Spring 2006MSTI 130-041 Daily Notes for MSTI 130 Spring 2006 Dr. Kris Green TR 11:00 – 12:20, K059.

Slides:



Advertisements
Similar presentations
Regression Analysis Once a linear relationship is defined, the independent variable can be used to forecast the dependent variable. Y ^ = bo + bX bo is.
Advertisements

Correlation and Regression
July 1, 2008Lecture 17 - Regression Testing1 Testing Relationships between Variables Statistics Lecture 17.
Some Terms Y =  o +  1 X Regression of Y on X Regress Y on X X called independent variable or predictor variable or covariate or factor Which factors.
Chapter 13 Multiple Regression
November 24, Transformations MGTSC 312: Lab 9.
Chapter 12 Multiple Regression
Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review
The Simple Regression Model
Statistics 350 Lecture 1. Today Course outline Stuff Section
Examining Relationship of Variables  Response (dependent) variable - measures the outcome of a study.  Explanatory (Independent) variable - explains.
Stat 217 – Day 26 Regression, cont.. Last Time – Two quantitative variables Graphical summary  Scatterplot: direction, form (linear?), strength Numerical.
Chapter Topics Types of Regression Models
Chapter 11 Multiple Regression.
Simple Linear Regression Analysis
Stat 217 – Day 25 Regression. Last Time - ANOVA When?  Comparing 2 or means (one categorical and one quantitative variable) Research question  Null.
1 1 Slide © 2003 South-Western/Thomson Learning™ Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Correlation and Regression Analysis
Chapter 12 Section 1 Inference for Linear Regression.
1 Chapter 10 Correlation and Regression We deal with two variables, x and y. Main goal: Investigate how x and y are related, or correlated; how much they.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.
Statistics for the Social Sciences Psychology 340 Fall 2013 Thursday, November 21 Review for Exam #4.
Chapter 13: Inference in Regression
Correlation and Linear Regression
Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.
Statistics for Decision Making Bivariate Descriptive Statistics QM Fall 2003 Instructor: John Seydel, Ph.D.
June 3, 2008Stat Lecture 5 - Correlation1 Exploring Data Numerical Summaries of Relationships between Statistics Lecture 5.
1 Chapter 3: Examining Relationships 3.1Scatterplots 3.2Correlation 3.3Least-Squares Regression.
BPS - 3rd Ed. Chapter 211 Inference for Regression.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Least-Squares Regression Section 3.3. Why Create a Model? There are two reasons to create a mathematical model for a set of bivariate data. To predict.
Forecasting Techniques: Single Equation Regressions Su, Chapter 10, section III.
Exploring Engineering Chapter 3, Part 2 Introduction to Spreadsheets.
Basic Concepts of Correlation. Definition A correlation exists between two variables when the values of one are somehow associated with the values of.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Multiple Regression Petter Mostad Review: Simple linear regression We define a model where are independent (normally distributed) with equal.
Chapter 13 Multiple Regression
Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.
Agresti/Franklin Statistics, 1 of 88 Chapter 11 Analyzing Association Between Quantitative Variables: Regression Analysis Learn…. To use regression analysis.
Chapter 22: Building Multiple Regression Models Generalization of univariate linear regression models. One unit of data with a value of dependent variable.
Applied Quantitative Analysis and Practices LECTURE#25 By Dr. Osman Sadiq Paracha.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Chapter 14: Inference for Regression. A brief review of chapter 4... (Regression Analysis: Exploring Association BetweenVariables )  Bi-variate data.
June 30, 2008Stat Lecture 16 - Regression1 Inference for relationships between variables Statistics Lecture 16.
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.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Chapter 10 Correlation and Regression 10-2 Correlation 10-3 Regression.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 12 More About Regression 12.1 Inference for.
Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
Stats Midterm Chapters: 4, 5, 8, 9, Vocab Questions / 31 terms / 23 terms are used / 8 are not used. 6 pages / 40 questions / 43 points.
Multiple Regression Learning Objectives n Explain the Linear Multiple Regression Model n Interpret Linear Multiple Regression Computer Output n Test.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Lecture Slides Elementary Statistics Tenth Edition and the.
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.
BPS - 5th Ed. Chapter 231 Inference for Regression.
Slide Slide 1 Chapter 10 Correlation and Regression 10-1 Overview 10-2 Correlation 10-3 Regression 10-4 Variation and Prediction Intervals 10-5 Multiple.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 12 More About Regression 12.1 Inference for.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Howard Community College
Lecture Slides Elementary Statistics Twelfth Edition
Chapter 14 Introduction to Multiple Regression
Chapter 20 Linear and Multiple Regression
Simple Linear Regression
Inference for Regression (Chapter 14) A.P. Stats Review Topic #3
Statistics 200 Lecture #5 Tuesday, September 6, 2016
Lecture Slides Elementary Statistics Thirteenth Edition
CHAPTER 29: Multiple Regression*
Chapter 10 Correlation and Regression
Section 3.2: Least Squares Regressions
A medical researcher wishes to determine how the dosage (in mg) of a drug affects the heart rate of the patient. Find the correlation coefficient & interpret.
Created by Erin Hodgess, Houston, Texas
Introduction to Regression Modeling
Presentation transcript:

Spring 2006MSTI Daily Notes for MSTI 130 Spring 2006 Dr. Kris Green TR 11:00 – 12:20, K059

Spring 2006MSTI Day 01 – Thurs. 01/12 Cancelled – Joint Math Meetings in San Antonio Posted the announcement below: –Mon, Jan 09, Missing First Class Due to a conference, Dr. Green will be out of town during the first class meeting of MST 130 (section 4). Thus, class on Thursday (11-12:20) is cancelled. You should, however, download the syllabus (under "Course Information") and review it before class on Tuesday so that you can ask questions; there will be an online quiz about the syllabus, so be sure that you read it thoroughly. In class on Tuesday, we will begin chapter 1 of the text (available in the bookstore) so it would be good to read chapter 1 as well.

Spring 2006MSTI Day 02 – Tues. 01/17 Discuss syllabus (online, should have read) Download and submit survey –At same time, take pictures of students in groups of 3 –Discuss proper naming of files (LastFI HW00) and point out errors as they occur Remind about quiz 1 Overview of unit 1, seeing the world as data Chapter 1 – Problem Solving, Section A Why Data? Work exploration 1A in class after discussion (in small groups) of examples

Spring 2006MSTI Day 03 – Thurs. 01/19 Quiz reminders (quiz 2 due Tues.) [3] HW 1 – memo (pg. 37-8) – due 1/26 [2] Discuss “Beef n’ Buns” examples [40] Complete Exploration (pg. 34-4) [25 – group; use a recorder to type notes and send to the rest of the group] Read and discuss memo problem [20]

Spring 2006MSTI Class 04 – Tues. 01/24 Mistakes on quizzes 1 and 2 Definitions and terms (methods of collecting data vs. types of data) Units for numerical data Exploration 2A Questions about chapter one memo Computer skills – starting up Word/Excel and screen navigation

Spring 2006MSTI Class 05 – Thurs. 01/26 Administrivia –How to get feedback on homework –Homework 2 –Download and install StatPro at home/dorm Pull up C02 Over Easy.xls –Examples 2B1 and 2 –Coding data and using comments in Excel Create two spreadsheets for Cruise Line –One observational, one survey-related

Spring 2006MSTI Class 06 – Tues. 01/31 Copying and pasting into Word from Excel Review of quiz questions Discuss HW 01 feedback Ex 3A3 “C03 Tots.xls” – calculate stats using Excel and StatPro Exploration 3A using Excel

Spring 2006MSTI Class 07 – Thurs. 02/02 Algebra and means –Talk about relative vs. absolute cell refs and set up a spreadsheet allow these to be input and to measure the result fixed change and percent change in the mean salary –Using exploration 3A, make a table Using StatPro to generate tables of statistics and explore: exploration 3B More means and quartiles discussion: some of the HW problems? Examples?

Spring 2006MSTI Class 08 – Tues. 02/06 In class quiz: Chapter 3 HW #4 (30 min) – turned into a class discussion/exercise Standard deviation development (50 min) –Measuring spread –Deviations, and sum of dev = 0 –Fixing this problem with standard deviation –Degrees of freedom

Spring 2006MSTI Class 09 – Thurs. 02/09 Q&A about homework and activities Example 4A3 (20 min) –Use Excel to compute averages, medians, standard deviations Exploration 4A (15 min) – discussion only Definitions (10 min) –z-scores –rules of thumb Example 4B3 (15 min) – collected, no analysis –Sorting the data by Z-scores helps

Spring 2006MSTI Class 10 – Tues. 02/14 Example 4B3 discussion (10 min) Exploration 4B (20 min) Memo 4 in class (30 min) Homework 04 questions (20 min)

