“GLMrous designs” “GLMrous designs” “Are you regressed or something?” “Pseudonyms & aliases” “Pseudonyms & aliases” Models I Models II.

Slides:



Advertisements
Similar presentations
Agenda of Week VII Review of Week VI Multiple regression Canonical correlation.
Advertisements

Overview of Techniques Case 1 Independent Variable is Groups, or Conditions Dependent Variable is continuous ( ) One sample: Z-test or t-test Two samples:
SI0030 Social Research Methods Week 6 Luke Sloan
Topic 12: Multiple Linear Regression
StatisticalDesign&ModelsValidation. Introduction.
1 1 Chapter 5: Multiple Regression 5.1 Fitting a Multiple Regression Model 5.2 Fitting a Multiple Regression Model with Interactions 5.3 Generating and.
Test practice Multiplication. Multiplication 9x2.
DESCRIPTORES O PALABRAS CLAVE. Diseño de dos grupos al azar experimental design random group/s two-group design independent-samples t-student t-test Mann-Whitney.
Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles.
Part V The Generalized Linear Model Chapter 16 Introduction.
FACTORIAL ANOVA.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.
Matrix Approach to Simple Linear Regression KNNL – Chapter 5.
Introduction to Multilevel Modeling Using SPSS
“I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it” Lord William Thomson, 1st.
Overall agenda Part 1 and 2  Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data -measures.
Biostatistics Case Studies 2015 Youngju Pak, PhD. Biostatistician Session 4: Regression Models and Multivariate Analyses.
Statistics Definition Methods of organizing and analyzing quantitative data Types Descriptive statistics –Central tendency, variability, etc. Inferential.
Introduction Multilevel Analysis
ALISON BOWLING THE GENERAL LINEAR MODEL. ALTERNATIVE EXPRESSION OF THE MODEL.
Introduction to Statistics Harry R. Erwin, PhD School of Computing and Technology University of Sunderland.
Multilevel Modeling Software Wayne Osgood Crime, Law & Justice Program Department of Sociology.
Multiple Regression BPS chapter 28 © 2006 W.H. Freeman and Company.
Analysis of Covariance (ANCOVA)
Chapter 6 Simple Regression Introduction Fundamental questions – Is there a relationship between two random variables and how strong is it? – Can.
ANALYSIS PLAN: STATISTICAL PROCEDURES
General Linear Model.
Psychology 202a Advanced Psychological Statistics November 12, 2015.
Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.
Instructor: Dr. Amery Wu
Advanced Correlation D/RS 1013 Research Questions and Associated Techniques.
ReCap Part II (Chapters 5,6,7) Data equations summarize pattern in data as a series of parameters (means, slopes). Frequency distributions, a key concept.
Problem What if we want to model: –Changes in a group over time Group by time slices (month, year) –Herd behavior of multiple species in the same area.
Types of Inheritance in C++. In C++ we have 5 different types of inheritance: – Single Inheritance – Multiple Inheritance – Hierarchical Inheritance –
STA302: Regression Analysis. Statistics Objective: To draw reasonable conclusions from noisy numerical data Entry point: Study relationships between variables.
ANCOVA.
Introduction to Multilevel Analysis Presented by Vijay Pillai.
ANCOVA (adding covariate) MANOVA (adding more DVs) MANCOVA (adding DVs and covariates) Group Differences: other situations…
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 18 Multivariate Statistics.
Return To Index Excel Microsoft Excel Basics Lesson 23 Simple Linear Regression An ExampleAn Example - 2 The Regression Steps - 3 The Regression.
Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.
基 督 再 來 (一). 經文: 1 你們心裡不要憂愁;你們信神,也當信我。 2 在我父的家裡有許多住處;若是沒有,我就早 已告訴你們了。我去原是為你們預備地去 。 3 我 若去為你們預備了地方,就必再來接你們到我那 裡去,我在 那裡,叫你們也在那裡, ] ( 約 14 : 1-3)
NURS 306, Nursing Research Lisa Broughton, MSN, RN, CCRN RESEARCH STATISTICS.
Methods of Presenting and Interpreting Information Class 9.
Causality, Null Hypothesis Testing, and Bivariate Analysis
Multivariate Analysis
Part Three. Data Analysis
STA 282 Introduction to Statistics
Random Coefficients Regression
Linear Regression.
Stat 414 – Day 1 Introductions Review.
Brief Introduction to Multilevel Analysis
ELEMENTS OF HIERARCHICAL REGRESSION LINEAR MODELS
Слайд-дәріс Қарағанды мемлекеттік техникалық университеті
.. -"""--..J '. / /I/I =---=-- -, _ --, _ = :;:.
Simple Linear Regression
II //II // \ Others Q.
I1I1 a 1·1,.,.,,I.,,I · I 1··n I J,-·
Comparisons Among Treatments/Groups
EQUATION 4.1 Relationship Between One Dependent and One Independent Variable: Simple Regression Analysis.
Predicted microbiota age against the actual age of visitors and villagers. Predicted microbiota age against the actual age of visitors and villagers. (A)
Mixed Up Multiplication Challenge
An Introductory Tutorial
Cases. Simple Regression Linear Multiple Regression.
Chapter 14 Multiple Regression
. '. '. I;.,, - - "!' - -·-·,Ii '.....,,......, -,
—ROC curves for each simple test compared with NCS (gold standard) plotting the sensitivity versus 1-specificity (the false-positive rate) for different.
Regression and Correlation of Data
2k Factorial Design k=2 Ex:.
Presentation transcript:

“GLMrous designs” “GLMrous designs” “Are you regressed or something?” “Pseudonyms & aliases” “Pseudonyms & aliases” Models I Models II

A. ANOVA B. Regression C. ANCOVA D. Multiple Regression E. Logistic Regression “GLMrous designs”

A. Linear regression B. Simple regression C. Multiple regression D. Multilevel regression E. Multivariate regression “Are you regressed or something?”

A. Mixed B. Random Coefficient C. Latent Curve D. Multilevel E. Hierarchical “Pseudonyms & aliases”

NULL: Models I

Models II