CS 147: Computer Systems Performance Analysis
|
|
- Meryl Strickland
- 5 years ago
- Views:
Transcription
1 CS 147: Computer Systems Performance Analysis Mistakes in Graphical Presentation CS 147: Computer Systems Performance Analysis Mistakes in Graphical Presentation 1 / 45
2 Overview Excess Information Multiple Scales Symbols for Text Poor Scales Bad Line Usage Overview Overview Excess Information Multiple Scales Symbols for Text Poor Scales Bad Line Usage Non-Zero Origins Double Whammy No Confidence Intervals Height Scaling Histogram Problems Graphical Integrity Special-Purpose Charts A Few Examples Non-Zero Origins Double Whammy No Confidence Intervals Height Scaling Histogram Problems Graphical Integrity Special-Purpose Charts A Few Examples 2 / 45
3 3 / 45 Excess Information Excess Information Excess Information Excess Information Excess Information Sneaky trick to meet length limits Rules of thumb: 6 curves on line chart 1 bars on bar chart 8 slices on pie chart (But note that Tufte hates pie charts) Extract essence; don t cram things in Sneaky trick to meet length limits Rules of thumb: 6 curves on line chart 1 bars on bar chart 8 slices on pie chart (But note that Tufte hates pie charts) Extract essence; don t cram things in
4 4 / 45 Excess Information Way Too Much Information Excess Information Way Too Much Information Way Too Much Information 4 3 Time REPL CP FIND FINDGREP GREP LS MAB RCP RM 4 3 Time 2 1 CP FIND FINDGREP GREP LS MAB RCP RM What s important on that chart? Times for cp and rcp rise with number of replicas Most other benchmarks are near constant Exactly constant for rm 1 REPL
5 Excess Information The Right Amount of Information Excess Information The Right Amount of Information The Right Amount of Information 4 3 cp Time 2 compile rm Replicas 4 Time cp compile rm Replicas 5 / 45
6 6 / 45 Multiple Scales Multiple Scales Multiple Scales Multiple Scales Multiple Scales Another way to meet length limits Basically, two graphs overlaid on each other Confuses reader (which line goes with which scale?) Misstates relationships Implies equality of magnitude that doesn t exist Another way to meet length limits Basically, two graphs overlaid on each other Confuses reader (which line goes with which scale?) Misstates relationships Implies equality of magnitude that doesn t exist
7 7 / 45 Multiple Scales Some Especially Bad Multiple Scales Multiple Scales Some Especially Bad Multiple Scales Some Especially Bad Multiple Scales Throughput 1 Response Time Throughput Response Time
8 8 / 45 Symbols for Text Using Symbols in Place of Text Symbols for Text Using Symbols in Place of Text Using Symbols in Place of Text Graphics should be self-explanatory Remember that the graphs often draw the reader in So use explanatory text, not symbols This means no Greek letters! Unless your conference is in Athens... Graphics should be self-explanatory Remember that the graphs often draw the reader in So use explanatory text, not symbols This means no Greek letters! Unless your conference is in Athens...
9 Symbols for Text It s All Greek To Me Symbols for Text It s All Greek To Me... It s All Greek To Me w ρ 1 8 w ρ 9 / 45
10 1 / 45 Symbols for Text Explanation is Easy 12 Waiting Time as a Function of Offered Load Symbols for Text Explanation is Easy Explanation is Easy Waiting Time as a Function of Offered Load Waiting Time Offered Load 1 Waiting Time Offered Load
11 11 / 45 Poor Scales Poor Scales Poor Scales Poor Scales Poor Scales Fiddle with axis ranges (and logarithms) to get your message across But don t lie or cheat Sometimes trimming off high ends makes things clearer Brings out low-end detail Fiddle with axis ranges (and logarithms) to get your message across But don t lie or cheat Sometimes trimming off high ends makes things clearer Brings out low-end detail
12 12 / 45 Poor Scales A Poor Axis Range Poor Scales A Poor Axis Range A Poor Axis Range st Qtr 2nd Qtr 3rd Qtr 4th Qtr st Qtr 2nd Qtr 3rd Qtr 4th Qtr
13 13 / 45 Poor Scales A Logarithmic Range Poor Scales A Logarithmic Range A Logarithmic Range st Qtr 2nd Qtr 3rd Qtr 4th Qtr st Qtr 2nd Qtr 3rd Qtr 4th Qtr
14 14 / 45 Poor Scales A Truncated Range 1 Poor Scales A Truncated Range A Truncated Range st Qtr 2nd Qtr 3rd Qtr 4th Qtr st Qtr 2nd Qtr 3rd Qtr 4th Qtr
15 15 / 45 Bad Line Usage Using Lines Incorrectly Bad Line Usage Using Lines Incorrectly Using Lines Incorrectly Don t connect points unless interpolation is meaningful Don t smooth lines that are based on samples Exception: fitted non-linear curves Don t connect points unless interpolation is meaningful Don t smooth lines that are based on samples Exception: fitted non-linear curves
16 Bad Line Usage Incorrect Line Usage Bad Line Usage Incorrect Line Usage Incorrect Line Usage 4 3 cp Time 2 compile rm Replicas 4 Time cp compile rm Replicas 16 / 45
17 17 / 45 Non-Zero Origins Non-Zero Origins and Broken Scales Non-Zero Origins Non-Zero Origins and Broken Scales Non-Zero Origins and Broken Scales People expect (,) origins Subconsciously So non-zero origins are great way to lie More common than not in popular press Also very common to cheat by omitting part of scale Really, Your Honor, I included (,) People expect (,) origins Subconsciously So non-zero origins are great way to lie More common than not in popular press Also very common to cheat by omitting part of scale Really, Your Honor, I included (,)
18 18 / 45 Non-Zero Origins Non-Zero Origins Non-Zero Origins Non-Zero Origins Non-Zero Origins 27 1 Us Us 26 Them 8 Them st Qtr 2nd Qtr 3rd Qtr 4th Qtr 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Us Them Us Them st Qtr 2nd Qtr 3rd Qtr 4th Qtr 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
