CS110 Personal Computing 1
|
|
- Harriet Lyons
- 6 years ago
- Views:
Transcription
1 CS110 Personal Computing FORMULAS Understanding formulas The trainings are starting to introduce basic formulas and functions We re also starting to look at the statistical analysis part of the class We noticed about ½ the class struggled with these basic formulas So Percentages If a city has 20% growth per year: How do we calculate the estimated population? By multiplying 20% times the current number And add that to the current number 2013 has 4000 people For 2014, that number is equal to : Calculate the increase (4000 * 20%) = 800 Add that to 4000 = 4800 CS110 Personal Computing 1
2 Let s look at this Why do we use absolute ref? Let s look at the action of copy and paste When a relative address is copied and pasted The references are updated This allows the same formula to be used When an absolute address is copied and pasted The references are not updated This allows constants or parameters to be used Back to our example CS110 Personal Computing 2
3 Copying formula Pasting formula See how the relative references were updated Note the absolute ref It stays the same: CS110 Personal Computing 3
4 Ratios What is a ratio? a ratio is a relationship between two numbers of the same kind Usually expressed A to B or A:B What does it mean? The ratio of bananas to apples is 1:4 The ratio of children to couples is 2.3:1 How do we compute ratios? If we want to know the ratio of cars to people We know there are 400 cars We know there are 200 people What is the ratio? 400:200 Can this be simplified? Yes, by dividing the first by the second Gives us 2:1 What do we use ratios for? To compare two quantities To simplify them to understandable numbers The ratio of children to couples is : 57,500:25,000 or 2.3:1 Which is easier to understand To extrapolate future numbers If the ratio of bananas to apples in a delivery is 4:1 A delivery has 50 apples CS110 Personal Computing 4
5 Averages How do we determine averages? The sum of the values divided by the number of the values So if : Bob as 4 bananas Terri has 2 bananas Sammy has 3 bananas What is the average number of bananas each of them has? What do we use averages for? To extrapolate numbers If each person need an average of 2 liters /day, how much water will 10,000 people need? The more samples there are, the more reliable the average Bob 5 liters Sammy 1 liters Average is 3 liters Small numbers have trouble with outliers What is an outlier? An observation that is numerically distant from the rest of the data What causes outliers? Error in measurement, outside influences Example if my study is tracking time to compute an algorithm, an outlier could be caused by someone else on the computer using the computer cycles or memory CS110 Personal Computing 5
6 How do they affect numbers? They skew the average We record daily water usage With Bob Bob 11 liters (watered his garden as well) Sammy 1.8 liters Tommy 2 liters Andrea 2.2 liters Average is 4.25 liters. If we used this number for 10,000 people, we would have 22,500 liters too many Without Bob Sammy 1.8 liters Tommy 2 liters Andrea 2.2 liters Average is 2 liters If we use this number, we would be right on. How do we get rid of outliers? First we have to identify them We use standard deviation for this Take those values out of the study Re-compute the averages Standard deviation Used to identify the variance in the data The formula for a complete population: If N = population size and Avg = deviation is: 1 N v i N, then the standard i=1 N v i A 2 N CS110 Personal Computing 6
7 How does this apply to outliers? We use the standard deviation to identify the outliers. Let s say we identify anything that is more than 2 times the standard deviation as an outlier Let s go back to our water example: Water example First we compute the average: Bob 11 liters (watered his garden as well) Sammy 1.8 liters Tommy 2 liters Andrea 2.2 liters Jeff 1.9 liters Jackie 2.1 liters Average is 3.5 liters. Water example Next, we compute the sum of the squares Compare to avg Deviation 2 Bob = -7.5 (-7.5) 2 = Sammy = 1.7 (1.7) 2 =2.89 Tommy = 1.5 (1.5) 2 = 2.25 Andrea = 1.3 (1.3) 2 = 1.69 Jeff = 1.6 (1.6) 2 = 2.56 Jackie (1.4) 2 = 1.96 So TotalStandard Deviation = CS110 Personal Computing 7
