# Minería de Datos ANALISIS DE UN SET DE DATOS.! Visualization Techniques! Combined Graph! Charts and Pies! Search for specific functions

Save this PDF as:

Size: px
Start display at page:

Download "Minería de Datos ANALISIS DE UN SET DE DATOS.! Visualization Techniques! Combined Graph! Charts and Pies! Search for specific functions"

## Transcription

1 Minería de Datos ANALISIS DE UN SET DE DATOS! Visualization Techniques! Combined Graph! Charts and Pies! Search for specific functions

2 Data Mining on the DAG ü When working with large datasets, annotation results need to be summarized ü The DAG provides visualization of annotation data within its biological context ü In Blast2GO --> Combined Graph Function

3 Combined Graph Each term has a number of sequences associated Node shape to differentiate between direct and indirect annotation Each term is displayed around its biological context Nodes can be coloured to indicate relevance

4 Combined Graph Different GO branches Reduces nodes by number of annotate sequences Node data to be displayed Criterion for highlighting and filtering nodes

5 Combined Graph Let's paint the DAG of the dataset analized yesterday (1000 sequences) Too many nodes!!! Need way to find relevant information

6 Node Information Content Accumulated by node (Sequence Count) Incomming information (Node Score)

7 Node score We compute a node score that reflects the amount of direct information at the node

8 Node score GO4 2.5 dist=0 dist=2 GO dist=2 α = 0.6 dist=1 GO1 1 GO2 3 dist=1 dist=0 dist=0 1 3 NodeScore (GO1) = 1 * = 1 NodeScore (GO2) = 3 * = 3 NodeScore (GO3) = 1 * * = = 2.4 NodeScore (GO4) = 1 * * * = = 2.5

9 Node score vs Annotation score DO NOT MIX-UP!!!!! ROOT 2.5 GO1 GO child seq GO hit1 GO child GO hit2 hit3 1 3 Annotation Score: - In annotation context - Relates to Blast results of ONE sequence Node Score: - In data-mining context - Relates to analysis of a GROUP of sequences AS = max{%sim * ECw]}+ (#TPR_GOs-1) * GOw

10 Filtered Graph # Filtered Nodes Transition nodes Direct annotations

11 Compacting Graphs by GOSlim

12 Show node content

13 Save as picture and as txt Saving Options

14 Graph Charts

15 Graph Charts Sequence Distribution/GO as Bar-Chart Sequence Distribution/GO as Level-Pie (level selection) Sequence Distribution/GO as Multilevel-Pie (#score or #seq cutoff)

16 Multilevel vs. GO-Slim Chart Multi-level Pie with a sequence filter of 20 GO-Slim: Handy to summarize functional content

17 Use DAG to analyze a function DAG can be used to make queries on general concepts without direct annotations How many sequences are annotated to the function photosynthesis? Option 1: Find in the GO graph à direct & indirect annotation Option 2: Find through the Select function. Two sub options Option 2.1. Direct annotation (use GOid or description) Option 2.2. Direct&indirect (use GOid and include GO parents )

18 Example: analyze a specific function export search Find a function on the graph

19 Example: analyze a specific function Select all sequences annotated to this function and its descendents

20 Example: analyze a specific function Locate these sequences

21 Example: analyze a specific function Exporting the sequence table you can see all Sequences annotated to a given function (GO) Explore the annotation diversity of a given function within the graph

22 Conclusions ü DAGs are interesting for browsing functional annotation but can be too large ü With filtering and pruning options you can create more navigable DAGs ü Pies are good to compact information: try out levels ü GO-Slim compacts to more equivalent terms than filtering the GO ü You can use the DAG to query on general terms

23 Minería de Datos ANALISIS DE VARIOS SETS DE DATOS! Functional Enrichment! Enriched Graphs! Meta-analysis

24 Enrichment Analysis Interpretation of a large list of genes: which are relevant functions? One Gene List (A) The other list (B) Are this two groups of genes carrying out different biological roles???? Biosynthesis 54% Biosynthesis 18%??? Sporulation 18% Sporulation 27% Are these differences statistically significant?