19 19 / 45 Non-Zero Origins The Three-Quarters Rule Highest point should be 3/4 of scale or more Non-Zero Origins The Three-Quarters Rule The Three-Quarters Rule Highest point should be 3/4 of scale or more Us 15 Them 1 5 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Us Them 5 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
20 2 / 45 Double Whammy Double-Whammy Graphs Double Whammy Double-Whammy Graphs Double-Whammy Graphs Put two related measures on same graph One is (almost) function of other Hits reader twice with same information And thus overstates impact 6 Sales ($) Units Shipped 4 2 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Put two related measures on same graph One is (almost) function of other Hits reader twice with same information And thus overstates impact Sales ($) Units Shipped 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
21 21 / 45 No Confidence Intervals Omitting Confidence Intervals No Confidence Intervals Omitting Confidence Intervals Omitting Confidence Intervals Statistical data is inherently fuzzy But means appear precise Giving confidence intervals can make it clear there s no real difference So liars and fools leave them out Statistical data is inherently fuzzy But means appear precise Giving confidence intervals can make it clear there s no real difference So liars and fools leave them out
22 22 / 45 No Confidence Intervals Graph Without Confidence Intervals No Confidence Intervals Graph Without Confidence Intervals Graph Without Confidence Intervals st Qtr 2nd Qtr 3rd Qtr 4th Qtr st Qtr 2nd Qtr 3rd Qtr 4th Qtr
23 23 / 45 No Confidence Intervals Graph With Confidence Intervals No Confidence Intervals Graph With Confidence Intervals Graph With Confidence Intervals st Qtr 2nd Qtr 3rd Qtr 4th Qtr st Qtr 2nd Qtr 3rd Qtr 4th Qtr
24 24 / 45 Height Scaling Scaling by Height Instead of Area Clip art is popular with illustrators: Height Scaling Scaling by Height Instead of Area Scaling by Height Instead of Area Clip art is popular with illustrators: Women in the Workforce Women in the Workforce
25 25 / 45 Height Scaling The Trouble with Height Scaling Height Scaling The Trouble with Height Scaling The Trouble with Height Scaling Previous graph had heights of 2:1 But people perceive areas, not heights So areas should be what s proportional to data Tufte defines lie factor: size of effect in graphic divided by size of effect in data Not limited to area scaling But especially insidious there (quadratic effect) Previous graph had heights of 2:1 But people perceive areas, not heights So areas should be what s proportional to data Tufte defines lie factor: size of effect in graphic divided by size of effect in data Not limited to area scaling But especially insidious there (quadratic effect)
26 26 / 45 Height Scaling Scaling by Area Same graph with 2:1 area: Height Scaling Scaling by Area Scaling by Area Same graph with 2:1 area: Women in the Workforce Women in the Workforce
27 Histogram Problems Poor Histogram Cell Size Histogram Problems Poor Histogram Cell Size Poor Histogram Cell Size Picking bucket size is always problem Prefer 5 or more observations per bucket Choice of bucket size can affect results: Picking bucket size is always problem Prefer 5 or more observations per bucket Choice of bucket size can affect results: Note that green bars are steadily decreasing, but blue bars rise, fall, and rise again. It s not clear which is correct (given small counts in the smaller buckets). 27 / 45
28 Graphical Integrity Principles of Graphics Integrity (Tufte) Graphical Integrity Principles of Graphics Integrity (Tufte) Principles of Graphics Integrity (Tufte) Proportional representation of numbers Clear, detailed, thorough labeling Show data variation, not design variation Use deflated money units Don t have more dimensions than data has Don t quote data out of context Proportional representation of numbers Clear, detailed, thorough labeling Show data variation, not design variation Use deflated money units Don t have more dimensions than data has Don t quote data out of context 28 / 45
29 29 / 45 Graphical Integrity Proportional Representation of Numbers Graphical Integrity Proportional Representation of Numbers Proportional Representation of Numbers Maintain lie factor of 1. Use areas, not heights, with clip art Avoiding decorative graphs will do wonders Not too hard for most engineers! Maintain lie factor of 1. Use areas, not heights, with clip art Avoiding decorative graphs will do wonders Not too hard for most engineers!
30 Graphical Integrity Clear, Detailed, Thorough Labeling Graphical Integrity Clear, Detailed, Thorough Labeling Clear, Detailed, Thorough Labeling Goal is to defeat distortion and ambiguity Write explanations on graphic itself Label important events in the data Goal is to defeat distortion and ambiguity Write explanations on graphic itself Label important events in the data 3 / 45
31 Graphical Integrity Show Data Variation, Not Design Variation Graphical Integrity Show Data Variation, Not Design Variation Show Data Variation, Not Design Variation Use one design for entire graphic In papers, try to use one design for all graphs Again, artistic license is big culprit Use one design for entire graphic In papers, try to use one design for all graphs Again, artistic license is big culprit 31 / 45
32 32 / 45 Graphical Integrity Use Deflated Money Units Graphical Integrity Use Deflated Money Units Use Deflated Money Units Often necessary to show money over time Even in computer science E.g., price/performance over time Or expected future cost of a disk Nominal dollars are meaningless Derate by some standard inflation measure That s what the WWW is for! Often necessary to show money over time Even in computer science E.g., price/performance over time Or expected future cost of a disk Nominal dollars are meaningless Derate by some standard inflation measure That s what the WWW is for!
