Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li

Transcription

1 Bostatstcs Chapter 11 Smple Lnear Correlaton and Regresson Jng L jng.l@sjtu.edu.cn Dept of Bonformatcs & Bostatstcs, SJTU

2 Recall eat chocolate

3 Cell 175, , October 4, 2018

4 Cell 175, , October 4, 2018 Comparson of frequency of the nonreference allele between the NIPT estmatons (CMDB) and Han Chnese estmatons n the 1000 genomes project (CHN)

5 Covarance ( ) 1 ) )( ( ), ( cov 1 = å = n Y y X x y x n Covarance s 2 =Var(x) =E(xµ) 2 Varance ( = = å ) ) a measure of how much two random varables change together

6 Interpretng Covarance cov(x,y) > 0 cov(x,y) < 0 cov(x,y) = 0 X and Y are postvely correlated X and Y are nversely correlated X and Y are ndependent

7 Correlaton coeffcent Pearson s Correlaton Coeffcent s standardzed covarance (untless): r = cov arance(x, y) var x var y Karl Pearson

8 Correlaton Measures the relatve strength of the lnear relatonshp between two varables Ranges between 1 and 1 The closer to 1, the stronger the negatve lnear relatonshp The closer to 1, the stronger the postve lnear relatonshp The closer to 0, the weaker any postve lnear relatonshp

9 Scatter Plots of Data wth Varous Correlaton Coeffcents Y Y Y Y X X r = 1 r =.6 r = 0 Y Y X r = +1 X r = +.3 X r = 0 X nslde from: Statstcs for Managers Usng Mcrosoft Excel 4th Edton, 2004 PrentceHall

10 Lnear Correlaton Lnear relatonshps Curvlnear relatonshps Y Y X X Y Y X X nslde from: Statstcs for Managers Usng Mcrosoft Excel 4th Edton, 2004 PrentceHall

11 Lnear Correlaton Strong relatonshps Weak relatonshps Y Y X X Y Y X X

12 Lnear Correlaton No relatonshp Y X Y nslde from: Statstcs for Managers Usng Mcrosoft Excel 4th Edton, 2004 PrentceHall X

13 Calculatng by hand 1 ) ( 1 ) ( 1 ) )( ( var var ), ( cov ˆ = = å å å = = = n y y n x x n y y x x y x y x arance r n n n

14 Smpler calculaton formula y x xy n n n n n n SS SS SS y y x x y y x x n y y n x x n y y x x r = = = å å å å å å = = = = = = ) ( ) ( ) )( ( 1 ) ( 1 ) ( 1 ) )( ( ˆ y x xy SS SS SS r = ˆ Numerator of covarance Numerators of varance

15 Correlaton Analyss 1 < r < 1 If the correlaton coeffcent s close to +1 that means you have a strong postve relatonshp. If the correlaton coeffcent s close to 1 that means you have a strong negatve relatonshp. If the correlaton coeffcent s close to 0 that means you have no correlaton. WE HAVE THE ABILITY TO TEST THE HYPOTHESIS H 0 : r = 0

16 Dstrbuton of the correlaton coeffcent SE( ˆr) = 1 r2 n 2 The sample correlaton coeffcent follows a T dstrbuton wth n2 degrees of freedom (snce you have to estmate the standard error). t = r / 1 r 2 n 2

17 Hstory Galton's Sweet Pea Data In Natural Inhertance, Galton (1894) provded a table, whch contaned a lst of frequences of daughter seeds of varous szes organzed n rows accordng to the sze of ther parent seeds In 1896, Pearson publshed hs frst rgorous treatment of correlaton and regresson A smpler proof than Pearson's for the productmoment method proposed by Ghsell (1981)

18 Lnear Regresson Can we predct Novel Laureates per 10 mllon populaton usng chocolate consumpton? Chocolate ~ Nobel laureates Smple Lnear Regresson

19 Lnear Regresson Regresson analyss s used to predct the value of one varable (the dependent varable, ) on the bass of other varables (the ndependent varables, ). Dependent varable: denoted Y Independent varables: denoted X 1, X 2,, X k If we only have ONE ndependent varable, the model s whch s referred to as smple lnear regresson. We would be nterested n estmatng β 0 and β 1 from the data we collect.

20 Lnear Regresson Varables: X = Independent Varable (we provde ths) Y = Dependent Varable (we observe ths) Parameters: β 0 = YIntercept β 1 = Slope ε ~ Normal Random Varable (μ ε = 0, σ ε =???) [Nose]

21 The Intercept, β0

22 The Slope, β1

23 The Slope, β1

24 Buldng the Model Collect Data Test 2 Grade = β 0 +β1*(test 1 Grade) From Data: Estmate β 0 Estmate β 1 Estmate σ ε Student Test 1 Test

25 Test 2 Test B2 Test B2 Lnear Regresson Analyss Plot of Ftted Model Plot of Ftted Model Test Plot of Ftted Model Test B Test B1

26 Whch lne has the best ft to the data????

27 Estmatng the Coeffcents In much the same way we base estmates of on, we estmate wth b 0 and wth b 1, the yntercept and slope (respectvely) of the least squares or regresson lne gven by: (Ths s an applcaton of the least squares method and t produces a straght lne that mnmzes the sum of the squared dfferences between the ponts and the lne)

28 Least Squares Lne these dfferences are called resduals or errors Ths lne mnmzes the sum of the squared dfferences between the ponts and the lne but where dd the lne equaton come from? How dd we get.934 for a yntercept and for slope??

29 Least Squares Lne [sure glad we have computers now!] The coeffcents b 1 and b 0 for the least squares lne are calculated as: SSE = (Y Y ˆ 2 ) = (Y b0 b 1 X) 2

30 Statstcs Least Squares Lne See f you can estmate Yntercept and slope from ths data Recall Data Informaton Data Ponts: x y y = x

31 Least Squares Lne See f you can estmate Yntercept and slope from ths data X Y X Xbar Y Ybar (XXbar)*(YYbar) (X Xbar) Sum = Xbar = Ybar = s xy = /(61) s 2 x = /(61) b 1 = /3.5 b 0 = *3.50

32 Example: Arm Crcumference and Heght

33 Arm Crcumference and Heght Ttest ANOVA

34 Vsualzng Arm Crcumference and Heght Relatonshp

35 Scatterplot wth regresson lne

36 Example: Arm Crcumference and Heght Estmated mean arm crcumference for chldren 60 cm n heght

37 Example: Arm Crcumference and Heght Estmated mean arm crcumference for chldren 60 cm n heght Notce, most ponts don t fall drectly on the lne: we are estmatng the mean arm crcumference of chldren 60 cm tall: observed ponts vary about the estmated mean

38 Lnear regresson assumes that The relatonshp between X and Y s lnear Y s dstrbuted normally at each value of X The varance of Y at every value of X s the same (homogenety of varances) The observatons are ndependent