Simple Linear Regression
Simple Linear Regression
Simple Linear Regression
dk helatmtheps betreen tm
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6(19.19718) ( 1) =74.2933
P - T
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u
stted Simhle
74.83 +19.197x
The AC1dual - a 1d fo egtimat
f 1 SS, 0. 7.)
SSE SS- SJ 2
Cale)
r -3)
Vaabe
aal
umf8tuares tk Aupo nse
totod
n th last Lle
altulate
113
u sle .
Cb) V():
S
( V(&) ( SL
-(K,R): -, Ss k
ACan d m plat
R fhmbd ralut ses
simde l tand and
The
stadard en
se()- Ahimatad
Si
ettimatud Jtondad f
Se(f) Si
knas ktgmn
K Aeri1n
1u n imle
Hyhothuns aso
also lndeperdent ly
ldepepdeAly distnbts
distnbit.s
dittu
ditta bited yOnd
bthdPOnd
be nmaly
OLt
- 2 ) depes Aulo.
hs tdstnbatn with
o ith
-Fe se6)
vejet
inor wpt
Tat t
(6)o
h%t he elate
olatonah tu Ad
ltm ma
(rAm tha
alttheh olnuud orhy. 1
tuld
betfn t sull
ddalon d olrameal sm "
14991 135
00059 18
288
Sinu
46.62
S
po=o jetid-
cluy l toos18
J tut siynatame kpamin
tnalys vasante oteath
S
Is
hAnaly u Vastante dentuty
ST SSR+ SSE
en um u
(-
wh
2 nen Sum Sfuaes.
SSg
Sum Suares
(esected
tofal
SST
ESSoa)), E(S] S
S
SS n
ndependent andon
vakiales wth In)anddasas haudn
3 n th
u , th
MS dutnbd
MSE
vyest
Ht MS SS
MSe SSe(»a)
n tae fo tehin fi(ante
asanin
Vanuane
Mean Fo
Andys
Andyis
Swm
Soueo sedn
MSg MSME
SSRS
Er SSSS- (-) MSE
aphse ah t tet fo
Use the analys r a n e
$iCana ema.
4.191, S lo/7711, n: do
S SST7338, :
6i Say = I5a. 13
SSg =
1as
SSE SST -
SSR =
f Mg 15 13 28 86
MSE 21-25 18
.01,18 82
,n-a
Sin 7,-
Ryet o
S
Y Sz
(ii) Fe t7taupt
Sx
Sxx Szs
w une yh 6t ,
win o T n a n s .
xmpesom k (12
sedd
ulue ohsesrnier
Plee( -a) sedchon Jnteral n a
rlue i
n-
n|%ée )) 4 Y+i+Ena +))
Sxx
whu +
minimum idth at
at
k
M h d l t i n Intural
tnueass
And widens
s |
hndon
wda than th
Than tenh dent
th Conhdent
d c e n Jntrd
15 alys
brth th e
th Th ee
ond uet
fittdplel
the
tbremotne
k t
ull lvok
leok
ea
J tuanftu haan
model
Hdeuae he iAtsn
Rpamn med) unes
Sema) ssunptins
amalns 6 done.
Kenlad Analy ss
Redea e - J l'z12, -- ,n,hWhent
nr
toprelott tobre)
we may also Stardandsze th enduals 6
Cmputin d t : 11, -yn . If ees ane neimalty
i)auist
(h)adauns i
(b)
(a)
(C)
( ) threstnts Jht ndeal S1tuat ion
uane may be
t slem
h Ce), henduals afkasane shey tnuguality asianit
n Cc medel Inade yuacyje
rdes Tetms heuld be adlded t e medel.
hyher
E Thk uwun meld-fo 0njen pnity data is
99
2.5
95
80 .5
50 O.5
20
-0.5
1.5
0.1 2.5 87
19 -0.9 0.1 1.1 2.1 89 91 93 5 97 99
Residuals Predicted values. y
( 6)
AND CORRELATIKON
SIMPLE LINIEAR REGRESSION
2.1
1.T
0.1
-0.9
(C)
Cotbitient dahnsten CR) t honds tfnel aboat
mlu t h
3fatitita euunes f ho
eodns f t fa molel. J
is
L apsoximato the
n
The Chbutrt ofdaotminatin
atu data
SS SSE
SST SST
kare.
E Fn 0 npuuby es an melt, we
R3S 52:15
45 38
0871 e he melel
SOncdim
Jomtimas satt
suttu deajsam Shes a nm-unear Julatonshp
tn a
and y. JAt nm- intan funtim (on be epeed
as Stakt line b tina Suitabl ttanhnnmalhon
8r
wean
Thnt is
no vheuun Yo X.
J: oo
dos net dst 1 n
tsedictin .
= Eneide fX
and o d and F. tepectiely
estomatadd rue
Th Jikeluhred estimatrs a
Y- Prand 2Y(M-
Sxx -)
TLeshimslt B
( x - S
that SSr
he
Sxx
SR Sxy
SST Sa
SST Sxx SS
SS
SST
(
with (n-a)
a t-d stibutcon
S4atots ases fsudn
J:o yeitn
Jtel tapn-2, thp h:
lo tst
Jt arllnt
Jo
4Ttst H,: J ,
Jt u Tompicatad
M: SAS,
(n>2s)
menataly luje Jamples
s+atatic Z: dnctnahk n(tE_
th tut -R
duttribuld with mean z : auttarhf: 1h//t)
sa nemebly
and
lvera) cI
+
tapb (asc taph k
-
4/ SSstb/acts,h e
n-3 n-3
whuna tarhu e-e
ete