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Chapter 14
Nonparametric Statistics
2
Introduction: Distribution-Free Tests
Distribution-free tests – statistical tests that don’t rely on assumptions about the probability distribution of the sampled populationNonparametrics – branch of inferential statistics devoted to distribution-free testsRank statistics (Rank tests) – nonparametric statistics based on the ranks of measurements
3
Single Population Inferences
The Sign test is used to make inferences about the central tendency of a single populationTest is based on the median η Test involves hypothesizing a value for the population median, then testing to see if the distribution of sample values around the hypothesized median value reaches significance
4
Single Population Inferences
Sign Test for a Population Median ηOne-Tailed Test Two-Tailed Test H0:η1 = η0 H0: η1 = η0 Ha :η1 < η0
{or Ha: η1> η0] Ha: η1 η0
Test Statistic S = Number of sample measurements greater than η0 [or S = number of measurements less than η0]
S = Larger of S1 and S2, where S1 is the number of measurements less than η0 and S2 is the number of measurements greater than η0
Observed Significance Level p-value = P(x ≥ S) p-value = 2P(x ≥ S) Where x has a binomial distribution with parameters n and p = .5
Rejection region: Reject H0 if p-value ≤ .05
Conditions required for sign test – sample must be randomly selected from a continuous probability distribution
5
Single Population Inferences
Large-Sample Sign Test for a Population Median η
Conditions required for sign test – sample must be randomly selected from a continuous probability distribution
One-Tailed Test Two-Tailed Test H0:η1 = η0 H0: η1 = η0 Ha :η1 < η0
{or Ha: η1> η0] Ha: η1 η0
Test Statistic
.5 .5
.5
S nz
n
Observed Significance Level p-value = P(x ≥ S) p-value = 2P(x ≥ S) Where x has a binomial distribution with parameters n and p = .5
Rejection region: z z Rejection region: / 2z z
6
Comparing Two Populations: Independent Samples
The Wilcoxon Rank Sum Test is used when two independent random samples are being used to compare two populations, and the t-test is not appropriate
It tests the hypothesis that the probability distributions associated with the two populations are equivalent
7
Comparing Two Populations: Independent Samples
Rank Data from both samples from smallest to largest
If populations are the same, ranks should be randomly mixed between the samples
Test statistic is based on the rank sums – the totals of the ranks for each of the samples. T1 is the sum for sample 1, T2 is the sum for sample 2
Percentage Cost of Living Change, as Predicted by Government and University Economists
Government Economist (1) University Economist (2) Prediction Rank Prediction Rank
3.1 4 4.4 6 4.8 7 5.8 9 2.3 2 3.9 5 5.6 8 8.7 11 0.0 1 6.3 10 2.9 3 10.5 12
10.8 13
8
Comparing Two Populations: Independent Samples
Wilcoxon Rank Sum Test: Independent Samples
Required Conditions: Random, independent samples
Probability distributions samples drawn from are continuous
One-Tailed Test Two-Tailed Test H0:D1 and D2 are identical H0:D1 and D2 are identical Ha :D1 is shifted to the right of D2
{or Ha: D1 is shifted to the left of D2]
Ha :D1 is shifted either to the left or to the right of D2
Test Statistic T1, if n1<n2; T2, if n2 < n1 (Either rank sum can be used if n1 = n2)
T1, if n1<n2; T2, if n2 < n1 (Either rank sum can be used if n1 = n2) We will denote this rank sum as T
Rejection region: T1: T1 ≥ TU [or T1 ≤ TL] T1: T1 ≤ TL [or T1 ≥ TU]
Rejection region: T ≤ TL or T ≥ TU
Where TL and TU are obtained from table
9
Comparing Two Populations: Independent Samples
Wilcoxon Rank Sum Test for Large Samples(n1 and n2 ≥ 10)
One-Tailed Test Two-Tailed Test H0:D1 and D2 are identical H0:D1 and D2 are identical Ha :D1 is shifted to the right of D2
{or Ha: D1 is shifted to the left of D2]
Ha :D1 is shifted either to the left or to the right of D2
Test Statistic
1 1 21
1 2 1 2
( 1)
2:( 1)
12
n n nT
Test statistic zn n n n
Rejection region: z>z(or z<-z)
Rejection region: |z|>z/2
10
Comparing Two Populations: Paired Differences Experiment
Wilcoxon Signed Rank Test: An alternative test to the paired difference of means procedure
Analysis is of the differences between ranks