Spring 2006MSTI Class 11 – Thurs. 02/16 Examples from 5A –Reading histograms (5A1) [5min] –Checking rules of thumb (5A2) [5 min] –Discuss bad histograms (5A3) [10 min] Making histograms (C05 BeefnBuns 2) –From data [10 min] –From z-scores [15 min] Exploration 5A and using histograms to infer about the problem context [30 min]

Spring 2006MSTI Class 12 – Tues. 02/21 Exam Review –Format of the exam and topics for the exam –Problem 1. Let’s more seriously discuss and investigate HW 4, #2 about the countries Rating them as “average” – compared to what? What’s constitutes a large std. deviation? –Problem 2. Continued analysis of the cruise ship problem (page 114)

Spring 2006MSTI Class 13 Midterm Exam

Spring 2006MSTI Class 14 – Tues. 02/28 Return midterm Discuss scatterplots –Independent (explanatory) and Dependent (response) –Reading them, axes, making them Correlation –Two variables and z-scores –Matrix of correlations and strength of relationships

Spring 2006MSTI Class 15 – Thurs. 03/02 Correlation visualization exercise (Exp 6A) Line Fitting Exercise (Exp 6B) –Put worksheet on Bb to fill in Discussion of slope, y-intercept, making trendlines, R^2

Spring 2006MSTI Class 16 – Tues. 03/14 Simple regression (section 7A) –Reading the equation of the line –R^2 and S_e –Diagnostic graphs (fitted vs actual, etc.) Example 7A1, 7A2, 7A3 HW 7 due date moved to Thursday Quiz 7 due Thursday before class

Spring 2006MSTI Class 17 – Thurs. 03/16 Proportionality and what that means Exploration 7A to be handed in electronically –Add in the following Interpret y (or x) intercepts for each model Explain quality of each model using R^2 Explain accuracy of each model’s predictions using S_e (and compare to std. dev. Of y)

Spring 2006MSTI Class 18 – 03/21 Exploration 8A and making multiple regression models Interpretation of the models Adjusted R^2 Other examples in section 8A

Spring 2006MSTI Class 19 – Tues. 03/28 More on Multiple regression Example 8A1, 8A2, 8A3 –P-values –Adjusted R^2 –Fitted vs. Actual diagnostic graph –Residuals vs. Fitted diagnostic graph Homework #4 – page 222

Spring 2006MSTI Class 20 – Thurs. 03/30 Dummy Variables in Regression –Exploration 8B (page 218) –Discuss parts together (use Example 8B1 to get the models for Exploration 8B started) If time, discuss memo 8 as an example Homework will be the problems (1-4) on pages , due next Thursday in class; note error in problem 2 statement

Spring 2006MSTI Class 21 – Tues. 04/04 Assumptions about regression (15 min) –Anscombe data –Linearity –Randomness in the residuals Review of regression models (15 min) Overview of nonlinear models (50 min) –What makes them different? –Examples (Exploration2.xls)

Spring 2006MSTI Class 22 – Thurs. 04/06 Notation for functions y = f(x) Parameters vs. variables Comparison of functions (10A2 and 3) Graphing these in Excel Shifts and scalings and reflections Exp 10B) –Notation Power Functions (incl. recip/square/sqrt)

Spring 2006MSTI Class 23 – Tues. 04/11 Continue analyzing the shifts, scales, etc. from last Thursday’s class Set up homework 10 (memo, pp ) Administrivia –HW 10 due Next Tuesday –Rework of 8 due Next Thursday –HW 11 – TBA –Quiz 11 due Tuesday by 11

Spring 2006MSTI Class 24 – Thurs. 04/13 Continue working on and discussing nonlinear fits by shifting and scaling –HW 09 (chapter 10 memo) –Calculating R^2

Spring 2006MSTI Class 25 – Tues. 04/18 Homework 10 – due Tuesday, Apr. 25 –Problems on pp –Memo, pg. 301 Overview of the semester – notes below Interpretation of nonlinear models Course Evaluations

Spring 2006MSTI Class 25 – Thurs. 04/20 Continue with nonlinear regression –Total change vs. Rate of change –Marginal Analysis and Parameter Analysis –Diagnostic graphs –Transformations of data to linearize (p. 285) Multiplicative Models Polynomial Models Don’t worry about calculating Se and R^2