8 Finishing standard deviation With the sum of the squares = So our standard deviation is 3.35 Identifying the outliers Standard deviation x standard deviation 6.7 Average = 3.5 Throw out anything Over 10.2 Under -3.2 Value with Bob is greater than 10.2 Bob 11 Value Sammy 1.8 Tommy 2 Andrea 2.2 Jeff 1.9 Jackie 2.1 Re-compute the average Value Sammy 1.8 Tommy 2 Andrea 2.2 Jeff 1.9 Jackie 2.1 CS110 Personal Computing 8
9 How to do this in Excel? First, set up standard deviation And average Eliminate the outliers CS110 Personal Computing 9
10 And now compute new average Confidence interval Given in a range and a percentage. Says that with the percentage given, the actual value will fall within the range I.E. given: A normal distribution a 95% confidence interval A range between 9 and 11 Says that at least 95% of the time, the value calculated will fall between 9 and 11. What is a normal distribution? CS110 Personal Computing 10
11 How is this helpful? When you calculate averages, you assign them a confidence interval and a range. This tells your readers how reliable your data is Calculated by: Assumes normal distribution Standard deviation Given a specific confidence level Number of data point Calculate in Excel Improve the interval By increasing the number of samples CS110 Personal Computing 11
12 Improve the interval Decrease the confidence level Median The numerical value separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median can be used as a measure of location when a distribution is skewed when end-values are not known when one requires reduced importance to be attached to outliers A disadvantage of the median is the difficulty of handling it theoretically Median Given the following sequence, what would be the median of the values: 1,2,2,3,3,3,4,5,5,6,6,6,14 CS110 Personal Computing 12
13 In Excel Mode In statistics, the mode is the value that occurs most frequently in a data set or a probability distribution Very useful for discreet functions rather than continuous functions If more than one, the series can described as bimodal or multimodal Mode What would be the mode of the series: 1,2,3,3,4,5,6,7,7,7,8,8,9,10,10,11,12 How about: 1,2,3,3,4,5,5,6,6 1,2,3,3,4,5,5,5,6,6,6 CS110 Personal Computing 13
14 In Excel References CS110 Personal Computing 14
Midrange: mean of highest and lowest scores. easy to compute, rough estimate, rarely used
Measures of Central Tendency Mode: most frequent score. best average for nominal data sometimes none or multiple modes in a sample bimodal or multimodal distributions indicate several groups included in
More informationPsychology 310 Exam1 FormA Student Name:
Psychology 310 Exam1 FormA Student Name: 1 Compute the sample mean X forthefollowing5numbers: 1,4,2,3,4 (a) 2. 8 (b) 3.00 (c) 2. 24 (d) 1. 4 (e) None of the above are correct 2 Compute the sample variance
More informationStatistics 1. Edexcel Notes S1. Mathematical Model. A mathematical model is a simplification of a real world problem.
Statistics 1 Mathematical Model A mathematical model is a simplification of a real world problem. 1. A real world problem is observed. 2. A mathematical model is thought up. 3. The model is used to make
More informationPerhaps the most important measure of location is the mean (average). Sample mean: where n = sample size. Arrange the values from smallest to largest:
1 Chapter 3 - Descriptive stats: Numerical measures 3.1 Measures of Location Mean Perhaps the most important measure of location is the mean (average). Sample mean: where n = sample size Example: The number
More informationChapter 4.notebook. August 30, 2017
Sep 1 7:53 AM Sep 1 8:21 AM Sep 1 8:21 AM 1 Sep 1 8:23 AM Sep 1 8:23 AM Sep 1 8:23 AM SOCS When describing a distribution, make sure to always tell about three things: shape, outliers, center, and spread
More informationLast Lecture. Distinguish Populations from Samples. Knowing different Sampling Techniques. Distinguish Parameters from Statistics
Last Lecture Distinguish Populations from Samples Importance of identifying a population and well chosen sample Knowing different Sampling Techniques Distinguish Parameters from Statistics Knowing different
More informationDescriptive Statistics-I. Dr Mahmoud Alhussami
Descriptive Statistics-I Dr Mahmoud Alhussami Biostatistics What is the biostatistics? A branch of applied math. that deals with collecting, organizing and interpreting data using well-defined procedures.
