neve \d_ at beart 1 heads
AUB}. 14 THH fe - Def: The" compument. of 4"
AVE, A THT | 6 Acnoted A is the set of
Aue |A Tre | | elementary outcomes Mot mA
A ist: B
— The “union of Aand 6"
denoted AUB is the set of
elementary outcomes in either
Roc B®
~ the “intersectin of A and RB"!
denoted: AB Is the cet of
Clementary outcomes In Aand &
- two €vents are Called incon patible
If Aw IS Cenpty Mutually 2xXClutive)
f (AL) =O
Let A: there Is exacHy 1 tails IAB eAoVhing
Let I | there are exactly 2 heads
- the Law of Complin (wt a
EO = 3 PAL 2 2 fA)ZI= AY A-A
- Addition Law :
FAus) = PA)+ P®) ~ PH)
A- at least one tail
B- atleast Ho heack pUAUe) =2 | 4-
%
lw
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-IF A,B ace compatible
P Avg) = PAD+PB
Eage is A,B
AG ~ empty/ In comperhile
ae
®o
RUC
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= PBA)= 4
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VACVICE .
[OO StudeyS- all. fresn or Sopn
50 girls
O of te girls are Soph
40 are franrnan
Lei = Student selected isa boy
B= Student sel€Cita |§ A sopn
(0 Soph (bc 40 ave €y esh)
- 20
40 boy Soph
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Ch.4 Frobabi lity
t-5.. Conditiona | Frokability: and (Independence
10 marbles
2 red
7 oe Se
let A = | pu | marbel on TAY te 7)
Bete \ pu \l Mmarbel On { HE Q
given A P(B) = es
Wen A P®@) =3.
9 =
a Aap f d
ae Ler FA = neaols fivs4 \ndependent
Sones tr B= teuls ust | A and 8 ave
H given AL PIS) = 2S Called \nolepenct
a given A P(B)=.5 syents
The Conditional Probability of A given B is
de oted P(AIB) and 1s defined toy
PA/B)= Pe)
Fe)
or
PAE) = Fe) PAE)
CK
(O marbles
3 ved P(A) = PAB) “YPA) = S/i0
i green rA)
A= ved Ist
B= aveen !S+
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Cha
to, Rando i
J My mpling fron A - populati
10. marbles e ang.
all Aitter es H Colors
Ho me Wwe :
i va -enany way AN tw) T a grup oF
marbles (wlout replacement
2 The Rute of Caml ations :
1 H+ of jeossible Chaces of r_Obyec
o
MOM A population of a (s
S
() wai n-l) Xx K(a-r +1)
rx(r- Xie _X2Xx1
1€
vtad WW choose
xX: ()* 10-4 ¢ 7. =. JO
Spe ek
fetes PEDO SH g-21 = |
‘lo 1 so Sif
pes O° = 99 = 45
a a: | )
1000, 000 7 (1. ee S 1,002 090, 024 Iga
2 — ee.
A Sample of size n Selected van Ed caste |
- OF ny AlStinct OnyeGs (f ra ee UE oleh. 1®)
possible Sample nat probability a
IH) of being Selected
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2 green bal\\s
X Yea balls
now 2 no replacement
a) green \S+
b= gre M 2ind
-\NA L \
Fab P(A = J4- blhere ave Agreen + tote
Ete ofa a
5 P(e) = PAG) + RAE) = ak aes
HAG =1/2-'/3
plAe) = VW2+ 4/3
Exampe:
=e 10 may bles Az ved \st
7 areen @:ved 2rd
3 red
PB) = Pie) + PAB)
PAR) =
P(AE) =
q
5h She
+
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Clhapier_S_
Random Variables
H
He
Hae Let X be the # of heads
3 a posside values: O
Nre tee
aes H a
v 3
P(x20) = '/3
Plx=1).> 4/9
P ye =a)= 3/%
P(x-=2= V8
-Q random variable KX associa tes a Numerical value
WIiHy ean outcome of an expenmes Be
- jhe Pebaleility AlStabuhan of a discrete random
Variable IS a liSt of tne distnct values OF X
Together wits +hetr prroealoi ii |
= 7%
P(x = Ne Hf
P(X=a) =
P(x 3) ie
O1F the roll iS [e3Sy aon oF ? ( to 2
X=l otherwise
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HW
lL. | PA)=.07 PB=.5s2 PRO =.37
Find F/B iA)
f (Ale) = Rae
VY)
wa
SN
PRIA)= 32 =.
