Instrumentation (cont.) Statistics vs. Parameters. Descriptive Statistics. Types of Numerical Data
|
|
- Mabel Kimberly Carroll
- 6 years ago
- Views:
Transcription
1 Norm-Referenced vs. Criterion- Referenced Instruments Instrumentation (cont.) October 1, 2007 Note: Measurement Plan Due Next Week All derived scores give meaning to individual scores by comparing them to the scores of a group. The group used to determine derived scores is called the norm group and the instruments that provide such scores are referred to as norm-referenced instruments. An alternative to the use of achievement or performance instruments is to use a criterion-referenced test. This is based on a specific goal or target (criterion) for each learner to achieve. The difference between the two tests is that the criterion referenced tests focus more directly on instruction. Statistics vs. Parameters Descriptive Statistics A parameter is a characteristic of a population. It is a numerical or graphic way to summarize data obtained from the population A statistic is a characteristic of a sample. It is a numerical or graphic way to summarize data obtained from a sample Types of Numerical Data Four Types of Measurement Scales There are two fundamental types of numerical data: 1) Categorical data: obtained by determining the frequency of occurrences in each of several categories 2) Quantitative data: obtained by determining placement on a scale that indicates amount or degree 1
2 Techniques for Summarizing and Presenting Quantitative Data Summary Measures Visual Frequency Distributions Histograms Stem and Leaf Plots Distribution curves Numerical Central Tendency Variability Central Tendency Arithmetic Mean Median Summary Measures Mode Range Variance Variation Standard Deviation Measures of Central Tendency Central Tendency Average (Mean) Median Mode X = μ = n i= 1 N i= 1 n N X X i i Mean The most common measure of central tendency Affected by extreme values (outliers) Mean = 5 Mean = 6 Median Robust measure of central tendency Not affected by extreme values Median = 5 Median = 5 In an Ordered array, median is the middle number If n or N is odd, median is the middle number If n or N is even, median is the average of the two middle numbers Mode A measure of central tendency Value that occurs most often Not affected by extreme values Used for either numerical or categorical data There may may be no mode There may be several modes Mode = No Mode 2
3 Variability Refers to the extent to which the scores on a quantitative variable in a distribution are spread out. The range represents the difference between the highest and lowest scores in a distribution. A five number summary reports the lowest, the first quartile, the median, the third quartile, and highest score. Five number summaries are often portrayed graphically by the use of box plots. Variance The Variance, s 2, represents the amount of variability of the data relative to their mean As shown below, the variance is the average of the squared deviations of the observations about their mean 2 ( x ) 2 i x s = n 1 Standard Deviation Calculation of the Variance and Standard Deviation of a Distribution (Definitional formula) Considered the most useful index of variability. It is a single number that represents the spread of a distribution. If a distribution is normal, then the mean plus or minus 3 SD will encompass about 99% of all scores in the distribution. Raw Score Mean X X (X X) Variance (SD 2 Σ(X X)2 ) = N-1 = 3640 = Standard deviation (SD) = Σ(X X) 2 N-1 Comparing Standard Deviations Facts about the Normal Distribution Data A Data B Data C Mean = 15.5 S = Mean = 15.5 S =.9258 Mean = 15.5 S = % of all the observations fall on each side of the mean. 68% of scores fall within 1 SD of the mean in a normal distribution. 27% of the observations fall between 1 and 2 SD from the mean. 99.7% of all scores fall within 3 SD of the mean. This is often referred to as the rule 3
4 The Normal Curve Different Distributions Compared Fifty Percent of All Scores in a Normal Curve Fall on Each Side of the Mean Probabilities Under the Normal Curve Interpreting the standard deviation We can compare the standard deviations of different samples to determine which has the greatest dispersion. Example A spelling test given to third-grader children 10, 12, 12, 12, 13, 13, 14 xbar = s = 1.25 The same test given to second- through fourthgrade children. 2, 8, 9, 11, 15, 17, 20 xbar = s = 6.10 Standard Normal Distribution Z-scores Convert a distribution to: Have a mean = 0 Have standard deviation = 1 However, if the parent distribution is not normal the calculated z-scores will not be normally distributed. 4
