Download presentation

Presentation is loading. Please wait.

Published byColton Sinor Modified over 3 years ago

1
Review of one-way ANOVA Kristin Sainani Ph.D. http://www.stanford.edu/~kcobb Stanford University Department of Health Research and Policy http://www.stanford.edu/~kcobb

2
ANOVA for comparing means between more than 2 groups

3
The F-distribution A ratio of variances follows an F-distribution: The F-test tests the hypothesis that two variances are equal. F will be close to 1 if sample variances are equal.

4
How to calculate ANOVA’s by hand… Treatment 1Treatment 2Treatment 3Treatment 4 y 11 y 21 y 31 y 41 y 12 y 22 y 32 y 42 y 13 y 23 y 33 y 43 y 14 y 24 y 34 y 44 y 15 y 25 y 35 y 45 y 16 y 26 y 36 y 46 y 17 y 27 y 37 y 47 y 18 y 28 y 38 y 48 y 19 y 29 y 39 y 49 y 110 y 210 y 310 y 410 n=10 obs./group k=4 groups The group means The (within) group variances

5
Sum of Squares Within (SSW), or Sum of Squares Error (SSE) The (within) group variances + ++ Sum of Squares Within (SSW) (or SSE, for chance error)

6
Sum of Squares Between (SSB), or Sum of Squares Regression (SSR) Sum of Squares Between (SSB). Variability of the group means compared to the grand mean (the variability due to the treatment). Overall mean of all 40 observations (“grand mean”)

7
Total Sum of Squares (SST) Total sum of squares(TSS). Squared difference of every observation from the overall mean. (numerator of variance of Y!)

8
Partitioning of Variance = + SSW + SSB = TSS 10x

9
ANOVA Table Between (k groups) k-1 SSB (sum of squared deviations of group means from grand mean) SSB/k-1 Go to F k-1,nk-k chart Total variation nk-1TSS (sum of squared deviations of observations from grand mean) Source of variation d.f. Sum of squares Mean Sum of Squares F-statisticp-value Within (n individuals per group) nk-k SSW (sum of squared deviations of observations from their group mean) s 2= SSW/nk-k TSS=SSB + SSW

10
ANOVA=t-test Between (2 groups) 1 SSB (squared difference in means multiplied by n) Squared difference in means times n Go to F 1, 2n-2 Chart notice values are just (t 2n-2 ) 2 Total variation 2n-1TSS Source of variation d.f. Sum of squares Mean Sum of SquaresF-statisticp-value Within2n-2SSW equivalent to numerator of pooled variance Pooled variance

11
Example Treatment 1Treatment 2Treatment 3Treatment 4 60 inches504847 67524967 42435054 67 5567 56675668 62596165 64676165 59646056 72635960 71656465

12
Example Treatment 1Treatment 2Treatment 3Treatment 4 60 inches504847 67524967 42435054 67 5567 56675668 62596165 64676165 59646056 72635960 71656465 Step 1) calculate the sum of squares between groups: Mean for group 1 = 62.0 Mean for group 2 = 59.7 Mean for group 3 = 56.3 Mean for group 4 = 61.4 Grand mean= 59.85 SSB = [(62-59.85) 2 + (59.7-59.85) 2 + (56.3-59.85) 2 + (61.4-59.85) 2 ] xn per group= 19.65x10 = 196.5

13
Example Treatment 1Treatment 2Treatment 3Treatment 4 60 inches504847 67524967 42435054 67 5567 56675668 62596165 64676165 59646056 72635960 71656465 Step 2) calculate the sum of squares within groups: (60-62) 2 + (67-62) 2 + (42-62) 2 + (67-62) 2 + (56-62) 2 + (62- 62) 2 + (64-62) 2 + (59-62) 2 + (72-62) 2 + (71-62) 2 + (50- 59.7) 2 + (52-59.7) 2 + (43- 59.7) 2 + 67-59.7) 2 + (67- 59.7) 2 + (69-59.7) 2 …+….(sum of 40 squared deviations) = 2060.6