Any differences of 0 are eliminated, and n is reduced accordingly
Softness Ratings of Paper
Product Difference Judge A B (A-B) Absolute Value of Difference Rank of Absolute Value
1 6 4 2 2 5 2 8 5 3 3 7.5 3 4 5 -1 1 2 4 9 8 1 1 2 5 4 1 3 3 7.5 6 7 9 -2 2 5 7 6 2 4 4 9 8 5 3 2 2 5 9 6 7 -1 1 2 10 8 2 6 6 10
T+ = Sum of positive ranks = 46 T- = Sum of negative ranks = 9
11
Comparing Two Populations: Paired Differences Experiment
Wilcoxon Signed Rank Test for a Paired Difference ExperimentLet D1 and D2 represent the probability distributions for populations 1 and 2, respectivelyOne-Tailed Test Two-Tailed Test H0:D1 and D2 are identical H0:D1 and D2 are identical Ha :D1 is shifted to the right of D2
[or Ha: D1 is shifted to the left of D2]
Ha :D1 is shifted either to the left or to the right of D2
Test Statistic T-, the rank sum of the negative distances (or T+, the rank sum of the positive distances)
T, the smaller of T+ or T-
Rejection region: T-: ≤ T0 [or T+: ≤ T0]
Rejection region: T ≤ T0
Where T0 is from table
Required Conditions
Sample of differences is randomly selected
Probability distribution from which sample is drawn is continuous
12
Comparing Three or More Populations: Completely Randomized Design
Kruskal-Wallis H-Test
An alternative to the completely randomized ANOVA
Based on comparison of rank sums
Number of Available Beds
Hospital 1 Hospital 2 Hospital 3
Beds Rank Beds Rank Beds Rank 6 5 34 25 13 9.5 38 27 28 19 35 26 3 2 42 30 19 15 17 13 13 9.5 4 3 11 8 40 29 29 20 30 21 31 22 0 1 15 11 9 7 7 6 16 12 32 23 33 24 25 17 39 28 18 14 5 4 27 18 24 16
R1 = 120 R2 = 210.5 R3 = 134.5
13
Comparing Three or More Populations: Completely Randomized Design
Kruskal-Wallis H-Test for Comparing k Probability Distributions
Required Conditions:•The k samples are random and independent•5 or more measurements per sample•Probability distributions samples drawn from are continuous
H0: The k probability distributions are identical Ha: At least two of the k probability distributions differ in location
Test statistic:
212
3( 1)1
j
j
RH n
n n n
Where Nj = Number of measurements in sample j Rj = Rank sum for sample j, where the rank of each measurement is computed according to its relative magnitude in the totality of data for the p samples n = Total Sample Size = n1 +n2 + ….+ nk Rejection region: 2H with (k-1) degrees of freedom
14
Comparing Three or More Populations: Randomized Block Design
The Friedman Fr Test
A nonparametric method for the randomized block design
Based on comparison of rank sums
Reaction Time for Three Drugs Subject Drug A Rank Drug B Rank Drug C Rank
1 1.21 1 1.48 2 1.56 3 2 1.63 1 1.85 2 2.01 3 3 1.42 1 2.06 3 1.70 2 4 2.43 2 1.98 1 2.64 3 5 1.16 1 1.27 2 1.48 3 6 1.94 1 2.44 2 2.81 3
R1 = 7 R2 = 12 R3 = 17
15
Comparing Three or More Populations: Randomized Block Design
The Friedman Fr-test
Required Conditions:•Random assignment of treatments to units within blocks•Measurements can be ranked within blocks•Probability distributions samples within each block drawn from are continuous
H0: The probability distributions for the p treatments are identical Ha: At least two of the p probability distributions differ in location
Test statistic:
2123 ( 1)
1r jF R b pbp p
Where b = Number of blocks p = number of treatments Rj = Rank sum of the jth treatment; where the rank of each measurement is computed relative to its position within its own block Rejection region: 2
rF with (p-1) degrees of freedom
16
Rank Correlation
Spearman’s rank correlation coefficient Rs provides a measure of correlation between ranks
Brake Rankings of New Car Models: Less than Perfect Agreement
Magazine Difference between Rank 1 and Rank 2
Car Model 1 2 D D2 1 4 5 -1 1 2 1 2 -1 1 3 9 10 -1 1 4 5 6 -1 1 5 2 1 1 1 6 10 9 1 1 7 7 7 0 0 8 3 3 0 0 9 6 4 2 4 10 8 8 0 0
2 10d
17
Rank Correlation
Conditions Required:Sample of experimental units is randomly selectedProbability distributions of two variables are continuous
One-Tailed Test Two-Tailed Test H0:p = 0 H0: p = 0 Ha :p < 0
{or Ha: p> 0] Ha: p 0
Test Statistic 2
2
61
( 1)i
s
dr
n n
Where di = ui –vi (difference in ranks of ith observations for samples 1 and 2 Rejection region: ,s sr r
(or ,s sr r when Ha: p> 0)
Rejection region: , / 2s sr r