25 Fisher's Exact Test One Gene List (A) The other list (B) Biosynthesis 54% Biosynthesis 18% Sporulation 18% Sporulation 27% Contingency table A B A B Biosynthesis 6 2 Sporulation 2 3 No biosynthesis 5 9 No sporulation 9 8 p-value for biosynthesis < 0.05 p-value for sporulation > 0.05

26 Multiple testing correction We do this for all GO term of our dataset!!! Many tests => Many false positive => We need correction! FDR control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of rejected hypotheses, FDR controls the expected proportion of incorrectly rejected null hypotheses. FWER control: The familywise error rate is the probability of making one or more false discoveries among all the hypotheses when performing multiple pairwise tests. (more conservative)

27 Fisher s Exact Test in Blast2GO Test-set Ref-set GO No GO A 2 9 B 3 8 Three files:! Blast2GO project with annotations (.dat/.annot)! One txt file with IDs: Test-set (.txt)! Other txt file with IDs: Ref-set (.txt)

28 Different types of comparisons Compare one condition against another Remove Common Ids Test and Ref-Set are interchangeable Compare a subset against the total Gossip default setting Test and Ref-Set are NOT interchangeable Common IDs Set 1 Set 2 Test- Set Common IDs Ref- Set Ref- Set Common IDs Test- Set

29 FET in Blast2GO Two-Tailed test not only identifies over but also under represented functions. If no Ref-Set is chosen all annotations are used as reference

30 Enrichment Results Result table with link outs to sequence lists

31 Most specific terms Retains only the lowest, most specific enriched term per GO branch

32 Enriched Graph View enriched terms data as DAG graphs! reduce => To draw all nodes, set filter to 1

33 Bar-Chart Export enriched terms as chart! => Filter results % of sequences in Test group % of sequences in Ref group If Test > Ref = overexpressed If Ref > Test = underexpressed

34 Meta-analysis in Blast2GO Annotation Result (.annot) Sequence_1 GO: Sequence_1 GO: Sequence_1 GO: Sequence_2 GO: Sequence_2 GO: Sequence_2 GO: Equivalent formats ó Enrichment Result Treatment_1 GO: Treatment_1 GO: Treatment_1 GO: Enrichment Result (.annot) By joining different functional enrichment results we can create and annotation file of conditions that capture their functional profile Treatment_1 GO: Treatment_1 GO: Treatment_1 GO: Treatment_2 GO: Treatment_2 GO: Treatment_2 GO:

35 Meta-analysis in Blast2GO FIND SIMILARITIES BETWEEN TREATMENTS Use seq names to see treatments Use color by SeqCount

36 Meta-analysis in Blast2GO DISPLAY FUNCTIONAL DISSIMILARITIES ON DAG Use second column number for color

37 Ejercicios: Minería de Datos

### Tutorial for proteome data analysis using the Perseus software platform

Tutorial for proteome data analysis using the Perseus software platform Laboratory of Mass Spectrometry, LNBio, CNPEM Tutorial version 1.0, January 2014. Note: This tutorial was written based on the information

### Unit 29 Chi-Square Goodness-of-Fit Test

Unit 29 Chi-Square Goodness-of-Fit Test Objectives: To perform the chi-square hypothesis test concerning proportions corresponding to more than two categories of a qualitative variable To perform the Bonferroni

### Bootstrapping p-value estimations

Bootstrapping p-value estimations In microarray studies it is common that the the sample size is small and that the distribution of expression values differs from normality. In this situations, permutation

### Blast2GO PRO Plug-in User Manual

Blast2GO PRO Plug-in User Manual CLC bio Genomics Workbench and Main Workbench Version 1.1.0 October 2013 BioBam Bioinformatics S.L. Valencia, Spain Contents Introduction 1 Quick-Start 2 Blast2GO PRO Plug-in

### Hypothesis testing S2

Basic medical statistics for clinical and experimental research Hypothesis testing S2 Katarzyna Jóźwiak k.jozwiak@nki.nl 2nd November 2015 1/43 Introduction Point estimation: use a sample statistic to

### 8-2 Basics of Hypothesis Testing. Definitions. Rare Event Rule for Inferential Statistics. Null Hypothesis

8-2 Basics of Hypothesis Testing Definitions This section presents individual components of a hypothesis test. We should know and understand the following: How to identify the null hypothesis and alternative