14
Step 3) Fill in the ANOVA table 3 196.5 65.51.14.344 362060.657.2 Source of variation d.f. Sum of squares Mean Sum of Squares F-statistic p-value Between Within Total 39 2257.1

15
Step 3) Fill in the ANOVA table 3 196.5 65.51.14.344 362060.657.2 Source of variation d.f. Sum of squares Mean Sum of Squares F-statistic p-value Between Within Total 39 2257.1 INTERPRETATION of ANOVA: How much of the variance in height is explained by treatment group? R 2= “Coefficient of Determination” = SSB/TSS = 196.5/2275.1=9%

16
Coefficient of Determination The amount of variation in the outcome variable (dependent variable) that is explained by the predictor (independent variable).

17
ANOVA example S1 a, n=25 aS2 b, n=25 bS3 c, n=25 cP-value d d Calcium (mg)Mean117.8158.7206.50.000 SD e e 62.470.586.2 Iron (mg)Mean2.0 0.854 SD0.6 Folate (μg)Mean26.638.742.60.000 SD13.114.515.1 Zinc (mg) Mean1.91.51.30.055 SD1.01.20.4 a School 1 (most deprived; 40% subsidized lunches). b School 2 (medium deprived; <10% subsidized). c School 3 (least deprived; no subsidization, private school). d ANOVA; significant differences are highlighted in bold (P<0.05). Table 6. Mean micronutrient intake from the school lunch by school

18
Answer Step 1) calculate the sum of squares between groups: Mean for School 1 = 117.8 Mean for School 2 = 158.7 Mean for School 3 = 206.5 Grand mean: 161 SSB = [(117.8-161) 2 + (158.7-161) 2 + (206.5-161) 2 ] x25 per group= 98,113

19
Answer Step 2) calculate the sum of squares within groups: S.D. for S1 = 62.4 S.D. for S2 = 70.5 S.D. for S3 = 86.2 Therefore, sum of squares within is: (24)[ 62.4 2 + 70.5 2 + 86.2 2 ]=391,066

20
Answer Step 3) Fill in your ANOVA table Source of variation d.f. Sum of squares Mean Sum of Squares F-statistic p-value Between 298,113490569 <.05 Within72 391,0665431 Total74 489,179 **R 2 =98113/489179=20% School explains 20% of the variance in lunchtime calcium intake in these kids.

21
Beyond one-way ANOVA Often, you may want to test more than 1 treatment. ANOVA can accommodate more than 1 treatment or factor, so long as they are independent. Again, the variation partitions beautifully! TSS = SSB1 + SSB2 + SSW

22
C A B A yi yi x y yi yi C B *Least squares estimation gave us the line (β) that minimized C 2 A 2 =SS y A 2 B 2 C 2 SS total Total squared distance of observations from naïve mean of y Total variation SS reg Distance from regression line to naïve mean of y Variability due to x (regression) SS residual Variance around the regression line Additional variability not explained by x—what least squares method aims to minimize The Regression Picture R 2 =SS reg /SS total

23
Standard error of y/x S y/x 2 = average residual squared (what we’ve tried to minimize) (equivalent to MSE(=SSW/df) in ANOVA)

24
Y X The standard error of Y given X is the average variability around the regression line at any given value of X. It is assumed to be equal at all values of X. S y/x

Similar presentations

OK

Copyright © 2013 Pearson Education, Inc. All rights reserved Chapter 11 Simple Linear Regression.

Copyright © 2013 Pearson Education, Inc. All rights reserved Chapter 11 Simple Linear Regression.

© 2018 SlidePlayer.com Inc.

All rights reserved.

By using this website, you agree with our use of **cookies** to functioning of the site. More info in our Privacy Policy and Google Privacy & Terms.

Ads by Google

Ppt on water scarcity in the united Ppt on product marketing strategies Ppt on ministry of corporate affairs govt Ppt on online banking security Ppt on conservation of ocean resources Ppt on business etiquettes training day Ppt on field study 3 File type ppt on cybercrime law Ppt on abraham lincoln biography Ppt on exim bank of india