Parametric hypotheses tests

Slides:



Advertisements
Similar presentations
Intro to ANOVA.
Advertisements

Introduction to R project Marek Majdan Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;
Categorical Data Analysis
Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function.
Finish Anova And then Chi- Square. Fcrit Table A-5: 4 pages of values Left-hand column: df denominator df for MSW = n-k where k is the number of groups.
Hypothesis Testing Steps in Hypothesis Testing:
Chi square.  Non-parametric test that’s useful when your sample violates the assumptions about normality required by other tests ◦ All other tests we’ve.
Statistical Inference for Frequency Data Chapter 16.
Chapter Seventeen HYPOTHESIS TESTING
Hypothesis Testing Steps of a Statistical Significance Test. 1. Assumptions Type of data, form of population, method of sampling, sample size.
ANOVA Analysis of Variance: Why do these Sample Means differ as much as they do (Variance)? Standard Error of the Mean (“variance” of means) depends upon.
12.The Chi-square Test and the Analysis of the Contingency Tables 12.1Contingency Table 12.2A Words of Caution about Chi-Square Test.
Analysis of Variance: Inferences about 2 or More Means
Finals Schedule n Section 1: 9:00 AM Monday, May 15.
Comparing Means.
PSY 307 – Statistics for the Behavioral Sciences Chapter 19 – Chi-Square Test for Qualitative Data Chapter 21 – Deciding Which Test to Use.
Comparing Population Parameters (Z-test, t-tests and Chi-Square test) Dr. M. H. Rahbar Professor of Biostatistics Department of Epidemiology Director,
Descriptive Statistics
Inferential Statistics
One Sample  M ean μ, Variance σ 2, Proportion π Two Samples  M eans, Variances, Proportions μ1 vs. μ2 σ12 vs. σ22 π1 vs. π Multiple.
Repeated ANOVA. Outline When to use a repeated ANOVA How variability is partitioned Interpretation of the F-ratio How to compute & interpret one-way ANOVA.
Correlation and simple linear regression Marek Majdan Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;
1 STATISTICAL HYPOTHESES AND THEIR VERIFICATION Kazimieras Pukėnas.
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 17 Inferential Statistics.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
A Repertoire of Hypothesis Tests  z-test – for use with normal distributions and large samples.  t-test – for use with small samples and when the pop.
Chapter 9: Non-parametric Tests n Parametric vs Non-parametric n Chi-Square –1 way –2 way.
Between-Groups ANOVA Chapter 12. >When to use an F distribution Working with more than two samples >ANOVA Used with two or more nominal independent variables.
Inference and Inferential Statistics Methods of Educational Research EDU 660.
AP STATS EXAM REVIEW Chapter 8 Chapter 13 and 14 Chapter 11 and 12 Chapter 9 and Chapter 10 Chapter 7.
Inferential Statistics Body of statistical computations relevant to making inferences from findings based on sample observations to some larger population.
Inferential Statistics. The Logic of Inferential Statistics Makes inferences about a population from a sample Makes inferences about a population from.
Psy 230 Jeopardy Related Samples t-test ANOVA shorthand ANOVA concepts Post hoc testsSurprise $100 $200$200 $300 $500 $400 $300 $400 $300 $400 $500 $400.
Business Statistics: A First Course (3rd Edition)
Chapter 10 Statistical Inference for Two Samples More than one but less than three! Chapter 10B < X
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 12 Tests of Goodness of Fit and Independence n Goodness of Fit Test: A Multinomial.
Chapter 12 Chi-Square Tests and Nonparametric Tests.
Jump to first page Inferring Sample Findings to the Population and Testing for Differences.
ANalysis Of VAriance (ANOVA) Used for continuous outcomes with a nominal exposure with three or more categories (groups) Result of test is F statistic.
Analysis of Variance ANOVA - method used to test the equality of three or more population means Null Hypothesis - H 0 : μ 1 = μ 2 = μ 3 = μ k Alternative.
CHAPTER 10: ANALYSIS OF VARIANCE(ANOVA) Leon-Guerrero and Frankfort-Nachmias, Essentials of Statistics for a Diverse Society.
Posthoc Comparisons finding the differences. Statistical Significance What does a statistically significant F statistic, in a Oneway ANOVA, tell us? What.
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
Causality, Null Hypothesis Testing, and Bivariate Analysis
Chapter 9: Non-parametric Tests
Part Four ANALYSIS AND PRESENTATION OF DATA
Dr. Amjad El-Shanti MD, PMH,Dr PH University of Palestine 2016
Chapter 4. Inference about Process Quality
STAT 312 Chapter 7 - Statistical Intervals Based on a Single Sample
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
R. E. Wyllys Copyright 2003 by R. E. Wyllys Last revised 2003 Jan 15
NULL HYPOTHESIS.
Data Analysis and Interpretation
MATH 2311 Section 8.2.
Hypothesis Theory PhD course.
Data Analysis for Two-Way Tables
In-class Exercise Guidelines
Inferences on Two Samples Summary
Do you know population SD? Use Z Test Are there only 2 groups to
Hypothesis Tests for Proportions
I. Statistical Tests: Why do we use them? What do they involve?
F-tests Testing hypotheses.
CHAPTER 6 Statistical Inference & Hypothesis Testing
Analyzing the Association Between Categorical Variables
CHAPTER 12 Inference for Proportions
CHAPTER 12 Inference for Proportions
Doing t-tests by hand.
Inference for Two Way Tables
Statistics collection, presentation, analysis and interpretation of data Descriptive collection and description of data sets to yield meaningful information.
Statistical Power.
Presentation transcript:

Parametric hypotheses tests Marek Majdan Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

Principle of hypothesis tests Null and alternative hypothesis Statistical tests used to reject or accept these hypotheses and infere results from a sample to the population Every test has his own hypothesis Choice of test depends on data Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

T test One sample (compares sample mean with population mean) Two independent samples (compares means of two samples) Paired (compares means of repeated measurements in the same sample) Null hypothesis: compared means are equal Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

T test in R 2 independent groups t-test t.test(y~x) - where y is numeric and x is a binary factor 2 independent groups t-test t.test(y1,y2) - where y1 and y2 are numeric Paired t-test t.test(y1,y2,paired=TRUE) - where y1 & y2 are numeric One samle t-test t.test(y,mu=3) – where mu is the population mean Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

ANOVA test To compare means of three or more groups Null hypothesis: the compared means are equal In R: summary(aov(variable~grouping variable, data=database name)) Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

Post hoc test Anova only tells us that at least two of the compared means are equal Post hoc test compares mean of each group with each Tukey honest significance test results=TukeyHSD(aov(ar_lv~sampleNO, data=zoltan));results Other tests: paired t test with corrections Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com

Comparing proportions Chi squared test to compare proportions between categories of a 2x2 table, 2xN table or MxN table In R: chisq.test (table) prop.test (table) Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD; marekmajdan@gmail.com