33 33 / 45 Graphical Integrity Don t Have More Dimensions Than Data Has This gets back to the Lie Factor 1-D data (e.g., money) should occupy one dimension on the graph: not Clip art is prohibited by this rule But if you have to, use an area measure Graphical Integrity Don t Have More Dimensions Than Data Has Don t Have More Dimensions Than Data Has This gets back to the Lie Factor 1-D data (e.g., money) should occupy one dimension on the graph: not Clip art is prohibited by this rule But if you have to, use an area measure $1. $2. $1. $2.
34 34 / 45 Graphical Integrity Don t Quote Data Out of Context Tufte s example: Graphical Integrity Don t Quote Data Out of Context Don t Quote Data Out of Context Tufte s example: Traffic Deaths and 35 Enforcement of Speed Limits After stricter 325 enforcement 3 Before stricter enforcement Traffic Deaths and Enforcement of Speed Limits 325 After stricter enforcement Before stricter enforcement
35 Graphical Integrity The Same Data in Context Connecticut Traffic Deaths, Graphical Integrity The Same Data in Context The Same Data in Context Connecticut Traffic Deaths, / 45
36 Special-Purpose Charts Special-Purpose Charts Special-Purpose Charts Special-Purpose Charts Special-Purpose Charts Tukey s box plot Histograms Scatter plots Gantt charts Kiviat graphs Tukey s box plot Histograms Scatter plots Gantt charts Kiviat graphs 36 / 45
37 37 / 45 Special-Purpose Charts Tukey s Box Plot Special-Purpose Charts Tukey s Box Plot Tukey s Box Plot Shows range, median, quartiles all in one: minimum quartile median quartile Tufte can t resist improvements: or or even maximum Shows range, median, quartiles all in one: minimum quartile median quartile Tufte can t resist improvements: maximum or or even
38 Special-Purpose Charts Histograms Tufte improves everything about them: Special-Purpose Charts Histograms Histograms Tufte improves everything about them: st 2nd 3rd 4th Quarter st 2nd 3rd 4th Quarter 38 / 45
39 39 / 45 Special-Purpose Charts Scatter Plots Useful in statistical analysis Also excellent for huge quantities of data Can show patterns otherwise invisible Special-Purpose Charts Scatter Plots Scatter Plots Useful in statistical analysis Also excellent for huge quantities of data Can show patterns otherwise invisible
40 4 / 45 Special-Purpose Charts Better Scatter Plots Again, Tufte improves the standard But it can be a pain with automated tools Can use modified Tukey box plot for axes: Special-Purpose Charts Better Scatter Plots Better Scatter Plots Again, Tufte improves the standard But it can be a pain with automated tools Can use modified Tukey box plot for axes:
41 41 / 45 Special-Purpose Charts Gantt Charts Shows relative duration of Boolean conditions Arranged to make lines continuous Each level after first follows FTTF pattern (Possibly repeated) CPU I/O Network Special-Purpose Charts Gantt Charts Gantt Charts Shows relative duration of Boolean conditions Arranged to make lines continuous Each level after first follows FTTF pattern (Possibly repeated) CPU I/O Network % Gantt charts are any chart with horizontal lines showing spans on the X axis. Also useful for scheduling; shows simultaneous tasks. Lines are divided in mid-true; any vertical line shows one unique combo of conditions. Length of line with particular condition shows percentage of time system spends in that state %
42 Special-Purpose Charts Kiviat Graphs Also called star charts or radar plots Special-Purpose Charts Kiviat Graphs Kiviat Graphs Also called star charts or radar plots Useful for looking at balance between HB and LB metrics Useful for looking at balance between HB and LB metrics 42 / 45
43 43 / 45 A Few Examples A Very Bad Graph A Few Examples A Very Bad Graph A Very Bad Graph
44 44 / 45 A Few Examples A Good Graph: Sunspots A Few Examples A Good Graph: Sunspots A Good Graph: Sunspots Vertical scale is latitude of sunspot; length of bar is extent of latitude width of sunspot (longitude width is not in the graph). The 11-year cycle is easily visible. The horizontal scale is empty in a few places where sunspot data extends into it. This graph was drawn in 194 by Edward Walter Maunder ( ). It is commonly called a butterfly diagram for obvious reasons.
45 45 / 45 A Few Examples A Superb Graph: DEC Traces A Few Examples A Superb Graph: DEC Traces A Superb Graph: DEC Traces X axis is time (instructions executed). Y axis is memory address referenced, modulo 4 MB. Red lines are data accesses, blue instructions. Green is perhaps stack? Note how parallel access to arrays is easy to see, as well as occasional faster access and reverse-order access.