P(BIA) = Pie)
5
Qs
Pee) = PIE): PIAIE) :
P@ve) = PA)+ P(e) - PAB)
3 Find PBIA)= PAB) 004
PA) AN
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pring 03
4° a) Pp (@) = 1- PR) = -&= -La\
PAG) = FA) Re = when Preyie wdlependerrt
P AE) PA +P) a)
i a - P(A)
G4 =. ll
-44
» SS Giver) al given
Te 36
tft
3 zs a given
P(AlB)= PAG -. 44
Ae) g
Pe) 2 = 70) r ©) they are indopes
ft SSS Se NOt mujua
TR ner
equal Zao
has ty =1* Sélye
For wi Kvioben
|
: b { =
B16 2° tre) te |
C) EY): Wt at 2+. | 4
ae %2
A) 3°]
FPoF hmes Class mech@ywas week: [45 i)
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oY Pees
Www Ww. Peer cee cu
n_ each trial
n trials
the es ty Aistribution of K_1
Cai EON oO binomial cist ilecchoy
\WWihnic depey dS on n and Ec
Example
X= # OF Students who pr neves Gorn _(ntenet
=A Stud
e= awe
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Chapter 5
Binomial Table
Example :
CO 7 of Flights at an airport depart on time
See = the probability that, of [4 randon ly
Cl Flights
Wie [4-
p=.
a) at l€ast & depart on time
bad most S do not olepart on time
Oo ekactly § departon time
ly a) AX >6) n=4 fo = F(x=@) + AXSTY tw P=)
PK 2G) = | - P(IX<6) which = 1 P(KKES) his value
(Sin the table gaundoy .C%aNd i@tross S + get value
b) asking for probability of failuye -l08% €or value of IK
ot
ae pl <s) go 40S and .4 get value
c) Poke = fe ge to § And Get value Oe nis
R48) Sub tae Hie one abe 1+ P(x 27)
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Bample
GO% of all pel tayo developuent
i) Feople & lected
A)at feast (o favor devclopment
PX 2G@)= |- Pie) rewrite |- PKES.
dO to N= \Q
goto .6
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p=.G
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look up & and 1 then P(Ks8)- Pkey
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tne de velopment
P (4x29)
go to n=I2 and -lo
F(IX49) ~ PXA4) because
OW Car + Pk <8) - GE 2)
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z
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P(B=,.OnF ,.@OH = . oO]
SO. "Ss tAce
they are
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4, (13) N=1I3 r=3 SO n-r+] =ll
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i fa-W-lo-9 = 495
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Chapter S
Practice
40°; of-all calls are put Mm held
|B calls fandomiy S¢iecteol
a) prrleabi lity that at least 7 put on hod
P (hold) = . 4 P(X2D)
N=IG Need 1 Flip inequality
pat USe law of Compl t
l= PK47) which = 1: P(x2G)
Sor e=1sS27 = [-473|
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= 64
P
I" ..4=.6 2 of allure
P(X 48) =. 294
c) expected | ##+hat would be puton hold
pat
=I
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i084
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Guartiles of a Probability Pensity Functron
= Fre st quartile \S the point
G1 SA that the area cnoter
the Curve to +ne left of qW
gual to .aS
le or mtdAlan Gn =25
loolting for a et
bhe O and Q
A= 1 bh
Zz
4276
total area uncler fs? dy
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- 4, > area NdayteCU ees as
- gg 2median Sonavead& .S ta let
- q,2 area 15 «15°10 He lett
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Me Standardized voriable
2 Koy has meandand sp=l
6
x 1S. QA Continuous random variable with th
PrOcabi lity Aensity function (x)
Quartiles
Lower q paint on X oxis that Satisfres +
the pt on Hoe. X axis Such thot
P® £91) =-as
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nedliain) P(X492)= 55
every Hang to the 1eft of Casts”. SO
ley ey Ya rt on xX axis. Sl
- FXS142)=
everything 1d +he left
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TO Find Quartiles:
PQ) =
(
7%
base= 4,
heignt =
HV te
1s 75 uch ee)
BX A,
ae aNd:
ZuatGZz
3 U
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6.2) NOmmMal piStn lution
hag NOV NiO W436
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23 The Standard Normal Dist bution
NCO, )
A lol 2
let X be a x randony, vav e
WI probability distribution N ©, |)
Find Rik 475
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Ea accept nooks ne mater
lf % iS QA pil
7 vials
| 10eBON. bb
20D) , [s4\l G00 eGo}
SUb farmer Contin uty corre
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Examples:
Let X be..bnmial. n= as p.=16
tna). .
