week 1 introduc on
play

Week 1: Introduc/on Random errors 2 1.2B Random errors - PowerPoint PPT Presentation

Sep 18, 2023 •259 likes •398 views

1 Week 1: Introduc/on Random errors 2 1.2B Random errors 3 Laser distance measurement Repeated individual measurements mean = truth Width measurement no. 4 Random errors where

  1. 1 ¡ Week ¡1: ¡Introduc/on ¡ Random ¡errors ¡

  2. 2 ¡ 1.2B ¡ Random ¡errors

  3. 3 ¡ Laser ¡distance ¡measurement Repeated individual measurements mean = truth Width measurement no.

  4. 4 ¡ Random ¡errors where ¡ ¡ i th measurement ¡ Place ¡image ¡credits ¡here mean of measurements

  5. 5 ¡ Laser ¡distance ¡measurement Repeated individual measurements mean = truth Width measurement no.

  6. 6 ¡ Histogram ¡of ¡random ¡errors Density Random error 0

  7. 7 ¡ Histogram ¡of ¡random ¡errors Density Random error i j

  8. 8 ¡ Histogram ¡of ¡random ¡errors high precision Density 0 low precision 0 Random error

  9. 9 ¡ INTERMEZZO: ¡Normal ¡distribu>on Density

  10. 10 ¡ INTERMEZZO: ¡Normal ¡distribu>on Density 68.3%

  11. 11 ¡ Histogram ¡of ¡random ¡errors Density 0 0 Random error

  12. 12 ¡ Histogram ¡of ¡random ¡errors high precision 68.3% Density 0 low precision 68.3% 0 Random error

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Towards an integrated bioprocess Introduc)on to week 5 Prof.

Towards an integrated bioprocess Introduc)on to week 5 Prof.

Towards an integrated bioprocess Introduc)on to week 5 Prof. Luuk van der Wielen, Department of Biotechnology, Faculty of Applied Sciences An integrated

345 views • 17 slides
Introduc)on 1 Introduc)on Founda)on Drilling Agriculture Mining

Introduc)on 1 Introduc)on Founda)on Drilling Agriculture Mining

Introduc)on 1 Introduc)on Founda)on Drilling Agriculture Mining Recycling Construc)on Road Building Steel Produc)on Dredging Forging Logging Energy/Power

458 views • 16 slides
Week 1: Introduc/on Precision and covariance matrix 2 1.2C

Week 1: Introduc/on Precision and covariance matrix 2 1.2C

1 Week 1: Introduc/on Precision and covariance matrix 2 1.2C Precision and covariance matrix 3 Histogram of random errors high precision Density 0 low precision 0 Random

308 views • 13 slides
An Introduc+on to the Pharmaceu+cal Industry? By James

An Introduc+on to the Pharmaceu+cal Industry? By James

An Introduc+on to the Pharmaceu+cal Industry? By James McDermo9 Contents Introduc+on Drug Development Lifecycle The Patent Clock The

271 views • 23 slides
Introduc)on to Distributed Systems Arvind Krishnamurthy Todays Lecture Introduc)on

Introduc)on to Distributed Systems Arvind Krishnamurthy Todays Lecture Introduc)on

Introduc)on to Distributed Systems Arvind Krishnamurthy Todays Lecture Introduc)on Course details RPCs Primary-backup systems (start discussion) Distributed Systems are everywhere! Some of the most powerful services are

539 views • 53 slides
Introduc Introduc Intr troduction to oducti tion t tion to Digi n to Di Digit gital I

Introduc Introduc Intr troduction to oducti tion t tion to Digi n to Di Digit gital I

E E LE LE 882 882 Introduc Introduc Intr troduction to oducti tion t tion to Digi n to Di Digit gital I ital Im tal Image P l Image age Pr e Proc Process ocessin ocessing essing ing Course Instructor: Prof. L ing Gua n

358 views • 22 slides
Introduc)on to the Analysis of RNA-seq Data Lecture

Introduc)on to the Analysis of RNA-seq Data Lecture

Introduc)on to the Analysis of RNA-seq Data Lecture 9: September 18, 2012 Introduc)on What is RNA-seq? RNA-seq refers to the method

492 views • 34 slides
Introduc)on to Chroma)n IP sequencing (ChIP-seq) data

Introduc)on to Chroma)n IP sequencing (ChIP-seq) data

Introduc)on to Chroma)n IP sequencing (ChIP-seq) data analysis Introduc)on to Bioinforma)cs Using NGS Data 27 January 2016 Agata Smialowska BILS,

1.07k views • 69 slides
Introduc)on to Chroma)n IP sequencing (ChIP-seq) data

Introduc)on to Chroma)n IP sequencing (ChIP-seq) data

Introduc)on to Chroma)n IP sequencing (ChIP-seq) data analysis Introduc)on to Bioinforma)cs using NGS data Linkping, 21 April 2016 Agata

947 views • 78 slides
Introduc)on to Sta)s)cs 02-223 How to Analyze Your Own

Introduc)on to Sta)s)cs 02-223 How to Analyze Your Own

Introduc)on to Sta)s)cs 02-223 How to Analyze Your Own Genome Fall 2013 Why Use Sta)s)cs? Anecdotal evidence is unreliable Why does the phone

367 views • 20 slides
Time Matters Week 7 Week 6 Prototyping + Needfinding Week 7 Week 8 Implementation Week 9