Scientific Investigation Use and Interpret Graphs Promotion Benchmark 3 Lesson Review Student Copy
Scientific Investigation Use and Interpret Graphs Promotion Benchmark 3 Lesson Review Student Copy Vocabulary Data Table A place to write down and keep track of data collected during an experiment. Line
More informationWhy Should We Care? More importantly, it is easy to lie or deceive people with bad plots
Elementary Plots Why Should We Care? Everyone uses plotting But most people ignore or are unaware of simple principles Default plotting tools (or default settings) are not always the best More importantly,
More informationUsing Figures - The Basics
Using Figures - The Basics by David Caprette, Rice University OVERVIEW To be useful, the results of a scientific investigation or technical project must be communicated to others in the form of an oral
More informationChapter 4. Displaying and Summarizing Quantitative Data. Copyright 2012, 2008, 2005 Pearson Education, Inc.
Chapter 4 Displaying and Summarizing Quantitative Data Copyright 2012, 2008, 2005 Pearson Education, Inc. Dealing With a Lot of Numbers Summarizing the data will help us when we look at large sets of quantitative
More informationImportant Considerations For Graphical Representations Of Data
This document will help you identify important considerations when using graphs (also called charts) to represent your data. First, it is crucial to understand how to create good graphs. Then, an overview
More informationChapter 3. Graphical Methods for Describing Data. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.
Chapter 3 Graphical Methods for Describing Data 1 Frequency Distribution Example The data in the column labeled vision for the student data set introduced in the slides for chapter 1 is the answer to the
More informationWhy Should We Care? Everyone uses plotting But most people ignore or are unaware of simple principles Default plotting tools are not always the best
Elementary Plots Why Should We Care? Everyone uses plotting But most people ignore or are unaware of simple principles Default plotting tools are not always the best More importantly, it is easy to lie
More informationStatistics. Graphing Statistics & Data. What is Data?. Data is organized information. It can be numbers, words, measurements,
Statistics Graphing Statistics & Data What is Data?. Data is organized information. It can be numbers, words, measurements, observations or even just descriptions of things. Qualitative vs Quantitative.
More informationTO PLOT OR NOT TO PLOT?
Graphic Examples This document provides examples of a number of graphs that might be used in understanding or presenting data. Comments with each example are intended to help you understand why the data
More informationA Visual Display. A graph is a visual display of information or data. This is a graph that shows a girl walking her dog. Communicating with Graphs
A Visual Display A graph is a visual display of information or data. This is a graph that shows a girl walking her dog. A Visual Display The horizontal axis, or the x-axis, measures time. Time is the independent
More informationConfidence Intervals. Class 23. November 29, 2011
Confidence Intervals Class 23 November 29, 2011 Last Time When sampling from a population in which 30% of individuals share a certain characteristic, we identified the reasonably likely values for the
More informationChapter 4. September 08, appstats 4B.notebook. Displaying Quantitative Data. Aug 4 9:13 AM. Aug 4 9:13 AM. Aug 27 10:16 PM.
Objectives: Students will: Chapter 4 1. Be able to identify an appropriate display for any quantitative variable: stem leaf plot, time plot, histogram and dotplot given a set of quantitative data. 2. Be
More informationChapter Displaying Graphical Data. Frequency Distribution Example. Graphical Methods for Describing Data. Vision Correction Frequency Relative
Chapter 3 Graphical Methods for Describing 3.1 Displaying Graphical Distribution Example The data in the column labeled vision for the student data set introduced in the slides for chapter 1 is the answer
More informationBusiness Statistics:
Department of Quantitative Methods & Information Systems Business Statistics: Chapter 2 Graphs, Charts, and Tables Describing Your Data QMIS 120 Dr. Mohammad Zainal Chapter Goals After completing this
More informationUnivariate Descriptive Statistics
Univariate Descriptive Statistics Displays: pie charts, bar graphs, box plots, histograms, density estimates, dot plots, stemleaf plots, tables, lists. Example: sea urchin sizes Boxplot Histogram Urchin
More informationDescribing Data Visually. Describing Data Visually. Describing Data Visually 9/28/12. Applied Statistics in Business & Economics, 4 th edition
A PowerPoint Presentation Package to Accompany Applied Statistics in Business & Economics, 4 th edition David P. Doane and Lori E. Seward Prepared by Lloyd R. Jaisingh Describing Data Visually Chapter
More informationUsing Charts and Graphs to Display Data
Page 1 of 7 Using Charts and Graphs to Display Data Introduction A Chart is defined as a sheet of information in the form of a table, graph, or diagram. A Graph is defined as a diagram that represents
More informationChapter 10. Definition: Categorical Variables. Graphs, Good and Bad. Distribution
Chapter 10 Graphs, Good and Bad Chapter 10 3 Distribution Definition: Tells what values a variable takes and how often it takes these values Can be a table, graph, or function Categorical Variables Places
More informationInfographics at CDC for a nonscientific audience
Infographics at CDC for a nonscientific audience A Standards Guide for creating successful infographics Centers for Disease Control and Prevention Office of the Associate Director for Communication 03/14/2012;
More informationDrawing Bode Plots (The Last Bode Plot You Will Ever Make) Charles Nippert
Drawing Bode Plots (The Last Bode Plot You Will Ever Make) Charles Nippert This set of notes describes how to prepare a Bode plot using Mathcad. Follow these instructions to draw Bode plot for any transfer
More informationGeneral tips for all graphs Choosing the right kind of graph scatter graph bar graph
Excerpted and adapted from: McDonald, J.H. 2014. Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, MD. (http://www.biostathandbook.com/graph.html) Guide to fairly good graphs
More informationHIGHWAY SAFETY RESEARCH GROUP
1. Why use data visualization? 2. Why we perceive data visualizations better than tabular data? 3. How do we choose the proper visualization to display our data? 4. What are the Dos and Don ts of creating
More informationOffice 2016 Excel Basics 24 Video/Class Project #36 Excel Basics 24: Visualize Quantitative Data with Excel Charts. No Chart Junk!!!