More informationAlgebra 2. Outliers. Measures of Central Tendency (Mean, Median, Mode) Standard Deviation Normal Distribution (Bell Curves)
Algebra 2 Outliers Measures of Central Tendency (Mean, Median, Mode) Standard Deviation Normal Distribution (Bell Curves) Algebra 2 Notes #1 Chp 12 Outliers In a set of numbers, sometimes there will be
More informationCS 147: Computer Systems Performance Analysis
CS 147: Computer Systems Performance Analysis Summarizing Variability and Determining Distributions CS 147: Computer Systems Performance Analysis Summarizing Variability and Determining Distributions 1
More informationCHAPTER 4 VARIABILITY ANALYSES. Chapter 3 introduced the mode, median, and mean as tools for summarizing the
CHAPTER 4 VARIABILITY ANALYSES Chapter 3 introduced the mode, median, and mean as tools for summarizing the information provided in an distribution of data. Measures of central tendency are often useful
More informationAnd how to do them. Denise L Seman City of Youngstown
And how to do them Denise L Seman City of Youngstown Quality Control (QC) is defined as the process of detecting analytical errors to ensure both reliability and accuracy of the data generated QC can be
More informationMEASURES OF LOCATION AND SPREAD
MEASURES OF LOCATION AND SPREAD Frequency distributions and other methods of data summarization and presentation explained in the previous lectures provide a fairly detailed description of the data and
More informationDescribing Data: Numerical Measures
Describing Data: Numerical Measures Chapter 3 Learning Objectives Calculate the arithmetic mean, weighted mean, geometric mean, median, and the mode. Explain the characteristics, uses, advantages, and
More informationVocabulary: Samples and Populations
Vocabulary: Samples and Populations Concept Different types of data Categorical data results when the question asked in a survey or sample can be answered with a nonnumerical answer. For example if we
More informationChapter 2. Mean and Standard Deviation
Chapter 2. Mean and Standard Deviation The median is known as a measure of location; that is, it tells us where the data are. As stated in, we do not need to know all the exact values to calculate the
More informationStatistics and parameters
Statistics and parameters Tables, histograms and other charts are used to summarize large amounts of data. Often, an even more extreme summary is desirable. Statistics and parameters are numbers that characterize
More informationMeasures of the Location of the Data
Measures of the Location of the Data 1. 5. Mark has 51 films in his collection. Each movie comes with a rating on a scale from 0.0 to 10.0. The following table displays the ratings of the aforementioned
More informationSolving Equations. Lesson Fifteen. Aims. Context. The aim of this lesson is to enable you to: solve linear equations
Mathematics GCSE Module Four: Basic Algebra Lesson Fifteen Aims The aim of this lesson is to enable you to: solve linear equations solve linear equations from their graph solve simultaneous equations from
More informationChapter 3. Measuring data
Chapter 3 Measuring data 1 Measuring data versus presenting data We present data to help us draw meaning from it But pictures of data are subjective They re also not susceptible to rigorous inference Measuring
More informationPerformance of fourth-grade students on an agility test
Starter Ch. 5 2005 #1a CW Ch. 4: Regression L1 L2 87 88 84 86 83 73 81 67 78 83 65 80 50 78 78? 93? 86? Create a scatterplot Find the equation of the regression line Predict the scores Chapter 5: Understanding
More informationChapter 4. Displaying and Summarizing. Quantitative Data
STAT 141 Introduction to Statistics Chapter 4 Displaying and Summarizing Quantitative Data Bin Zou (bzou@ualberta.ca) STAT 141 University of Alberta Winter 2015 1 / 31 4.1 Histograms 1 We divide the range
More informationDescribing Center: Mean and Median Section 5.4
Describing Center: Mean and Median Section 5.4 Look at table 5.2 at the right. We are going to make the dotplot of the city gas mileages of midsize cars. How to describe the center of a distribution: x
More informationAlgebra 1: Semester 2 Practice Final Unofficial Worked Out Solutions by Earl Whitney
Algebra 1: Semester 2 Practice Final Unofficial Worked Out Solutions by Earl Whitney 1. The situation described in this problem involves probability without replacement. Because of this, the probabilities
More informationDEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS QM 120. Spring 2008
DEPARTMENT OF QUANTITATIVE METHODS & INFORMATION SYSTEMS Introduction to Business Statistics QM 120 Chapter 3 Spring 2008 Measures of central tendency for ungrouped data 2 Graphs are very helpful to describe
More informationPractice Questions for Exam 1
Practice Questions for Exam 1 1. A used car lot evaluates their cars on a number of features as they arrive in the lot in order to determine their worth. Among the features looked at are miles per gallon
More informationCHAPTER 1. Introduction
CHAPTER 1 Introduction Engineers and scientists are constantly exposed to collections of facts, or data. The discipline of statistics provides methods for organizing and summarizing data, and for drawing
More informationSummarizing and Displaying Measurement Data/Understanding and Comparing Distributions
Summarizing and Displaying Measurement Data/Understanding and Comparing Distributions Histograms, Mean, Median, Five-Number Summary and Boxplots, Standard Deviation Thought Questions 1. If you were to
More informationSTATISTICS 1 REVISION NOTES
STATISTICS 1 REVISION NOTES Statistical Model Representing and summarising Sample Data Key words: Quantitative Data This is data in NUMERICAL FORM such as shoe size, height etc. Qualitative Data This is
More informationChapter 3 Statistics for Describing, Exploring, and Comparing Data. Section 3-1: Overview. 3-2 Measures of Center. Definition. Key Concept.