ay P[X dhs trie ed as Vas: .ej=l5
yr Z 25 (0 6)64) =
SE P/le5 “5 424115
6
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ae S
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CPL AX41¢) = Pha 2 xan]* Pls 2K 49k
P/T).5-1s_ €2 4 S-!5
a G
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0.5
Normal approximatia fo tie binomial
X 1S G@ binomial random variable
when np and n(i-p) are large MWe
binomial clistributian \S approximated by
IN ne \net py
also 27 XK -=np IS apprax (0,1)
pe (-p)
Ckample :
a peat ieee
aw” PIS2 4 xeTi}= P53 £ xe 4]=
as Rdistibuted as N C4 iso tise-4- 6)
e p[s?.s 4 Meee |S
A eeth Pi5a5-C4-\50) SBS Ths- C4 SD
Oe ISO 4 ISO)C FIG
get answers fram table + Subtract
Coot
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aS
Central Limit Theorem.
Whatever +ne population , the distribution
of Xi. us Arproximately Normal when +ne
Sample SiZ€ 18 large.
F(x)
TtAEe a Sample of
SiZe N (where_h 1S brg)
“ 6 and calculate K Sampeman
mean
sss peradion= = 300, 000, 000
5 too big S€ N=(00
talce joo
ceMtral
Limit
Theorem
K XK IS a random variable With p
Qn lation
mean [Y and standard cleviation 6.
IF your take a vandem sample size A where
IS lavage then X I approx NCNM, ra
Z>X=M x wn (of
SAN
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Point Estimation +
A Statistic intended for estimating wa
Of a population. IS. CC
The standard deviaton of a point eStimator
\ red
Hyv€
tAt
+O (= aur
take a’ rardan sample CFLS
4,
the point estimator for /v
Giiven F A
lf we have a epee On with population. mean [V
Std A xr
ana
of
viation Go take
A CmMuUck aller than PF
ano Compute ple Mtans X
How 1S XK distributed -
> use C¢ ral timit Theorem NON, 2)
Notation Z oY is_ the_pont such that
Standard normal a
Eyal
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mean Font timation :
want to estimate tne parameter [Vv
we Know about a yandom Sample of Size n
, Xing, Neiait Kiqg cites §
Powt eShmator for MV = K = e Xi
Standard d Evror(X)? &
1 Pproximia 1
(Sample
fy wy
If N Is lavyge the |\000-«)% error margin
\S Zoggs
mw
f PEE OY
Mme AI, error margin 1s A2.SB-_S_
| mM
with 14 fe Confidence tre _ population
mean is in me interval Kk + 2,53 se
ny
Example:
A random sam ple of 100 Is ee" for) the
Population at large CUS poy J one. 4 AVERAGE NA Come
‘S Computed « The Sample average “ts 045 00.