### Course on Functional Analysis. ::: Gene Set Enrichment Analysis - GSEA -

Course on Functional Analysis ::: Madrid, June 31st, 2007. Gonzalo Gómez, PhD. ggomez@cnio.es Bioinformatics Unit CNIO ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA

### Introduction to Hypothesis Testing. Hypothesis Testing. Step 1: State the Hypotheses

Introduction to Hypothesis Testing 1 Hypothesis Testing A hypothesis test is a statistical procedure that uses sample data to evaluate a hypothesis about a population Hypothesis is stated in terms of the

### Chapter 16 Multiple Choice Questions (The answers are provided after the last question.)

Chapter 16 Multiple Choice Questions (The answers are provided after the last question.) 1. Which of the following symbols represents a population parameter? a. SD b. σ c. r d. 0 2. If you drew all possible

### Lecture 13 More on hypothesis testing

Lecture 13 More on hypothesis testing Thais Paiva STA 111 - Summer 2013 Term II July 22, 2013 1 / 27 Thais Paiva STA 111 - Summer 2013 Term II Lecture 13, 07/22/2013 Lecture Plan 1 Type I and type II error

### The alternative hypothesis,, is the statement that the parameter value somehow differs from that claimed by the null hypothesis. : 0.5 :>0.5 :<0.

Section 8.2-8.5 Null and Alternative Hypotheses... The null hypothesis,, is a statement that the value of a population parameter is equal to some claimed value. :=0.5 The alternative hypothesis,, is the

### The Goodness-of-Fit Test

on the Lecture 49 Section 14.3 Hampden-Sydney College Tue, Apr 21, 2009 Outline 1 on the 2 3 on the 4 5 Hypotheses on the (Steps 1 and 2) (1) H 0 : H 1 : H 0 is false. (2) α = 0.05. p 1 = 0.24 p 2 = 0.20

### Package empiricalfdr.deseq2

Type Package Package empiricalfdr.deseq2 May 27, 2015 Title Simulation-Based False Discovery Rate in RNA-Seq Version 1.0.3 Date 2015-05-26 Author Mikhail V. Matz Maintainer Mikhail V. Matz

### Correlational Research

Correlational Research Chapter Fifteen Correlational Research Chapter Fifteen Bring folder of readings The Nature of Correlational Research Correlational Research is also known as Associational Research.

### 6. Statistical Inference: Significance Tests

6. Statistical Inference: Significance Tests Goal: Use statistical methods to check hypotheses such as Women's participation rates in elections in France is higher than in Germany. (an effect) Ethnic divisions

### HYPOTHESIS TESTING AND TYPE I AND TYPE II ERROR

HYPOTHESIS TESTING AND TYPE I AND TYPE II ERROR Hypothesis is a conjecture (an inferring) about one or more population parameters. Null Hypothesis (H 0 ) is a statement of no difference or no relationship

### MultiExperiment Viewer Quickstart Guide

MultiExperiment Viewer Quickstart Guide Table of Contents: I. Preface - 2 II. Installing MeV - 2 III. Opening a Data Set - 2 IV. Filtering - 6 V. Clustering a. HCL - 8 b. K-means - 11 VI. Modules a. T-test

### Quantitative Biology Lecture 5 (Hypothesis Testing)

15 th Oct 2015 Quantitative Biology Lecture 5 (Hypothesis Testing) Gurinder Singh Mickey Atwal Center for Quantitative Biology Summary Classification Errors Statistical significance T-tests Q-values (Traditional)

### Hypothesis Testing. Chapter 7

Hypothesis Testing Chapter 7 Hypothesis Testing Time to make the educated guess after answering: What the population is, how to extract the sample, what characteristics to measure in the sample, After

### Confidence Interval: pˆ = E = Indicated decision: < p <

Hypothesis (Significance) Tests About a Proportion Example 1 The standard treatment for a disease works in 0.675 of all patients. A new treatment is proposed. Is it better? (The scientists who created

### Statistical issues in the analysis of microarray data

Statistical issues in the analysis of microarray data Daniel Gerhard Institute of Biostatistics Leibniz University of Hannover ESNATS Summerschool, Zermatt D. Gerhard (LUH) Analysis of microarray data

### Basic Statistics Self Assessment Test

Basic Statistics Self Assessment Test Professor Douglas H. Jones PAGE 1 A soda-dispensing machine fills 12-ounce cans of soda using a normal distribution with a mean of 12.1 ounces and a standard deviation