5 The Standard Normal Distribution Why do we calculate z-scores? Z-scores A descriptive statistic that represents the distance between an observed score and the mean relative to the standard deviation xi x z = s x μ z = i σ To compare two different measures e.g., Math score to reading score, weight to height. Area under the curve Can be used to calculate what proportion of scores are between different scores or to calculate what proportion of scores are greater than or less than a particular score. Used to set cut score for screening instruments. From the spelling example A spelling test given to third-grader children 10, 12, 12, 12, 13, 13, 14 xbar = s = 1.25 What is the z-score of child who scores 11? What proportion of scores are greater? What proportion of scores are less? What about a child who scores 12? Correlation Correlation Coefficients Positive Correlation Pearson product-moment correlation The relationship between two variables of degree. Positive: As one variable increases (or decreases) so does the other. Negative: As one variable increases the other decreases. Magnitude or strength of relationship to Correlation does not equate to causation 5
6 Negative Correlation No Correlation Correlations Negative Correlation Thickness of scatter plot determines strength of correlation, not slope of line. For example see: Remember correlation does not equate causation. Validity and Reliability Validity and Reliability Chapter 8 Validity is an important consideration in the choice of an instrument to be used in a research investigation It should measure what it is supposed to measure Researchers want instruments that will allow them to make warranted conclusions about the characteristics of the subjects they study Reliability is another important consideration, since researchers want consistent results from instrumentation Consistency gives researchers confidence that the results actually represent the achievement of the individuals involved 6
7 Reliability Test-retest reliability Inter-rater reliability Parallel forms reliability Internal consistency (a.k.a. Cronbach s alpha) Validity Face Does it appear to measure what it purports to measure? Content Do the items cover the domain? Construct Does it measure the unobservable attribute that it purports to measure? Criterion Predictive Concurrent Consequential Validity Types of validity (cont.) The scores The construct Here the instrument samples some and only of the construct Types of validity The scores The instrument The construct Here the instrument fails to sample ANY of the construct Here the instrument samples all and more of the construct The scores 7
8 Perfection! The construct Here the instrument samples some but not all of the construct The construct and the scores! The scores Reliability and Validity 8
P8130: Biostatistical Methods I
P8130: Biostatistical Methods I Lecture 2: Descriptive Statistics Cody Chiuzan, PhD Department of Biostatistics Mailman School of Public Health (MSPH) Lecture 1: Recap Intro to Biostatistics Types of Data
More informationChapter 2 Descriptive Statistics
Chapter 2 Descriptive Statistics Lecture 1: Measures of Central Tendency and Dispersion Donald E. Mercante, PhD Biostatistics May 2010 Biostatistics (LSUHSC) Chapter 2 05/10 1 / 34 Lecture 1: Descriptive
More informationDescriptive Univariate Statistics and Bivariate Correlation
ESC 100 Exploring Engineering Descriptive Univariate Statistics and Bivariate Correlation Instructor: Sudhir Khetan, Ph.D. Wednesday/Friday, October 17/19, 2012 The Central Dogma of Statistics used to
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 informationDescriptive Data Summarization
Descriptive Data Summarization Descriptive data summarization gives the general characteristics of the data and identify the presence of noise or outliers, which is useful for successful data cleaning
More informationDescribing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
More information2.0 Lesson Plan. Answer Questions. Summary Statistics. Histograms. The Normal Distribution. Using the Standard Normal Table
2.0 Lesson Plan Answer Questions 1 Summary Statistics Histograms The Normal Distribution Using the Standard Normal Table 2. Summary Statistics Given a collection of data, one needs to find representations
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 informationClass 11 Maths Chapter 15. Statistics
1 P a g e Class 11 Maths Chapter 15. Statistics Statistics is the Science of collection, organization, presentation, analysis and interpretation of the numerical data. Useful Terms 1. Limit of the Class
More informationAfter completing this chapter, you should be able to:
Chapter 2 Descriptive Statistics Chapter Goals After completing this chapter, you should be able to: Compute and interpret the mean, median, and mode for a set of data Find the range, variance, standard
More informationØ Set of mutually exclusive categories. Ø Classify or categorize subject. Ø No meaningful order to categorization.