With sample Sd of 44,000
a) AMaTS pout eHimato f
We aniple mean
b) What (S the a3 % eroremargin
N=|00 §
Zo, S.—> | 4-400 2784 (ge aon
im t
income
=1.96 (table)
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pixssiso
4.) A= S0900 o =
/
mean = 30940
Sd £2 SHUD = $46
mM 100
= Aa ohare S06 - 2094 o.|= Pal
= S54 ASST
wn {cars
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Homeware
le sample mcan
a) find Standavd €'rov rs Ee
SEK) = <6 52 = [8.320]
b) find LOG (l- £) 7% error margin
1S 7, evror maaigin = : =
215S= 1.9% . z = R11
2. Pow ae @k2
Estimated Standard Error
Ce to find
mM
n-! -
S2 = @&3 G€3 =] 3S= Nga | Qe
3. K= 1b. 4G
point €
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GWEN 1.9% _S. =5S.9 (48% error) SO
n
b) WOK evvor Margin
MOPS Sa
Wn
. FOR confidence \merval
or We tes MSS es. shee. F
2vvo ES Pa = ds
ar gi lwASissGfes ASO
and 8\.:
€
Z ' >F | {
1, N =O mean = 8.6 sd= 4.3 CRO7.
G45» 4% 7 .6
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Ch.¥
Sa Saya population has mean |V anol oO
You wait 1d fake Qa random Sample
tn that ne W1% margin of evry is smaller
than df
44% Margin, of _ervov
2.5% (2
is
we want margin of errors = 2.580
bs Tn
Ns (aoe oo
al
Our Population has mean 7] Si Gs
Flow big A Sample Must we talce go ensaur
that he 75% margin of error js hess Haar)
AZ Lo, ©
was
H22y,5
m2. 4eS = NZ aes)
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Aa) what 1s the W177. margin of error
Litjs, 28
2m
loole for hs
ys. OW =
3 Os
Want Efe S +072 | =,ols
loolt at chart at .oals
Za gip = Sas lF
A df. = iealey
Lodijiy Sip as Bult 1. SC rep)
z a Te
#5 The Sample mMe€an K=715.43 S=a473 neSo
Q) what 1S_ the aes nt eShmator of pop AIcan
2
b) JO% evvor ‘of mam
|6WAS AYA =/ 1104S 24e7S
{50 ST
C)WwhatS JO% confidence intevVal
ao >|
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%.2
Confidence Intervals fer large
We Want to estimate IV
USE a vandom. SamAe
\ Ooms Ute x
Chey B= KN IS Standard noma!
o
The. P that nis in + tnterval
Le ag K+ [-¢
this Intewval 1S [00 (1-4
Jmaller # Chigher cant
lavger ~ (larer co ridenie )
bigger Sample Sizes gmauer
=
E XQNple :
n= 30.4
Qt.
SI
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KAY Example: N=40 K=l4l 3= 30a
Ho: = 150
Hy: 4150
k= OS
PK <C) = OS solve for c if tts More
than KK reject +o
% 1s Normal, mean ISO, Sd 20.2
ice Ponett Wo
One and two tailed terts
Ho! M#=O f
Hi Meo
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Example :
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K =157 H,! M#ICO
S = 4 #=,0)
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-a.S8
-Ba- & MGCL puss Zips 2
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KX Ho g -Fe Me 4 -Zey, Ae Zags
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F Claim ov reSearch
ot \ rN TO. €fStablifn
- Gpposite Statement one
Ch hypo
nat are es ne
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RS
Inferences Regarding a Population Proport
au Px \ tions made that_one Four
Hird arad ers, are Mahler nar t+ ta
j coOmMeone thinks that more fhan VLS_ propor
Of 3rd gradevs ore 4A (ft all
ie Popul ahion proportion. P= FoF pple wisome property
pee 5 VAS
) take a random sample and co puie p
@ =H Some prop
V
I
Take a random sam; t nb ample “4
Let xX ty f of GF fall 3rd gq rol CVS
X's a binomial random yaviable w/ P(S) =p
US@ norma Goprok to binemi a
AS AppIrx N np Vnpa Pp)
alee) a2 = » VE
bot j IX) 0)
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