Chapter 3 Statistics for Describing, Exploring, and Comparing Data 3-1 Overview 3- Measures of Center 3-3 Measures of Variation Section 3-1: Overview Descriptive Statistics summarize or describe the important
More information- a value calculated or derived from the data.
Descriptive statistics: Note: I'm assuming you know some basics. If you don't, please read chapter 1 on your own. It's pretty easy material, and it gives you a good background as to why we need statistics.
More information2/2/2015 GEOGRAPHY 204: STATISTICAL PROBLEM SOLVING IN GEOGRAPHY MEASURES OF CENTRAL TENDENCY CHAPTER 3: DESCRIPTIVE STATISTICS AND GRAPHICS
Spring 2015: Lembo GEOGRAPHY 204: STATISTICAL PROBLEM SOLVING IN GEOGRAPHY CHAPTER 3: DESCRIPTIVE STATISTICS AND GRAPHICS Descriptive statistics concise and easily understood summary of data set characteristics
More informationChapter 1: Introduction. Material from Devore s book (Ed 8), and Cengagebrain.com
1 Chapter 1: Introduction Material from Devore s book (Ed 8), and Cengagebrain.com Populations and Samples An investigation of some characteristic of a population of interest. Example: Say you want to
More informationCS 147: Computer Systems Performance Analysis
CS 147: Computer Systems Performance Analysis Advanced Regression Techniques CS 147: Computer Systems Performance Analysis Advanced Regression Techniques 1 / 31 Overview Overview Overview Common Transformations
More information6-3 Solving Systems by Elimination
Another method for solving systems of equations is elimination. Like substitution, the goal of elimination is to get one equation that has only one variable. To do this by elimination, you add the two
More information2 Which graph shows the solution to the following
1 In order for Josh to graph the solution to the inequality 4x 2y > 6, which of the following steps does he need to use? Select all that apply. A. Shade above the line. B. Draw the line y = 2x 3 as a solid
More informationChapter 1:Descriptive statistics
Slide 1.1 Chapter 1:Descriptive statistics Descriptive statistics summarises a mass of information. We may use graphical and/or numerical methods Examples of the former are the bar chart and XY chart,
More informationCRP 272 Introduction To Regression Analysis
CRP 272 Introduction To Regression Analysis 30 Relationships Among Two Variables: Interpretations One variable is used to explain another variable X Variable Independent Variable Explaining Variable Exogenous
More information1 Work, Power, and Machines
CHAPTER 13 1 Work, Power, and Machines SECTION Work and Energy KEY IDEAS As you read this section, keep these questions in mind: What is work, and how is it measured? How are work and power related? How
More informationCS 160: Lecture 16. Quantitative Studies. Outline. Random variables and trials. Random variables. Qualitative vs. Quantitative Studies
Qualitative vs. Quantitative Studies CS 160: Lecture 16 Professor John Canny Qualitative: What we ve been doing so far: * Contextual Inquiry: trying to understand user s tasks and their conceptual model.
More informationHonors Algebra 1 - Fall Final Review
Name: Period Date: Honors Algebra 1 - Fall Final Review This review packet is due at the beginning of your final exam. In addition to this packet, you should study each of your unit reviews and your notes.
