Overall agenda Part 1 and 2  Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data -measures.

Slides:

Advertisements

Similar presentations

6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.

Advertisements

Departments of Medicine and Biostatistics

Linear regression models

Objectives (BPS chapter 24)

1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.

Statistical presentation in international scientific publications 6. Reporting more complicated findings Malcolm Campbell Lecturer in Statistics, School.

Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.

Programme in Statistics (Courses and Contents). Elementary Probability and Statistics (I) 3(2+1)Stat. 101 College of Science, Computer Science, Education.

Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review

Final Review Session.

Correlation. Two variables: Which test? X Y Contingency analysis t-test Logistic regression Correlation Regression.

Chapter 19 Data Analysis Overview

13-1 Designing Engineering Experiments Every experiment involves a sequence of activities: Conjecture – the original hypothesis that motivates the.

Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.

Summary of Quantitative Analysis Neuman and Robson Ch. 11

Simple Linear Regression and Correlation

Simple Linear Regression Analysis

Multivariate Probability Distributions. Multivariate Random Variables In many settings, we are interested in 2 or more characteristics observed in experiments.

Generalized Linear Models

13 Design and Analysis of Single-Factor Experiments:

Chapter 9 Two-Sample Tests Part II: Introduction to Hypothesis Testing Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social & Behavioral.

Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.

Chapter 13: Inference in Regression

Chapter 11 Simple Regression

5-1 Introduction 5-2 Inference on the Means of Two Populations, Variances Known Assumptions.

Simple Linear Regression

1 - 1 Module 1: Introduction This module describes the purpose of the course and the general approach followed for addressing this purpose. Reviewed 15.

COMM 250 Agenda - Week 12 Housekeeping RP2 Due Wed. RAT 5 – Wed. (FBK 12, 13) Lecture Experiments Descriptive and Inferential Statistics.

● Final exam Wednesday, 6/10, 11:30-2:30. ● Bring your own blue books ● Closed book. Calculators and 2-page cheat sheet allowed. No cell phone/computer.

Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.

Statistics for the Terrified Talk 4: Analysis of Clinical Trial data 30 th September 2010 Janet Dunn Louise Hiller.

STA 286 week 131 Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression.

Simple Linear Regression (SLR)

Chapter 6 Simple Regression Introduction Fundamental questions – Is there a relationship between two random variables and how strong is it? – Can.

Inferential Statistics. The Logic of Inferential Statistics Makes inferences about a population from a sample Makes inferences about a population from.

Review Lecture 51 Tue, Dec 13, Chapter 1 Sections 1.1 – 1.4. Sections 1.1 – 1.4. Be familiar with the language and principles of hypothesis testing.

Introduction to Basic Statistical Tools for Research OCED 5443 Interpreting Research in OCED Dr. Ausburn OCED 5443 Interpreting Research in OCED Dr. Ausburn.

Chap 18-1 Copyright ©2012 Pearson Education, Inc. publishing as Prentice Hall Chap 18-1 Chapter 18 A Roadmap for Analyzing Data Basic Business Statistics.

STATISTICS AND OPTIMIZATION Dr. Asawer A. Alwasiti.

Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.

Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 11: Models Marshall University Genomics Core Facility.

Recapitulation! Statistics 515. What Have We Covered? Elements Variables and Populations Parameters Samples Sample Statistics Population Distributions.

Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.

Beginning Statistics Table of Contents HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant Systems, Inc.

1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression Chapter 14.

Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.

Marginal Distribution Conditional Distribution. Side by Side Bar Graph Segmented Bar Graph Dotplot Stemplot Histogram.

Statistics and probability Dr. Khaled Ismael Almghari Phone No:

INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.

Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 16 : Summary Marshall University Genomics Core Facility.

Chapter 18 Data Analysis Overview Yandell – Econ 216 Chap 18-1.

Review 1. Describing variables.

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

Inference in Simple Linear Regression

Chapter Six Normal Curves and Sampling Probability Distributions

Multiple Regression and Model Building

Generalized Linear Models

Part Three. Data Analysis

SA3202 Statistical Methods for Social Sciences

BA 275 Quantitative Business Methods

CHAPTER 29: Multiple Regression*

STAT 5372: Experimental Statistics

Sihua Peng, PhD Shanghai Ocean University

Statistical Thinking and Applications

Introductory Statistics

Presentation transcript:

Overall agenda Part 1 and 2  Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data -measures of location and variation -exploring data -types of data population and sample  Part 2: Statistical Distributions concepts important distributions: -Normal distribution and related -other important continuous distributions (life-time and survival distributions) -discrete distributions (binomial, Poisson, count data) 1| Introduction to Biostatistics | Ekkehard Glimm | 9/10 December 2013 | Part 1

Overall agenda Part 3 and 4  Part 3: Estimation and confidence intervals point estimates interval estimates point and interval estimates for various types of data prediction intervals  Part 4: Hypothesis testing Concept important statistical tests: -t-tests (one/paired sample, two sample with and without equal variances) -F-tests and ANOVA -testing parameters in more general models -testing hypotheses about count and binomial data 2| Introduction to Biostatistics | Ekkehard Glimm | 9/10 December 2013 | Part 1

Overall agenda Part 5 and 6  Part 5: Regression and correlation method of least squares and simple linear regression Multiple linear regression correlation extensions: ANOVA as regression; logistic, Poisson and non-linear regression  Part 6: design of experiments and sample size calculations types of experiments: clinical trials, observational studies, other planned experiments (parallel group designs and cross-over designs, factorial experiments) principles of experimentation: blinding, randomization, stratification operating characteristics and sample size assessment 3| Introduction to Biostatistics | Ekkehard Glimm | 9/10 December 2013 | Part 1

Overall agenda Part 7 and 8  Part 7: time-to-event analysis why this is special and important: survival times and censoring Kaplan-Meier curve hazard ratio the log-rank test extensions: Cox regression  Part 8: Statistics in clinical trials phases of Clinical Development: -pre-clinical research and transition Preclinical-Clinical -clinical Development Phase I -clinical development: Phases II-IV describing statistical methods and reporting statistical results 4| Introduction to Biostatistics | Ekkehard Glimm | 9/10 December 2013 | Part 1