### Identifying unknown samples based on 16s rdna sequences

BioNumerics Tutorial: Identifying unknown samples based on 16s rdna sequences 1 Aim BioNumerics contains powerful tools for the identification of unknown samples against a reference set. With the internal

### the online and local desktop version of the Pathway tools) (2) and genome overview chart of

1 Supplementary Figure S1. The Omics Viewer description. (1) the cellular overview (available in the online and local desktop version of the Pathway tools) (2) and genome overview chart of the maize gene

### 1 Why is multiple testing a problem?

Spring 2008 - Stat C141/ Bioeng C141 - Statistics for Bioinformatics Course Website: http://www.stat.berkeley.edu/users/hhuang/141c-2008.html Section Website: http://www.stat.berkeley.edu/users/mgoldman

### Module 5 Hypotheses Tests: Comparing Two Groups

Module 5 Hypotheses Tests: Comparing Two Groups Objective: In medical research, we often compare the outcomes between two groups of patients, namely exposed and unexposed groups. At the completion of this

### Introduction to Hypothesis Testing. Point estimation and confidence intervals are useful statistical inference procedures.

Introduction to Hypothesis Testing Point estimation and confidence intervals are useful statistical inference procedures. Another type of inference is used frequently used concerns tests of hypotheses.

### Chapter 8 Introduction to Hypothesis Testing

Chapter 8 Student Lecture Notes 8-1 Chapter 8 Introduction to Hypothesis Testing Fall 26 Fundamentals of Business Statistics 1 Chapter Goals After completing this chapter, you should be able to: Formulate

### Package dunn.test. January 6, 2016

Version 1.3.2 Date 2016-01-06 Package dunn.test January 6, 2016 Title Dunn's Test of Multiple Comparisons Using Rank Sums Author Alexis Dinno Maintainer Alexis Dinno

### An Introduction to Statistics Course (ECOE 1302) Spring Semester 2011 Chapter 10- TWO-SAMPLE TESTS

The Islamic University of Gaza Faculty of Commerce Department of Economics and Political Sciences An Introduction to Statistics Course (ECOE 130) Spring Semester 011 Chapter 10- TWO-SAMPLE TESTS Practice

### False Discovery Rates

False Discovery Rates John D. Storey Princeton University, Princeton, USA January 2010 Multiple Hypothesis Testing In hypothesis testing, statistical significance is typically based on calculations involving

### One-Sample t-test. Example 1: Mortgage Process Time. Problem. Data set. Data collection. Tools

One-Sample t-test Example 1: Mortgage Process Time Problem A faster loan processing time produces higher productivity and greater customer satisfaction. A financial services institution wants to establish

### Section 7.1. Introduction to Hypothesis Testing. Schrodinger s cat quantum mechanics thought experiment (1935)

Section 7.1 Introduction to Hypothesis Testing Schrodinger s cat quantum mechanics thought experiment (1935) Statistical Hypotheses A statistical hypothesis is a claim about a population. Null hypothesis

### Friedman's Two-way Analysis of Variance by Ranks -- Analysis of k-within-group Data with a Quantitative Response Variable

Friedman's Two-way Analysis of Variance by Ranks -- Analysis of k-within-group Data with a Quantitative Response Variable Application: This statistic has two applications that can appear very different,

### Exercise with Gene Ontology - Cytoscape - BiNGO

Exercise with Gene Ontology - Cytoscape - BiNGO This practical has material extracted from http://www.cbs.dtu.dk/chipcourse/exercises/ex_go/goexercise11.php In this exercise we will analyze microarray

### Chapter 8 Hypothesis Testing Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing

Chapter 8 Hypothesis Testing 1 Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim About a Proportion 8-5 Testing a Claim About a Mean: s Not Known 8-6 Testing

### Frequently Asked Questions Next Generation Sequencing

Frequently Asked Questions Next Generation Sequencing Import These Frequently Asked Questions for Next Generation Sequencing are some of the more common questions our customers ask. Questions are divided

### Search & Export Report Data

Search & Export Report Data Version: Draft 2 Abstract This document describes how to export the data from a saved report document. Document Revisions Version Date Description of Changes Draft 2 08/12/2005