More informationChapter 15 Sampling Distribution Models
Chapter 15 Sampling Distribution Models 1 15.1 Sampling Distribution of a Proportion 2 Sampling About Evolution According to a Gallup poll, 43% believe in evolution. Assume this is true of all Americans.
More informationMAT Mathematics in Today's World
MAT 1000 Mathematics in Today's World Last Time 1. Three keys to summarize a collection of data: shape, center, spread. 2. Can measure spread with the fivenumber summary. 3. The five-number summary can
More informationPAPER A numerical answers. 1 Proof by forming quadratic >0 then sh0w quadratic has no solutions using discriminant b 2 4ac < 0 or similar method
PAPER A numerical answers 1 Proof by forming quadratic >0 then sh0w quadratic has no solutions using discriminant b 4ac < 0 or similar method 9a 51 + 04px + 4608 p x + 576 p x + a 5y + 9x 1 = 0 9b p =
More information1. AN INTRODUCTION TO DESCRIPTIVE STATISTICS. No great deed, private or public, has ever been undertaken in a bliss of certainty.
CIVL 3103 Approximation and Uncertainty J.W. Hurley, R.W. Meier 1. AN INTRODUCTION TO DESCRIPTIVE STATISTICS No great deed, private or public, has ever been undertaken in a bliss of certainty. - Leon Wieseltier
More information1 Measures of the Center of a Distribution
1 Measures of the Center of a Distribution Qualitative descriptions of the shape of a distribution are important and useful. But we will often desire the precision of numerical summaries as well. Two aspects
More informationUnit 2. Describing Data: Numerical
Unit 2 Describing Data: Numerical Describing Data Numerically Describing Data Numerically Central Tendency Arithmetic Mean Median Mode Variation Range Interquartile Range Variance Standard Deviation Coefficient
More informationUnit Two Descriptive Biostatistics. Dr Mahmoud Alhussami
Unit Two Descriptive Biostatistics Dr Mahmoud Alhussami Descriptive Biostatistics The best way to work with data is to summarize and organize them. Numbers that have not been summarized and organized are
More informationSampling Distributions: Central Limit Theorem
Review for Exam 2 Sampling Distributions: Central Limit Theorem Conceptually, we can break up the theorem into three parts: 1. The mean (µ M ) of a population of sample means (M) is equal to the mean (µ)
More informationRatios, Proportions, Unit Conversions, and the Factor-Label Method
Ratios, Proportions, Unit Conversions, and the Factor-Label Method Math 0, Littlefield I don t know why, but presentations about ratios and proportions are often confused and fragmented. The one in your
More informationS2 QUESTIONS TAKEN FROM JANUARY 2006, JANUARY 2007, JANUARY 2008, JANUARY 2009
S2 QUESTIONS TAKEN FROM JANUARY 2006, JANUARY 2007, JANUARY 2008, JANUARY 2009 SECTION 1 The binomial and Poisson distributions. Students will be expected to use these distributions to model a real-world
More informationCIVL 7012/8012. Collection and Analysis of Information
CIVL 7012/8012 Collection and Analysis of Information Uncertainty in Engineering Statistics deals with the collection and analysis of data to solve real-world problems. Uncertainty is inherent in all real
More informationHomework 7. Name: ID# Section
Homework 7 Name: ID# Section 1 Find the probabilities for each of the following using the standard normal distribution. 1. P(0 < z < 1.69) 2. P(-1.57 < z < 0) 3. P(z > 1.16) 4. P(z < -1.77) 5. P(-2.46
More informationWeek 1: Intro to R and EDA
Statistical Methods APPM 4570/5570, STAT 4000/5000 Populations and Samples 1 Week 1: Intro to R and EDA Introduction to EDA Objective: study of a characteristic (measurable quantity, random variable) for
More informationVariables, distributions, and samples (cont.) Phil 12: Logic and Decision Making Fall 2010 UC San Diego 10/18/2010
Variables, distributions, and samples (cont.) Phil 12: Logic and Decision Making Fall 2010 UC San Diego 10/18/2010 Review Recording observations - Must extract that which is to be analyzed: coding systems,
More informationMatrices. Introduction to Matrices Class Work How many rows and columns does each matrix have? 1. A = ( ) 2.