Time Matters Week 7 Week 6 Prototyping + Needfinding Week 7 Week 8 Implementation Week 9

Time Matters Week 7 Week 6 Prototyping + Needfinding Week 7 Week 8 Implementation Week 9 Week 10 User Testing + Debugging + Final Prep User Experience http://cogs121.ucsd.edu/schedule/ UX: Interaction Cost Interaction cost: any

415 views • 18 slides
Introduc)on We are going to look at how change occurs in

Introduc)on We are going to look at how change occurs in

Introduc)on We are going to look at how change occurs in a large, sexually reproducing popula)on: mountain sheep. Well be developing ideas that

402 views • 13 slides
Introduc*on Our Mission & What we do Our Core

Introduc*on Our Mission & What we do Our Core

OUTLINE Introduc*on Our Mission & What we do Our Core Areas Partners Geographical & Sector Distribu*on RMF In-Depth INTRODUCTION

364 views • 34 slides
Introduc=on: The Big Ques=on Q: Why did the field of

Introduc=on: The Big Ques=on Q: Why did the field of

8/22/11 The Origins of Molecular Biology: A Mendelian and Darwinian View of the World Introduc=on: The Big Ques=on Q: Why did the field of

160 views • 14 slides
Introduc)on to single-cell genome assembly Kasia (Katarzyna)

Introduc)on to single-cell genome assembly Kasia (Katarzyna)

Introduc)on to single-cell genome assembly Kasia (Katarzyna) Zaremba-Niedzwiedzka Uppsala University Outline: introduc)on Assembly basics Assembly

585 views • 37 slides
CSE 344 Introduc/on to Data Management Sec/on 9: AWS,

CSE 344 Introduc/on to Data Management Sec/on 9: AWS,

CSE 344 Introduc/on to Data Management Sec/on 9: AWS, Hadoop, Pig La/n Srini (sviyer@cs) Homework 8 (Last hw J ) Huge Graphs out there!

588 views • 31 slides
Session 11 More on mixed effects modelling A Generalized Linear Mixed Model Recovery of the

Session 11 More on mixed effects modelling A Generalized Linear Mixed Model Recovery of the

Session 11 More on mixed effects modelling A Generalized Linear Mixed Model Recovery of the benthos after trawling on the Great Barrier Reef 6 Control plots, 6 Impact plots Visited 2 times prior to treatment and 4 times afterwards

981 views • 22 slides
Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017

Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017

-1- Workshop 11.2a: Generalized Linear Mixed Effects Models (GLMM) Murray Logan February 7, 2017 Table of contents 1 Generalized Linear Mixed Effects Models 1 2 Worked Examples 5 1. Generalized Linear Mixed Effects Models 1.1.

286 views • 5 slides
Introduction to General and Generalized Linear Models Mixed effects models - Part I Henrik Madsen

Introduction to General and Generalized Linear Models Mixed effects models - Part I Henrik Madsen

Introduction to General and Generalized Linear Models Mixed effects models - Part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby January 2011 Henrik Madsen Poul

923 views • 28 slides
Lecture 20: Bayesianism and Modern Theories of Evidence - 2 1 The combination of ideas we are

Lecture 20: Bayesianism and Modern Theories of Evidence - 2 1 The combination of ideas we are

HPSC 1001/1901/2101/2901 WHAT IS THIS THING CALLED SCIENCE? Semester 2, 2020 Lecture 20: Bayesianism and Modern Theories of Evidence - 2 1 The combination of ideas we are looking at: 1. Degrees of belief, 2. Interpreted as probabilities, 3.

2.08k views • 21 slides
Uncertainty in Eddy Sources of Random Error Random Errors: . . . Covariance Measurements:

Uncertainty in Eddy Sources of Random Error Random Errors: . . . Covariance Measurements:

Random and . . . Random Errors of . . . Random Errors: . . . Uncertainty in Eddy Sources of Random Error Random Errors: . . . Covariance Measurements: Systematic Error Systematic . . . An Overview Based on How to Use . . . a Recent Book

510 views • 13 slides
One-Way ANOVA (MD3) Paul Gribble Winter, 2019 . . . . . . . . . . . . . . . . .

One-Way ANOVA (MD3) Paul Gribble Winter, 2019 . . . . . . . . . . . . . . . . .

One-Way ANOVA (MD3) Paul Gribble Winter, 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Review from last class sample vs population estimating population

406 views • 25 slides
A Side-Channel Assisted Cryptanalytic Attack Against QcBits Mlissa Rossi Mike Hamburg

A Side-Channel Assisted Cryptanalytic Attack Against QcBits Mlissa Rossi Mike Hamburg

A Side-Channel Assisted Cryptanalytic Attack Against QcBits Mlissa Rossi Mike Hamburg Michael Hutter Mark E. Marson Possible path for post-quantum security Error-correcting codes Quantum computers may threaten the mathematical

892 views • 66 slides
Introd u ction to Random Forest TR E E - BASE D MOD E L S IN R Erin LeDell Instr u ctor Random

Introd u ction to Random Forest TR E E - BASE D MOD E L S IN R Erin LeDell Instr u ctor Random

Introd u ction to Random Forest TR E E - BASE D MOD E L S IN R Erin LeDell Instr u ctor Random Forest be er performance sample s u bset of the feat u res impro v ed v ersion of bagging red u ced correlation bet w een the sampled trees TREE

308 views • 18 slides

More recommend