testing of hypothesis 2013.pptx
DESCRIPTION
yoTRANSCRIPT
![Page 1: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/1.jpg)
TESTING OF HYPOTHESES
![Page 2: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/2.jpg)
What is a Hypothesis?
A hypothesis is an assumption or a statement that may or may not be true.
The hypothesis is tested on the basis of information obtained from a sample.
![Page 3: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/3.jpg)
Test hypotheses
Hypothesis = predictionInvolves association between variables
Children who watch violent television are more violent than children who do not watch violent television
The more spinach people eat, the longer they live
H0: No change in interest rate is warranted.
H1: Raise interest rate to control inflation.
![Page 4: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/4.jpg)
Null and Alternative HypothesesConvert the research question to null and alternative
hypotheses The null hypothesis (H0) is a claim of “no difference
in the population” The alternative hypothesis (Ha) claims “H0 is false”
Collect data and seek evidence against H0 as a way of bolstering Ha (deduction)
![Page 5: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/5.jpg)
Steps in Testing of Hypothesis Exercise
![Page 6: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/6.jpg)
Make a prior expectation or judgment
Define an alternative supposition
Apply appropriate approach to test whether the prior expectation is statistically different from the alternative supposition or not.
13:0 XH
13:1 XH 13:1 XH 13:1 XH
![Page 7: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/7.jpg)
Types of Errors…A Type I error occurs when we reject a true null hypothesis (i.e. Reject H0 when it is TRUE)
A Type II error occurs when we don’t reject a false null hypothesis (i.e. Do NOT reject H0 when it is FALSE)
11.7
H0 T F
Reject I
Reject II
![Page 8: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/8.jpg)
Hypothesis Testing Procedures
HypothesisTesting
Procedures
NonparametricParametric
Z Test
Kruskal-WallisH-Test
WilcoxonRank Sum
Test
t Test One-WayANOVA
Many More Tests Exist!
![Page 9: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/9.jpg)
Parametric Test Procedures
Enables us to determine if there is a significant difference between sample means with underlying assumptions of normality, homogeneity of variances
1. Involve Population Parameters (Mean)
2. Have Stringent Assumptions
(Normality)
Examples: Z Test, t Test, 2 Test,
F test
![Page 10: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/10.jpg)
Nonparametric Test Procedures (also called “distribution free statistics”)
1. Do Not Involve Population ParametersExample: Probability Distributions,
Independence
2. Data Measured on Any Scale (Ratio or Interval, Ordinal or Nominal)
3. Example: Wilcoxon Rank Sum Mann-Whitney Test , Kruskal-Wallis Test
![Page 11: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/11.jpg)
Parametric Test Nonparametric Test
One Sample One Sample t-test One sample sign test
Two independent Two-sample independent Wilcoxson rank-sum test Samples test Mann-Whitney U test
Two dependent Two-paired t-test Wilcoxson signed-rank test Samples Sign test
Correlation Pearson r Spearman (rho) rank-order correlation
Multiple Groups One-way ANOVA Kruskal-Wallis one-way One Factor ANOVA
![Page 12: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/12.jpg)
Statistical Methods to test Hypothesis
![Page 13: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/13.jpg)
Statistical procedures for continuous data:- t-test- Analysis of variance- Correlation- RegressionStatistical procedures for discrete data:- Chi-Square test- Mann-Whitney U test
![Page 14: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/14.jpg)
T –Test: two groups
Paired t-test: This is a special t-test to use for data from paired samples e.g. if we collect data from the same individual before and after a particular treatment
![Page 15: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/15.jpg)
Paired t-test: CalculationSubject Week 1 Week 2 Diff (D) Diff2 (D2)
1 10 122 50 523 20 254 8 105 115 1206 75 807 45 508 170 175
∑D = ∑D2 =Steps 1 & 2: Complete this table
![Page 16: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/16.jpg)
Paired t-test: Calculation
Step 3:
Calculate the t statistic
t = n x ∑D2 – (∑D)2 = √ (n - 1)
∑D
![Page 17: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/17.jpg)
GT = the sum of all observations Ti = the sum of the observations in the i-th ni = the number of observations in the i-th sampleN = total number of observationsΣ ------ = the sum of the square of each sample’s total divided by its sample sizeΣi Σj (x ij )2 = the sum of the squares of all observationsk = the number of groups
Analysis of Variance Anova- To compare three or more groups using F distribution
![Page 18: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/18.jpg)
Correlation This method is used to examine a relationship between two (or more) quantitative variables, e.g. height and weight.
![Page 19: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/19.jpg)
REGRESSION
To determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables).
the formula for a straight line: y = a + bx
Y= the variable that we are trying to predictX= the variable that we are using to predict Y a= the intercept b= the slope
![Page 20: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/20.jpg)
e) Chi-Square testChi square: determines relationship between variables. Chi-square test can handle any number of groups and any number of possible outcomes. The formula for calculating Chisquare a 2 x 2 situation (two groups and two possible outcomes) is as follows:
![Page 21: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/21.jpg)
Mann-Whitney U TestActual measurements not used – ranks of the
measurements usedData can be ranked from highest to lowest or
lowest to highest valuesCalculate Mann-Whitney U statistic
U = n1n2 + n1(n1+1) – R1
2
![Page 22: Testing of Hypothesis 2013.pptx](https://reader035.vdocument.in/reader035/viewer/2022062404/552a3cb55503466b428b458d/html5/thumbnails/22.jpg)
Mann-Whitney U: CalculationStep 1:
Rank all the data from both groups in one series, then total each
Student
School A School B
StudentGrade GradeRank Rank
J. S.
L. D.
H. L. M. J.
T. J.
M. M
K. S.
P. S
B
c
A+
D
D
C+
C+
A
9
14
1
45
Median = ; Median = ;∑RA = ∑RB =
43
11
13
3