Matrices Introduction to Matrices How many rows and columns does each matrix have? 1. A = ( 2 4 1 5 ) 2. B = ( 1 0) 0 2. C = ( 5 ) 1 4 2 4 5 1 4 8 4. D = ( ) 0 2 1 6 5. E 5x1 6. F 2x4 Identify the given
More informationAddition & Subtraction of Polynomials
Chapter 12 Addition & Subtraction of Polynomials Monomials and Addition, 1 Laurent Polynomials, 3 Plain Polynomials, 6 Addition, 8 Subtraction, 10. While, as we saw in the preceding chapter, monomials
More informationChapter 5 Simplifying Formulas and Solving Equations
Chapter 5 Simplifying Formulas and Solving Equations Look at the geometry formula for Perimeter of a rectangle P = L W L W. Can this formula be written in a simpler way? If it is true, that we can simplify
More informationLesson 3.4 Exercises, pages
Lesson 3. Exercises, pages 17 A. Identify the values of a, b, and c to make each quadratic equation match the general form ax + bx + c = 0. a) x + 9x - = 0 b) x - 11x = 0 Compare each equation to ax bx
More informationHypothesis testing: Steps
Review for Exam 2 Hypothesis testing: Steps Exam 2 Review 1. Determine appropriate test and hypotheses 2. Use distribution table to find critical statistic value(s) representing rejection region 3. Compute
More informationCreated by T. Madas. Candidates may use any calculator allowed by the regulations of this examination.
IYGB GCE Mathematics MMS Advanced Level Practice Paper Q Difficulty Rating: 3.400/0.6993 Time: 3 hours Candidates may use any calculator allowed by the regulations of this examination. Information for
More informationΣ x i. Sigma Notation
Sigma Notation The mathematical notation that is used most often in the formulation of statistics is the summation notation The uppercase Greek letter Σ (sigma) is used as shorthand, as a way to indicate
More informationFinal Exam - Solutions
Ecn 102 - Analysis of Economic Data University of California - Davis March 19, 2010 Instructor: John Parman Final Exam - Solutions You have until 5:30pm to complete this exam. Please remember to put your
More informationUnit 1: Statistics. Mrs. Valentine Math III
Unit 1: Statistics Mrs. Valentine Math III 1.1 Analyzing Data Statistics Study, analysis, and interpretation of data Find measure of central tendency Mean average of the data Median Odd # data pts: middle
More informationLecture 2 and Lecture 3
Lecture 2 and Lecture 3 1 Lecture 2 and Lecture 3 We can describe distributions using 3 characteristics: shape, center and spread. These characteristics have been discussed since the foundation of statistics.
More informationPart 7: Glossary Overview
Part 7: Glossary Overview In this Part This Part covers the following topic Topic See Page 7-1-1 Introduction This section provides an alphabetical list of all the terms used in a STEPS surveillance with
More informationPredicting AGI: What can we say when we know so little?
Predicting AGI: What can we say when we know so little? Fallenstein, Benja Mennen, Alex December 2, 2013 (Working Paper) 1 Time to taxi Our situation now looks fairly similar to our situation 20 years
More informationElementary Algebra Study Guide Some Basic Facts This section will cover the following topics
Elementary Algebra Study Guide Some Basic Facts This section will cover the following topics Notation Order of Operations Notation Math is a language of its own. It has vocabulary and punctuation (notation)
More informationGAP CLOSING. Algebraic Expressions. Intermediate / Senior Facilitator s Guide
GAP CLOSING Algebraic Expressions Intermediate / Senior Facilitator s Guide Topic 6 Algebraic Expressions Diagnostic...5 Administer the diagnostic...5 Using diagnostic results to personalize interventions...5
More informationMeasures of Central Tendency
Measures of Central Tendency Summary Measures Summary Measures Central Tendency Mean Median Mode Quartile Range Variance Variation Coefficient of Variation Standard Deviation Measures of Central Tendency
More informationChapter 6: SAMPLING DISTRIBUTIONS
Chapter 6: SAMPLING DISTRIBUTIONS Read Section 1.5 Graphical methods may not always be sufficient for describing data. Numerical measures can be created for both populations and samples. Definition A numerical
More informationLecture 1: Description of Data. Readings: Sections 1.2,
Lecture 1: Description of Data Readings: Sections 1.,.1-.3 1 Variable Example 1 a. Write two complete and grammatically correct sentences, explaining your primary reason for taking this course and then
More informationDescribing Data: Numerical Measures GOALS. Why a Numeric Approach? Chapter 3 Dr. Richard Jerz
Describing Data: Numerical Measures Chapter 3 Dr. Richard Jerz 1 GOALS Calculate the arithmetic mean, weighted mean, median, and mode Explain the characteristics, uses, advantages, and disadvantages of
More informationHow Accurate is My Forecast?