Office 2016 Excel Basics 24 Video/Class Project #36 Excel Basics 24: Visualize Quantitative Data with Excel Charts. No Chart Junk!!! Goal in video # 24: Learn about how to Visualize Quantitative Data with
More information1.3 Density Curves and Normal Distributions
1.3 Density Curves and Normal Distributions Ulrich Hoensch Tuesday, January 22, 2013 Fitting Density Curves to Histograms Advanced statistical software (NOT Microsoft Excel) can produce smoothed versions
More informationConstructing Line Graphs*
Appendix B Constructing Line Graphs* Suppose we are studying some chemical reaction in which a substance, A, is being used up. We begin with a large quantity (1 mg) of A, and we measure in some way how
More information1.3 Density Curves and Normal Distributions. Ulrich Hoensch MAT210 Rocky Mountain College Billings, MT 59102
1.3 Density Curves and Normal Distributions Ulrich Hoensch MAT210 Rocky Mountain College Billings, MT 59102 Fitting Density Curves to Histograms Advanced statistical software (NOT Microsoft Excel) can
More informationNumerical: Data with quantity Discrete: whole number answers Example: How many siblings do you have?
Types of data Numerical: Data with quantity Discrete: whole number answers Example: How many siblings do you have? Continuous: Answers can fall anywhere in between two whole numbers. Usually any type of
More informationSection 1.5 Graphs and Describing Distributions
Section 1.5 Graphs and Describing Distributions Data can be displayed using graphs. Some of the most common graphs used in statistics are: Bar graph Pie Chart Dot plot Histogram Stem and leaf plot Box
More informationChapter 4 Displaying and Describing Quantitative Data
Chapter 4 Displaying and Describing Quantitative Data Overview Key Concepts Be able to identify an appropriate display for any quantitative variable. Be able to guess the shape of the distribution of a
More information1.3 Density Curves and Normal Distributions
1.3 Density Curves and Normal Distributions Ulrich Hoensch Tuesday, September 11, 2012 Fitting Density Curves to Histograms Advanced statistical software (NOT Microsoft Excel) can produce smoothed versions
More informationSS Understand charts and graphs used in business.
SS2 2.02 Understand charts and graphs used in business. Purpose of Charts and Graphs 1. Charts and graphs are used in business to communicate and clarify spreadsheet information. 2. Charts and graphs emphasize
More informationElementary Statistics. Graphing Data
Graphing Data What have we learned so far? 1 Randomly collect data. 2 Sort the data. 3 Compute the class width for specific number of classes. 4 Complete a frequency distribution table with the following
More informationLine Graphs. Name: The independent variable is plotted on the x-axis. This axis will be labeled Time (days), and
Name: Graphing Review Graphs and charts are great because they communicate information visually. For this reason graphs are often used in newspapers, magazines, and businesses around the world. Sometimes,
More informationEXPERIMENTAL ERROR AND DATA ANALYSIS
EXPERIMENTAL ERROR AND DATA ANALYSIS 1. INTRODUCTION: Laboratory experiments involve taking measurements of physical quantities. No measurement of any physical quantity is ever perfectly accurate, except
More information10 Wyner Statistics Fall 2013
1 Wyner Statistics Fall 213 CHAPTER TWO: GRAPHS Summary Terms Objectives For research to be valuable, it must be shared. The fundamental aspect of a good graph is that it makes the results clear at a glance.
More informationOutline. Drawing the Graph. 1 Homework Review. 2 Introduction. 3 Histograms. 4 Histograms on the TI Assignment
Lecture 14 Section 4.4.4 on Hampden-Sydney College Fri, Sep 18, 2009 Outline 1 on 2 3 4 on 5 6 Even-numbered on Exercise 4.25, p. 249. The following is a list of homework scores for two students: Student
More informationAppendix C: Graphing. How do I plot data and uncertainties? Another technique that makes data analysis easier is to record all your data in a table.
Appendix C: Graphing One of the most powerful tools used for data presentation and analysis is the graph. Used properly, graphs are an important guide to understanding the results of an experiment. They
More informationESSENTIAL MATHEMATICS 1 WEEK 17 NOTES AND EXERCISES. Types of Graphs. Bar Graphs
ESSENTIAL MATHEMATICS 1 WEEK 17 NOTES AND EXERCISES Types of Graphs Bar Graphs Bar graphs are used to present and compare data. There are two main types of bar graphs: horizontal and vertical. They are
More informationChapter 2 Frequency Distributions and Graphs
Chapter 2 Frequency Distributions and Graphs Outline 2-1 Organizing Data 2-2 Histograms, Frequency Polygons, and Ogives 2-3 Other Types of Graphs Objectives Organize data using a frequency distribution.
More informationSimple Graphical Techniques
Simple Graphical Techniques Graphs are the pictorial representation of facts and figures, or data. The eye can detect patterns and trends from graphs far more easily than from a lot of numbers. Linear
More informationGoing back to the definition of Biostatistics. Organizing and Presenting Data. Learning Objectives. Nominal Data 10/10/2016. Tabulation and Graphs
1/1/1 Organizing and Presenting Data Tabulation and Graphs Introduction to Biostatistics Haleema Masud Going back to the definition of Biostatistics The collection, organization, summarization, analysis,
More informationMs. Cavo Graphic Art & Design Illustrator CS3 Notes
Ms. Cavo Graphic Art & Design Illustrator CS3 Notes 1. Selection tool - Lets you select objects and groups by clicking or dragging over them. You can also select groups within groups and objects within
More informationCopyright 1997 by the Society of Photo-Optical Instrumentation Engineers.
