Stat 414 – Day 1 Introductions Review.

Slides:



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

Lecture 17: Tues., March 16 Inference for simple linear regression (Ch ) R2 statistic (Ch ) Association is not causation (Ch ) Next.
Stat 112: Lecture 7 Notes Homework 2: Due next Thursday The Multiple Linear Regression model (Chapter 4.1) Inferences from multiple regression analysis.
Lecture 28 Categorical variables: –Review of slides from lecture 27 (reprint of lecture 27 categorical variables slides with typos corrected) –Practice.
Inference for Regression
Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles.
January 7, afternoon session 1 Multi-factor ANOVA and Multiple Regression January 5-9, 2008 Beth Ayers.
Stat 512 – Lecture 18 Multiple Regression (Ch. 11)
Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review
Statistics 350 Lecture 1. Today Course outline Stuff Section
Lecture 24: Thurs. Dec. 4 Extra sum of squares F-tests (10.3) R-squared statistic (10.4.1) Residual plots (11.2) Influential observations (11.3,
Stat 217 – Week 10. Outline Exam 2 Lab 7 Questions on Chi-square, ANOVA, Regression  HW 7  Lab 8 Notes for Thursday’s lab Notes for final exam Notes.
Statistics 350 Lecture 10. Today Last Day: Start Chapter 3 Today: Section 3.8 Homework #3: Chapter 2 Problems (page 89-99): 13, 16,55, 56 Due: February.
What Is Multivariate Analysis of Variance (MANOVA)?
Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Stat 112: Notes 2 This class: Start Section 3.3. Thursday’s class: Finish Section 3.3. I will and post on the web site the first homework tonight.
Lecture 0 Introduction. Course Information Your instructor: – Hyunseung (pronounced Hun-Sung) – Or HK (not Hong Kong ) –
Inference for regression - Simple linear regression
Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test.
Interval Estimation for Means Notes of STAT6205 by Dr. Fan.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
Brain Mapping Unit The General Linear Model A Basic Introduction Roger Tait
Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.
EQT 373 Chapter 3 Simple Linear Regression. EQT 373 Learning Objectives In this chapter, you learn: How to use regression analysis to predict the value.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
Quantitative Methods in Geography Geography 391. Introductions and Questions What (and when) was the last math class you had? Have you had statistics.
Lecture 8 Simple Linear Regression (cont.). Section Objectives: Statistical model for linear regression Data for simple linear regression Estimation.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Stat 112: Notes 2 Today’s class: Section 3.3. –Full description of simple linear regression model. –Checking the assumptions of the simple linear regression.
Analysis of Covariance Combines linear regression and ANOVA Can be used to compare g treatments, after controlling for quantitative factor believed to.
ECON 338/ENVR 305 CLICKER QUESTIONS Statistics – Question Set #8 (from Chapter 10)
Ledolter & Hogg: Applied Statistics Section 6.2: Other Inferences in One-Factor Experiments (ANOVA, continued) 1.
Introduction to Basic Statistical Tools for Research OCED 5443 Interpreting Research in OCED Dr. Ausburn OCED 5443 Interpreting Research in OCED Dr. Ausburn.
28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.
Psychology 202a Advanced Psychological Statistics November 12, 2015.
Chapter 12 Inference for Linear Regression. Reminder of Linear Regression First thing you should do is examine your data… First thing you should do is.
“GLMrous designs” “GLMrous designs” “Are you regressed or something?” “Pseudonyms & aliases” “Pseudonyms & aliases” Models I Models II.
Stat 112 Notes 14 Assessing the assumptions of the multiple regression model and remedies when assumptions are not met (Chapter 6).
ANCOVA (adding covariate) MANOVA (adding more DVs) MANCOVA (adding DVs and covariates) Group Differences: other situations…
Chapter 13 Lesson 13.2a Simple Linear Regression and Correlation: Inferential Methods 13.2: Inferences About the Slope of the Population Regression Line.
Chapter 13 Lesson 13.2a Simple Linear Regression and Correlation: Inferential Methods 13.2: Inferences About the Slope of the Population Regression Line.
Criteria Rollout Meeting October 30, 2016
Chapter 13 Simple Linear Regression
Statistics 350 Lecture 4.
MATH Instructor: Dr. Saralees Nadarajah
BUSI 410 Business Analytics
Simple Linear Regression - Introduction
Data Analysis for Managers
Regression model Y represents a value of the response variable.
Statistics and Data Analysis
Stat 112 Notes 4 Today: Review of p-values for one-sided tests
CHAPTER 29: Multiple Regression*
Stat 217 – Day 28 Review Stat 217.
Regression Models - Introduction
Simple Linear Regression
Simple Linear Regression
Sampling Distributions
Stat Introduction to Statistical Concepts and Methods
Wrap-up and Course Review
Chapter 4, Regression Diagnostics Detection of Model Violation
ANOVA family Statistic’s name “Groups” DVs (which means are calculated for the groups) t-test one IV (binomial) one DV (I/R) F-test one IV (nominal) one.
MA171 Introduction to Probability and Statistics
Multivariate Analysis: Analysis of Variance
Sampling Distribution Models
Cases. Simple Regression Linear Multiple Regression.
Regression Models - Introduction
Multivariate Analysis: Analysis of Variance
STAT 515 Statistical Methods I Lecture 1 August 22, 2019 Originally prepared by Brian Habing Department of Statistics University of South Carolina.
Presentation transcript:

Stat 414 – Day 1 Introductions Review

Multilevel models Multilevel data Modelling Technical and Nontechnical communication Applications Software

Stat 414 Syllabus JMP vs. R vs. other Textbook Handouts Homework (due date) Labs Practice quizzes Office hours Project

Day 1 handout Discuss (a)-(f) with a partner

Statistical Modelling General Linear Model (Data = Model + Error) Simple linear regression Multiple regression t-tests ANOVA ANCOVA MANOVA MANCOVA Factor analysis Discriminant analysis

LINE assumptions for inference (L) The mean value for Y at each level of X falls on the regression line. (I) We’ll need to check the design of the study to determine if the errors (distances from the line) are independent of one another. (N) At each level of X, the values for Y are normally distributed. (E) The spread in the Y’s for each level of X is the same.

Discuss Example 1 with partner Will take some speculation Practice translating the conditions into the particular context, e.g., L: The mean reaction time is linearly related to decibel level of the music. I: Stopping distances are independent. The random selection of drivers should assure independence. N: The stopping distances for a given decibel level of music vary and are normally distributed. E: The variation in stopping distances should be approximately the same for each decibel level of music.

For next time Initial survey (Mon) Readings Install JMP and/or R Overview article Ch. 1 Ch. 2 Install JMP and/or R Start HW 1 Initial course survey (Monday) Will need to remember/figure out some software steps