How Accurate is My Forecast? Tao Hong, PhD Utilities Business Unit, SAS 15 May 2012 PLEASE STAND BY Today s event will begin at 11:00am EDT The audio portion of the presentation will be heard through your
More informationSTATISTICS/MATH /1760 SHANNON MYERS
STATISTICS/MATH 103 11/1760 SHANNON MYERS π 100 POINTS POSSIBLE π YOUR WORK MUST SUPPORT YOUR ANSWER FOR FULL CREDIT TO BE AWARDED π YOU MAY USE A SCIENTIFIC AND/OR A TI-83/84/85/86 CALCULATOR ONCE YOU
More informationChapter 3. Data Description. McGraw-Hill, Bluman, 7 th ed, Chapter 3
Chapter 3 Data Description McGraw-Hill, Bluman, 7 th ed, Chapter 3 1 Chapter 3 Overview Introduction 3-1 Measures of Central Tendency 3-2 Measures of Variation 3-3 Measures of Position 3-4 Exploratory
More informationDirected Reading B. Section: Tools and Models in Science TOOLS IN SCIENCE MAKING MEASUREMENTS. is also know as the metric system.
Skills Worksheet Directed Reading B Section: Tools and Models in Science TOOLS IN SCIENCE 1. What is a tool? a. anything with a handle b. anything that gives off energy c. anything that requires electricity
More informationModeling: Start to Finish
A model for Vehicular Stopping Distance 64 Modeling: Start to Finish Example. Vehicular Stopping Distance Background: In driver s training, you learn a rule for how far behind other cars you are supposed
More informationMeasures of Central Tendency and their dispersion and applications. Acknowledgement: Dr Muslima Ejaz
Measures of Central Tendency and their dispersion and applications Acknowledgement: Dr Muslima Ejaz LEARNING OBJECTIVES: Compute and distinguish between the uses of measures of central tendency: mean,
More informationIn this investigation you will use the statistics skills that you learned the to display and analyze a cup of peanut M&Ms.
M&M Madness In this investigation you will use the statistics skills that you learned the to display and analyze a cup of peanut M&Ms. Part I: Categorical Analysis: M&M Color Distribution 1. Record the
More informationChapter 10. Lesson is special because any non-zero number divided by itself is 1, and anything multiplied by 1 remains the same.
Chapter 10 Lesson 10.1.1 10-1. 1 is special because any non-zero number divided by itself is 1, and anything multiplied by 1 remains the same. 10-2. a. yes You cannot divide by zero. c. Yes; x 3 d. Answers
More informationare the objects described by a set of data. They may be people, animals or things.
( c ) E p s t e i n, C a r t e r a n d B o l l i n g e r 2016 C h a p t e r 5 : E x p l o r i n g D a t a : D i s t r i b u t i o n s P a g e 1 CHAPTER 5: EXPLORING DATA DISTRIBUTIONS 5.1 Creating Histograms
More informationLecture 6: Chapter 4, Section 2 Quantitative Variables (Displays, Begin Summaries)
Lecture 6: Chapter 4, Section 2 Quantitative Variables (Displays, Begin Summaries) Summarize with Shape, Center, Spread Displays: Stemplots, Histograms Five Number Summary, Outliers, Boxplots Cengage Learning
More informationIntroduction to Basic Statistics Version 2
Introduction to Basic Statistics Version 2 Pat Hammett, Ph.D. University of Michigan 2014 Instructor Comments: This document contains a brief overview of basic statistics and core terminology/concepts
More informationData Presentation. Naureen Ghani. May 4, 2018
Data Presentation Naureen Ghani May 4, 2018 Data is only as good as how it is presented. How do you take hundreds or thousands of data points and create something a human can understand? This is a problem
More informationA Bayesian Method for Guessing the Extreme Values in a Data Set
A Bayesian Method for Guessing the Extreme Values in a Data Set Mingxi Wu University of Florida May, 2008 Mingxi Wu (University of Florida) May, 2008 1 / 74 Outline Problem Definition Example Applications
More informationHistograms allow a visual interpretation
Chapter 4: Displaying and Summarizing i Quantitative Data s allow a visual interpretation of quantitative (numerical) data by indicating the number of data points that lie within a range of values, called
More informationSem. 1 Review Ch. 1-3
AP Stats Sem. 1 Review Ch. 1-3 Name 1. You measure the age, marital status and earned income of an SRS of 1463 women. The number and type of variables you have measured is a. 1463; all quantitative. b.