Copyright 1997 by the Society of Photo-Optical Instrumentation Engineers. This paper was published in the proceedings of Microlithographic Techniques in IC Fabrication, SPIE Vol. 3183, pp. 14-27. It is
More informationMicrosoft Excel: Data Analysis & Graphing. College of Engineering Engineering Education Innovation Center
Microsoft Excel: Data Analysis & Graphing College of Engineering Engineering Education Innovation Center Objectives Use relative, absolute, and mixed cell referencing Identify the types of graphs and their
More informationENGINEERING GRAPHICS ESSENTIALS
ENGINEERING GRAPHICS ESSENTIALS Text and Digital Learning KIRSTIE PLANTENBERG FIFTH EDITION SDC P U B L I C AT I O N S Better Textbooks. Lower Prices. www.sdcpublications.com ACCESS CODE UNIQUE CODE INSIDE
More informationChapter 2. Organizing Data. Slide 2-2. Copyright 2012, 2008, 2005 Pearson Education, Inc.
Chapter 2 Organizing Data Slide 2-2 Section 2.1 Variables and Data Slide 2-3 Definition 2.1 Variables Variable: A characteristic that varies from one person or thing to another. Qualitative variable: A
More informationAWM 11 UNIT 1 WORKING WITH GRAPHS
AWM 11 UNIT 1 WORKING WITH GRAPHS Assignment Title Work to complete Complete 1 Introduction to Statistics Read the introduction no written assignment 2 Bar Graphs Bar Graphs 3 Double Bar Graphs Double
More informationGraphs. This tutorial will cover the curves of graphs that you are likely to encounter in physics and chemistry.
Graphs Graphs are made by graphing one variable which is allowed to change value and a second variable that changes in response to the first. The variable that is allowed to change is called the independent
More informationfile:///d:/mohammad 1/New Folder/Freeman/Microeconomics Paul Krug...
1 of 33 5/26/2013 10:46 PM COURSES > C > CONTROL PANEL > POOL MANAGER > POOL CANVAS Add, modify, and remove questions. Select a question type from the Add drop-down list and click Go to add questions.
More informationSTAB22 section 2.4. Figure 2: Data set 2. Figure 1: Data set 1
STAB22 section 2.4 2.73 The four correlations are all 0.816, and all four regressions are ŷ = 3 + 0.5x. (b) can be answered by drawing fitted line plots in the four cases. See Figures 1, 2, 3 and 4. Figure
More informationDESCRIBING DATA. Frequency Tables, Frequency Distributions, and Graphic Presentation
DESCRIBING DATA Frequency Tables, Frequency Distributions, and Graphic Presentation Raw Data A raw data is the data obtained before it is being processed or arranged. 2 Example: Raw Score A raw score is
More informationExcel Manual X Axis Scale Start At Graph
Excel Manual X Axis Scale Start At 0 2010 Graph But when I plot them by XY chart in Excel (2003), it looks like a rectangle, even if I havesame for both X, and Y axes, and I can see the X and Y data maximum
More informationStatistical Pulse Measurements using USB Power Sensors
Statistical Pulse Measurements using USB Power Sensors Today s modern USB Power Sensors are capable of many advanced power measurements. These Power Sensors are capable of demodulating the signal and processing
More informationAP Statistics Composition Book Review Chapters 1 2
AP Statistics Composition Book Review Chapters 1 2 Terms/vocabulary: Explain each term with in the STATISTICAL context. Bar Graph Bimodal Categorical Variable Density Curve Deviation Distribution Dotplot
More informationExcel Manual X Axis Label Below Chart 2010 >>>CLICK HERE<<<
Excel Manual X Axis Label Below Chart 2010 When the X-axis is crowded with labels one way to solve the problem is to split the labels for to use two rows of labels enter the two rows of X-axis labels as
More informationProblem Solving with Length, Money, and Data
Grade 2 Module 7 Problem Solving with Length, Money, and Data OVERVIEW Module 7 presents an opportunity for students to practice addition and subtraction strategies within 100 and problem-solving skills
More informationLecture Notes: Writing and figures
Lecture Notes: Writing and figures The creation of a good figure is somewhat of a creative process. It is definitely not trivial. It is not sufficient to use a simple plot command and do nothing else.
More information5.1N Key Features of Rational Functions
5.1N Key Features of Rational Functions A. Vocabulary Review Domain: Range: x-intercept: y-intercept: Increasing: Decreasing: Constant: Positive: Negative: Maximum: Minimum: Symmetry: End Behavior/Limits:
More informationLESSON 2: FREQUENCY DISTRIBUTION
LESSON : FREQUENCY DISTRIBUTION Outline Frequency distribution, histogram, frequency polygon Relative frequency histogram Cumulative relative frequency graph Stem-and-leaf plots Scatter diagram Pie charts,
More informationUNIT I FUNDAMENTALS OF ANALOG COMMUNICATION Introduction In the Microbroadcasting services, a reliable radio communication system is of vital importance. The swiftly moving operations of modern communities
More informationChpt 2. Frequency Distributions and Graphs. 2-3 Histograms, Frequency Polygons, Ogives / 35
Chpt 2 Frequency Distributions and Graphs 2-3 Histograms, Frequency Polygons, Ogives 1 Chpt 2 Homework 2-3 Read pages 48-57 p57 Applying the Concepts p58 2-4, 10, 14 2 Chpt 2 Objective Represent Data Graphically
More informationNotes 5C: Statistical Tables and Graphs
Notes 5C: Statistical Tables and Graphs Frequency Tables A frequency table is an easy way to display raw data. A frequency table typically has between two to four columns: The first column lists all the
More informationExperiment 3. Ohm s Law. Become familiar with the use of a digital voltmeter and a digital ammeter to measure DC voltage and current.