### E205 Final: Version B

Name: Class: Date: E205 Final: Version B Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The owner of a local nightclub has recently surveyed a random

### QVALUE: The Manual Version 1.0

QVALUE: The Manual Version 1.0 Alan Dabney and John D. Storey Department of Biostatistics University of Washington Email: jstorey@u.washington.edu March 2003; Updated June 2003; Updated January 2004 Table

### CHAPTER 11 SECTION 2: INTRODUCTION TO HYPOTHESIS TESTING

CHAPTER 11 SECTION 2: INTRODUCTION TO HYPOTHESIS TESTING MULTIPLE CHOICE 56. In testing the hypotheses H 0 : µ = 50 vs. H 1 : µ 50, the following information is known: n = 64, = 53.5, and σ = 10. The standardized

### A Trial Analogy for Statistical. Hypothesis Testing. Legal Trial Begin with claim: Statistical Significance Test Hypotheses (statements)

A Trial Analogy for Statistical Slide 1 Hypothesis Testing Legal Trial Begin with claim: Smith is not guilty If this is rejected, we accept Smith is guilty reasonable doubt Present evidence (facts) Evaluate

### Two Correlated Proportions (McNemar Test)

Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with

### Introduction to SAGEnhaft

Introduction to SAGEnhaft Tim Beissbarth October 13, 2015 1 Overview Serial Analysis of Gene Expression (SAGE) is a gene expression profiling technique that estimates the abundance of thousands of gene

### Hypothesis. Testing Examples and Case Studies. Chapter 23. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.

Hypothesis Chapter 23 Testing Examples and Case Studies Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc. 23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis

### Package copa. R topics documented: August 9, 2016

Package August 9, 2016 Title Functions to perform cancer outlier profile analysis. Version 1.41.0 Date 2006-01-26 Author Maintainer COPA is a method to find genes that undergo

### How to Conduct a Hypothesis Test

How to Conduct a Hypothesis Test The idea of hypothesis testing is relatively straightforward. In various studies we observe certain events. We must ask, is the event due to chance alone, or is there some

: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

### HYPOTHESIS TESTING: POWER OF THE TEST

HYPOTHESIS TESTING: POWER OF THE TEST The first 6 steps of the 9-step test of hypothesis are called "the test". These steps are not dependent on the observed data values. When planning a research project,

### From Reads to Differentially Expressed Genes. The statistics of differential gene expression analysis using RNA-seq data

From Reads to Differentially Expressed Genes The statistics of differential gene expression analysis using RNA-seq data experimental design data collection modeling statistical testing biological heterogeneity

### Gene Expression Analysis

Gene Expression Analysis Jie Peng Department of Statistics University of California, Davis May 2012 RNA expression technologies High-throughput technologies to measure the expression levels of thousands

### Calculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation

Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.

### Hypothesis testing: Examples. AMS7, Spring 2012

Hypothesis testing: Examples AMS7, Spring 2012 Example 1: Testing a Claim about a Proportion Sect. 7.3, # 2: Survey of Drinking: In a Gallup survey, 1087 randomly selected adults were asked whether they

### 7. Data Packager: Sharing and Merging Data

7. Data Packager: Sharing and Merging Data Introduction The Epi Info Data Packager tool provides an easy way to share data with other users or to merge data collected by multiple users into a single database

### Section 13, Part 1 ANOVA. Analysis Of Variance

Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability

### THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7.

THERE ARE TWO WAYS TO DO HYPOTHESIS TESTING WITH STATCRUNCH: WITH SUMMARY DATA (AS IN EXAMPLE 7.17, PAGE 236, IN ROSNER); WITH THE ORIGINAL DATA (AS IN EXAMPLE 8.5, PAGE 301 IN ROSNER THAT USES DATA FROM

### Chapter Five: Paired Samples Methods 1/38

Chapter Five: Paired Samples Methods 1/38 5.1 Introduction 2/38 Introduction Paired data arise with some frequency in a variety of research contexts. Patients might have a particular type of laser surgery

### Shark Talent Management System Performance Reports

Shark Talent Management System Performance Reports Goals Reports Goal Details Report. Page 2 Goal Exception Report... Page 4 Goal Hierarchy Report. Page 6 Goal Progress Report.. Page 8 Goal Status Report...