More informationChapter 1: Introduction. Material from Devore s book (Ed 8), and Cengagebrain.com
1 Chapter 1: Introduction Material from Devore s book (Ed 8), and Cengagebrain.com Populations and Samples An investigation of some characteristic of a population of interest. Example: Say you want to
More informationChapter 1 - Lecture 3 Measures of Location
Chapter 1 - Lecture 3 of Location August 31st, 2009 Chapter 1 - Lecture 3 of Location General Types of measures Median Skewness Chapter 1 - Lecture 3 of Location Outline General Types of measures What
More informationPrecision Correcting for Random Error
Precision Correcting for Random Error The following material should be read thoroughly before your 1 st Lab. The Statistical Handling of Data Our experimental inquiries into the workings of physical reality
More informationTouring Around the Islands of Atlantic Canada
Lesson Overview Touring Around the Islands of Atlantic Canada In this lesson, students will examine the history and heritage of the islands of Atlantic Canada and examine their similarities and differences.
More information15-451/651: Design & Analysis of Algorithms September 13, 2018 Lecture #6: Streaming Algorithms last changed: August 30, 2018
15-451/651: Design & Analysis of Algorithms September 13, 2018 Lecture #6: Streaming Algorithms last changed: August 30, 2018 Today we ll talk about a topic that is both very old (as far as computer science
More informationALGEBRA 1 FINAL EXAM 2006
Overall instructions: Your Name Teacher ALGEBRA FINAL EXAM 2006 There is a mix of easier and harder problems. Don t give up if you see some questions that you don t know how to answer. Try moving on to
More information3 GRAPHICAL DISPLAYS OF DATA
some without indicating nonnormality. If a sample of 30 observations contains 4 outliers, two of which are extreme, would it be reasonable to assume the population from which the data were collected has
More informationCh. 3 Review - LSRL AP Stats
Ch. 3 Review - LSRL AP Stats Multiple Choice Identify the choice that best completes the statement or answers the question. Scenario 3-1 The height (in feet) and volume (in cubic feet) of usable lumber
More informationALGEBRA 1 SEMESTER 1 INSTRUCTIONAL MATERIALS Courses: Algebra 1 S1 (#2201) and Foundations in Algebra 1 S1 (#7769)
Multiple Choice: Identify the choice that best completes the statement or answers the question. 1. Ramal goes to the grocery store and buys pounds of apples and pounds of bananas. Apples cost dollars per
More informationChapter 6. Exploring Data: Relationships. Solutions. Exercises:
Chapter 6 Exploring Data: Relationships Solutions Exercises: 1. (a) It is more reasonable to explore study time as an explanatory variable and the exam grade as the response variable. (b) It is more reasonable
More informationChapter 6 Group Activity - SOLUTIONS
Chapter 6 Group Activity - SOLUTIONS Group Activity Summarizing a Distribution 1. The following data are the number of credit hours taken by Math 105 students during a summer term. You will be analyzing
More informationFactorial Designs. Outline. Definition. Factorial designs. Factorial designs. Higher order interactions
1 Factorial Designs Outline 2 Factorial designs Conditions vs. levels Naming Why use them Main effects Interactions Simple main effect Graphical definition Additivity definition Factorial designs Independent
More informationPractice Algebra 1 SCP Midterm 2015
Practice Algebra 1 SCP Midterm 01 Multiple Choice Identif the choice that best completes the statement or answers the question. Solve the equation. 1. = m 0 7 7 7 7. 0 7 60. x 6 = 7 8 19 1 1 19 1..1x +.
More information