Experiment 3 Ohm s Law 3.1 Objectives Become familiar with the use of a digital voltmeter and a digital ammeter to measure DC voltage and current. Construct a circuit using resistors, wires and a breadboard
More informationPurpose. Charts and graphs. create a visual representation of the data. make the spreadsheet information easier to understand.
Purpose Charts and graphs are used in business to communicate and clarify spreadsheet information. convert spreadsheet information into a format that can be quickly and easily analyzed. make the spreadsheet
More informationLaboratory 2: Graphing
Purpose It is often said that a picture is worth 1,000 words, or for scientists we might rephrase it to say that a graph is worth 1,000 words. Graphs are most often used to express data in a clear, concise
More informationENGINEERING GRAPHICS ESSENTIALS
ENGINEERING GRAPHICS ESSENTIALS with AutoCAD 2012 Instruction Introduction to AutoCAD Engineering Graphics Principles Hand Sketching Text and Independent Learning CD Independent Learning CD: A Comprehensive
More informationExperiment 2. Ohm s Law. Become familiar with the use of a digital voltmeter and a digital ammeter to measure DC voltage and current.
Experiment 2 Ohm s Law 2.1 Objectives Become familiar with the use of a digital voltmeter and a digital ammeter to measure DC voltage and current. Construct a circuit using resistors, wires and a breadboard
More informationMP211 Principles of Audio Technology
MP211 Principles of Audio Technology Guide to Electronic Measurements Copyright Stanley Wolfe All rights reserved. Acrobat Reader 6.0 or higher required Berklee College of Music MP211 Guide to Electronic
More informationScience Binder and Science Notebook. Discussions
Lane Tech H. Physics (Joseph/Machaj 2016-2017) A. Science Binder Science Binder and Science Notebook Name: Period: Unit 1: Scientific Methods - Reference Materials The binder is the storage device for
More informationTOPIC 4 GRAPHICAL PRESENTATION
TOPIC 4 GRAPHICAL PRESENTATION Public agencies are very keen on amassing statistics they collect them, raise them to the nth power, take the cube root, and prepare wonderful diagrams. But what you must
More informationTJP TOP TIPS FOR IGCSE STATS & PROBABILITY
TJP TOP TIPS FOR IGCSE STATS & PROBABILITY Dr T J Price, 2011 First, some important words; know what they mean (get someone to test you): Mean the sum of the data values divided by the number of items.
More informationCambridge Secondary 1 Progression Test. Mark scheme. Mathematics. Stage 9
Cambridge Secondary 1 Progression Test Mark scheme Mathematics Stage 9 DC (CW/SW) 9076/8RP These tables give general guidelines on marking answers that involve number and place value, and units of length,
More informationExcel Tool: Plots of Data Sets
Excel Tool: Plots of Data Sets Excel makes it very easy for the scientist to visualize a data set. In this assignment, we learn how to produce various plots of data sets. Open a new Excel workbook, and
More informationElementary Plotting Techniques
book 2007/9/11 13:53 page 39 #45 5 Elementary Plotting Techniques Plotting data is one of the oldest forms of visualization. In fact, many of the standard plotting techniques were introduced in the late
More information11 Wyner Statistics Fall 2018
11 Wyner Statistics Fall 218 CHAPTER TWO: GRAPHS Review September 19 Test September 28 For research to be valuable, it must be shared, and a graph can be an effective way to do so. The fundamental aspect
More informationNCSS Statistical Software
Chapter 147 Introduction A mosaic plot is a graphical display of the cell frequencies of a contingency table in which the area of boxes of the plot are proportional to the cell frequencies of the contingency
More informationThomson Learning TWO-VARIABLE DIAGRAMS
ppendix Working With iagrams picture is worth a thousand words. With this familiar saying in mind, economists construct their diagrams or graphs. With a few lines and a few points, much can be conveyed.
More informationMathology Ontario Grade 2 Correlations
Mathology Ontario Grade 2 Correlations Curriculum Expectations Mathology Little Books & Teacher Guides Number Sense and Numeration Quality Relations: Read, represent, compare, and order whole numbers to
More informationCHM 152 Lab 1: Plotting with Excel updated: May 2011
CHM 152 Lab 1: Plotting with Excel updated: May 2011 Introduction In this course, many of our labs will involve plotting data. While many students are nerds already quite proficient at using Excel to plot
More informationA marathon is a race that lasts for 26.2 miles. It has been a very popular race
The Man Who Ran from Marathon to Athens Graphing Direct Proportions Learning Goals In this lesson, you will: Graph relationships that are directly proportional. Interpret the graphs of relationships that
More informationCHAPTER 13A. Normal Distributions
CHAPTER 13A Normal Distributions SO FAR We always want to plot our data. We make a graph, usually a histogram or a stemplot. We want to look for an overall pattern (shape, center, spread) and for any striking
More informationAlgebra. Teacher s Guide
Algebra Teacher s Guide WALCH PUBLISHING Table of Contents To the Teacher.......................................................... vi Classroom Management..................................................