About Hypothesis Testing TABLE OF CONTENTS About Hypothesis Testing... 1 What is a HYPOTHESIS TEST?... 1 Hypothesis Testing... 1 Hypothesis Testing... 1 Steps in Hypothesis Testing... 2 Steps in Hypothesis

### How to create and interpret the predictive analysis of a compound

How to create and interpret the predictive analysis of a compound Platform with suite of tools Predict & understand biological effects of small molecules & compounds Predict targets and metabolites, potential

### PASS Sample Size Software

Chapter 250 Introduction The Chi-square test is often used to test whether sets of frequencies or proportions follow certain patterns. The two most common instances are tests of goodness of fit using multinomial

### Chapter 9, Part A Hypothesis Tests. Learning objectives

Chapter 9, Part A Hypothesis Tests Slide 1 Learning objectives 1. Understand how to develop Null and Alternative Hypotheses 2. Understand Type I and Type II Errors 3. Able to do hypothesis test about population

### Gene expression analysis. Ulf Leser and Karin Zimmermann

Gene expression analysis Ulf Leser and Karin Zimmermann Ulf Leser: Bioinformatics, Wintersemester 2010/2011 1 Last lecture What are microarrays? - Biomolecular devices measuring the transcriptome of a

### Database Searching Tutorial/Exercises Jimmy Eng

Database Searching Tutorial/Exercises Jimmy Eng Use the PETUNIA interface to run a search and generate a pepxml file that is analyzed through the PepXML Viewer. This tutorial will walk you through the

### BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420

BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420 1. Which of the following will increase the value of the power in a statistical test

### Basic concepts and introduction to statistical inference

Basic concepts and introduction to statistical inference Anna Helga Jonsdottir Gunnar Stefansson Sigrun Helga Lund University of Iceland (UI) Basic concepts 1 / 19 A review of concepts Basic concepts Confidence

### DDBA 8438: Introduction to Hypothesis Testing Video Podcast Transcript

DDBA 8438: Introduction to Hypothesis Testing Video Podcast Transcript JENNIFER ANN MORROW: Welcome to "Introduction to Hypothesis Testing." My name is Dr. Jennifer Ann Morrow. In today's demonstration,

### Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

### Hypothesis Testing --- One Mean

Hypothesis Testing --- One Mean A hypothesis is simply a statement that something is true. Typically, there are two hypotheses in a hypothesis test: the null, and the alternative. Null Hypothesis The hypothesis

### Introduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.

Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative

### Mining Social Network Graphs

Mining Social Network Graphs Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata November 13, 17, 2014 Social Network No introduc+on required Really? We s7ll need to understand

### TRANSCRIPT: In this lecture, we will talk about both theoretical and applied concepts related to hypothesis testing.

This is Dr. Chumney. The focus of this lecture is hypothesis testing both what it is, how hypothesis tests are used, and how to conduct hypothesis tests. 1 In this lecture, we will talk about both theoretical

### Hypothesis testing. Hypothesis testing asks how unusual it is to get data that differ from the null hypothesis.

Hypothesis testing Hypothesis testing asks how unusual it is to get data that differ from the null hypothesis. If the data would be quite unlikely under H 0, we reject H 0. So we need to know how good

USING MYWEBSQL MyWebSQL is a database web administration tool that will be used during LIS 458 & CS 333. This document will provide the basic steps for you to become familiar with the application. 1. To

### Navigating Through SpamTitan

Navigating Through SpamTitan Table of Contents Access SpamTitan How to Create/Edit Whitelist Whitelist (Sender Domain) Whitelist (Sender E-mail) Whitelist (Import Text) How to Create/Edit Blacklist Blacklist

### IQ of deaf children example: Are the deaf children lower in IQ? Or are they average? If µ100 and σ 2 225, is the 88.07 from the sample of N59 deaf chi

PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 All inferential statistics have the following in common: Use of some descriptive statistic Use of probability Potential for estimation

### Analyzing microrna Data and Integrating mirna with Gene Expression Data in Partek Genomics Suite 6.6

Analyzing microrna Data and Integrating mirna with Gene Expression Data in Partek Genomics Suite 6.6 Overview This tutorial outlines how microrna data can be analyzed within Partek Genomics Suite. Additionally,