More informationCore Connections, Course 2 Checkpoint Materials
Core Connections, Course Checkpoint Materials Notes to Students (and their Teachers) Students master different skills at different speeds. No two students learn exactly the same way at the same time. At
More informationEngineering Graphics Essentials with AutoCAD 2015 Instruction
Kirstie Plantenberg Engineering Graphics Essentials with AutoCAD 2015 Instruction Text and Video Instruction Multimedia Disc SDC P U B L I C AT I O N S Better Textbooks. Lower Prices. www.sdcpublications.com
More informationData Visualizations in SSRS 2008 R2. Stacia Misner Principal Consultant, Data Inspirations
Data Visualizations in SSRS 2008 R2 Stacia Misner Principal Consultant, Data Inspirations 5/13/2011 About Me Stacia Misner Consultant, Educator, Mentor, Author smisner@datainspirations.com Twitter: @StaciaMisner
More informationHow to define Graph in HDSME
How to define Graph in HDSME HDSME provides several chart/graph options to let you analyze your business in a visual format (2D and 3D). A chart/graph can display a summary of sales, profit, or current
More informationFrequency Distribution and Graphs
Chapter 2 Frequency Distribution and Graphs 2.1 Organizing Qualitative Data Denition 2.1.1 A categorical frequency distribution lists the number of occurrences for each category of data. Example 2.1.1
More information8.EE. Development from y = mx to y = mx + b DRAFT EduTron Corporation. Draft for NYSED NTI Use Only
8.EE EduTron Corporation Draft for NYSED NTI Use Only TEACHER S GUIDE 8.EE.6 DERIVING EQUATIONS FOR LINES WITH NON-ZERO Y-INTERCEPTS Development from y = mx to y = mx + b DRAFT 2012.11.29 Teacher s Guide:
More informationName: Date: Class: Lesson 3: Graphing. a. Useful for. AMOUNT OF HEAT PRODUCED IN KJ. b. Difference between a line graph and a scatter plot:
AMOUNT OF HEAT PRODUCED IN KJ NOTES Name: Date: Class: Lesson 3: Graphing Types of Graphs 1. Bar Graph a. Useful for. b. Helps us see quickly. Heat Produced Upon Mixture of Different Acids into Water 90
More informationNotes: Displaying Quantitative Data
Notes: Displaying Quantitative Data Stats: Modeling the World Chapter 4 A or is often used to display categorical data. These types of displays, however, are not appropriate for quantitative data. Quantitative
More informationExcel Manual X Axis Scales 2010 Graph Two X-
Excel Manual X Axis Scales 2010 Graph Two X-axis same for both X, and Y axes, and I can see the X and Y data maximum almost the same, but the graphy on Thanks a lot for any help in advance. Peter T, Jan
More informationObjectives. Organizing Data. Example 1. Making a Frequency Distribution. Solution
Lesson 7.2 Objectives Organize data into a frequency distribution. Find the mean using a frequency distribution. Create a histogram from a frequency distribution. Frequency Distributions In Lesson 7.1,
More informationChapter 2: PRESENTING DATA GRAPHICALLY
2. Presenting Data Graphically 13 Chapter 2: PRESENTING DATA GRAPHICALLY A crowd in a little room -- Miss Woodhouse, you have the art of giving pictures in a few words. -- Emma 2.1 INTRODUCTION Draw a
More informationPre-LAB 5 Assignment
Name: Lab Partners: Date: Pre-LA 5 Assignment Fundamentals of Circuits III: Voltage & Ohm s Law (Due at the beginning of lab) Directions: Read over the Lab Fundamentals of Circuits III: Voltages :w & Ohm
More informationPrinceton ELE 201, Spring 2014 Laboratory No. 2 Shazam
Princeton ELE 201, Spring 2014 Laboratory No. 2 Shazam 1 Background In this lab we will begin to code a Shazam-like program to identify a short clip of music using a database of songs. The basic procedure
More informationGRAPHS & CHARTS. Prof. Rahul C. Basole CS/MGT 8803-DV > January 23, 2017 INFOVIS 8803DV > SPRING 17
GRAPHS & CHARTS Prof. Rahul C. Basole CS/MGT 8803-DV > January 23, 2017 HW2: DataVis Examples Tumblr 47 students = 47 VIS of the Day submissions Random Order We will start next week Stay tuned Tufte Seminar
More informationDescribing Data: Displaying and Exploring Data. Chapter 4
Describing Data: Displaying and Exploring Data Chapter 4 Learning Objectives Develop and interpret a dot plot. Develop and interpret a stem-and-leaf display. Compute and understand quartiles. Construct
More informationFunctions: Transformations and Graphs
Paper Reference(s) 6663/01 Edexcel GCE Core Mathematics C1 Advanced Subsidiary Functions: Transformations and Graphs Calculators may NOT be used for these questions. Information for Candidates A booklet
More informationGRAPHICAL PRESENTATION OF DATA
GRAPHICAL PRESENTATION OF DATA Mathematicians measure with their minds alone the forms of things separated from all matter. Since we wish the object to be seen, we will use a more sensate wisdom. Leon
More informationDescribing Data. Presenting Categorical Data Graphically. Describing Data 143
Describing Data 143 Describing Data Once we have collected data from surveys or experiments, we need to summarize and present the data in a way that will be meaningful to the reader. We will begin with
More information