### How to Use Assets with Dashboards

HOW-TO GUIDE How to Use Assets with Dashboards SecurityCenter allows you to easily build customized dashboards using components that provide insight into the vulnerabilities and events occurring on your

### How Does My TI-84 Do That

How Does My TI-84 Do That A guide to using the TI-84 for statistics Austin Peay State University Clarksville, Tennessee How Does My TI-84 Do That A guide to using the TI-84 for statistics Table of Contents

### Multiple testing with gene expression array data

Multiple testing with gene expression array data Anja von Heydebreck Max Planck Institute for Molecular Genetics, Dept. Computational Molecular Biology, Berlin, Germany heydebre@molgen.mpg.de Slides partly

### Using Excel in Research. Hui Bian Office for Faculty Excellence

Using Excel in Research Hui Bian Office for Faculty Excellence Data entry in Excel Directly type information into the cells Enter data using Form Command: File > Options 2 Data entry in Excel Tool bar:

### Hypothesis Testing 1

Hypothesis Testing 1 Statistical procedures for addressing research questions involves formulating a concise statement of the hypothesis to be tested. The hypothesis to be tested is referred to as the

### Microsoft Office Visio Professional 2007 for IT. How to Use Visio for Project Management

Microsoft Office Visio Professional 2007 for IT How to Use Visio for Project Management Project Management Summary Use Visio 2007 with Project 2007 Diagram All Project Phases Planning Design Engineering

### Rapid alignment methods: FASTA and BLAST. p The biological problem p Search strategies p FASTA p BLAST

Rapid alignment methods: FASTA and BLAST p The biological problem p Search strategies p FASTA p BLAST 257 BLAST: Basic Local Alignment Search Tool p BLAST (Altschul et al., 1990) and its variants are some

### ED632G: Research/Applied Educational Psychology

1 ED632G: Research/Applied Educational Psychology This tutorial is designed to help ED632G students have a better understanding on how to run a general pre-test vs. posttest or improvement over semesters

### SPSS on two independent samples. Two sample test with proportions. Paired t-test (with more SPSS)

SPSS on two independent samples. Two sample test with proportions. Paired t-test (with more SPSS) State of the course address: The Final exam is Aug 9, 3:30pm 6:30pm in B9201 in the Burnaby Campus. (One

### An introduction to IBM SPSS Statistics

An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive

### EXCEL PIVOT TABLE David Geffen School of Medicine, UCLA Dean s Office Oct 2002

EXCEL PIVOT TABLE David Geffen School of Medicine, UCLA Dean s Office Oct 2002 Table of Contents Part I Creating a Pivot Table Excel Database......3 What is a Pivot Table...... 3 Creating Pivot Tables

### Using CrunchIt (http://bcs.whfreeman.com/crunchit/bps4e) or StatCrunch (www.calvin.edu/go/statcrunch)

Using CrunchIt (http://bcs.whfreeman.com/crunchit/bps4e) or StatCrunch (www.calvin.edu/go/statcrunch) 1. In general, this package is far easier to use than many statistical packages. Every so often, however,

### Non-Inferiority Tests for Two Proportions

Chapter 0 Non-Inferiority Tests for Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority and superiority tests in twosample designs in which

### Gene Expression Macro Version 1.1

QAD Usability Customization Demo Overview This demonstration focuses on one aspect of QAD Enterprise Applications Customization and shows how this functionality supports the vision of the Effective Enterprise;

### Descriptive Statistics

Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### Introduction to Hypothesis Testing

I. Terms, Concepts. Introduction to Hypothesis Testing A. In general, we do not know the true value of population parameters - they must be estimated. However, we do have hypotheses about what the true

### Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

### 9-3.4 Likelihood ratio test. Neyman-Pearson lemma

9-3.4 Likelihood ratio test Neyman-Pearson lemma 9-1 Hypothesis Testing 9-1.1 Statistical Hypotheses Statistical hypothesis testing and confidence interval estimation of parameters are the fundamental

### PANTHER User Manual. For PANTHER 9.0. Date: January 7, 2015. The PANTHER Team. Authors:

PANTHER User Manual For PANTHER 9.0 Date: January 7, 2015 Authors: The PANTHER Team Contents 1 Welcome to PANTHER System 1 1.1 About this document........... 1 1.2 How to cite PANTHER.......... 1 1.3 PANTHER