Statistical Tools in Evaluation HPS 41 Fall 213 Dr. Joe G. Schmalfeldt Types of Scores Continuous Scores scores with a potentially infinite number of values. Discrete Scores scores limited to a specific
More informationDescriptive Statistics C H A P T E R 5 P P
Descriptive Statistics C H A P T E R 5 P P 1 1 0-130 Graphing data Frequency distributions Bar graphs Qualitative variable (categories) Bars don t touch Histograms Frequency polygons Quantitative variable
More informationDescribing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
More informationCollege Mathematics
Wisconsin Indianhead Technical College 10804107 College Mathematics Course Outcome Summary Course Information Description Instructional Level Total Credits 3.00 Total Hours 48.00 This course is designed
More informationContents. Acknowledgments. xix
Table of Preface Acknowledgments page xv xix 1 Introduction 1 The Role of the Computer in Data Analysis 1 Statistics: Descriptive and Inferential 2 Variables and Constants 3 The Measurement of Variables
More informationGlossary. The ISI glossary of statistical terms provides definitions in a number of different languages:
Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the
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 informationUnit Six Information. EOCT Domain & Weight: Algebra Connections to Statistics and Probability - 15%
GSE Algebra I Unit Six Information EOCT Domain & Weight: Algebra Connections to Statistics and Probability - 15% Curriculum Map: Describing Data Content Descriptors: Concept 1: Summarize, represent, and
More informationQuantitative Tools for Research
Quantitative Tools for Research KASHIF QADRI Descriptive Analysis Lecture Week 4 1 Overview Measurement of Central Tendency / Location Mean, Median & Mode Quantiles (Quartiles, Deciles, Percentiles) Measurement
More informationBasic Statistical Analysis
indexerrt.qxd 8/21/2002 9:47 AM Page 1 Corrected index pages for Sprinthall Basic Statistical Analysis Seventh Edition indexerrt.qxd 8/21/2002 9:47 AM Page 656 Index Abscissa, 24 AB-STAT, vii ADD-OR rule,
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 informationBNG 495 Capstone Design. Descriptive Statistics
BNG 495 Capstone Design Descriptive Statistics Overview The overall goal of this short course in statistics is to provide an introduction to descriptive and inferential statistical methods, with a focus
More informationPreliminary Statistics course. Lecture 1: Descriptive Statistics
Preliminary Statistics course Lecture 1: Descriptive Statistics Rory Macqueen (rm43@soas.ac.uk), September 2015 Organisational Sessions: 16-21 Sep. 10.00-13.00, V111 22-23 Sep. 15.00-18.00, V111 24 Sep.
More informationUNIT 3 CONCEPT OF DISPERSION
UNIT 3 CONCEPT OF DISPERSION Structure 3.0 Introduction 3.1 Objectives 3.2 Concept of Dispersion 3.2.1 Functions of Dispersion 3.2.2 Measures of Dispersion 3.2.3 Meaning of Dispersion 3.2.4 Absolute Dispersion
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 informationAlgebra I Learning Targets Chapter 1: Equations and Inequalities (one variable) Section Section Title Learning Target(s)
Chapter 1: Equations and Inequalities (one variable) Section Learning Target(s) I can 1.2 Evaluate and Simplify Algebraic Expressions 1. Evaluate and simplify numeric and algebraic expressions (order of
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 informationSections OPIM 303, Managerial Statistics H Guy Williams, 2006
Sections 3.1 3.5 The three major properties which describe a set of data: Central Tendency Variation Shape OPIM 303 Lecture 3 Page 1 Most sets of data show a distinct tendency to group or cluster around
More informationTOPIC: Descriptive Statistics Single Variable
TOPIC: Descriptive Statistics Single Variable I. Numerical data summary measurements A. Measures of Location. Measures of central tendency Mean; Median; Mode. Quantiles - measures of noncentral tendency
More informationA is one of the categories into which qualitative data can be classified.
Chapter 2 Methods for Describing Sets of Data 2.1 Describing qualitative data Recall qualitative data: non-numerical or categorical data Basic definitions: A is one of the categories into which qualitative
More informationCh. 17. DETERMINATION OF SAMPLE SIZE
LOGO Ch. 17. DETERMINATION OF SAMPLE SIZE Dr. Werner R. Murhadi www.wernermurhadi.wordpress.com Descriptive and Inferential Statistics descriptive statistics is Statistics which summarize and describe
More informationSTP 420 INTRODUCTION TO APPLIED STATISTICS NOTES
INTRODUCTION TO APPLIED STATISTICS NOTES PART - DATA CHAPTER LOOKING AT DATA - DISTRIBUTIONS Individuals objects described by a set of data (people, animals, things) - all the data for one individual make
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 informationModule 1. Identify parts of an expression using vocabulary such as term, equation, inequality
Common Core Standards Major Topic Key Skills Chapters Key Vocabulary Essential Questions Module 1 Pre- Requisites Skills: Students need to know how to add, subtract, multiply and divide. Students need
More informationChapter 3 Data Description
Chapter 3 Data Description Section 3.1: Measures of Central Tendency Section 3.2: Measures of Variation Section 3.3: Measures of Position Section 3.1: Measures of Central Tendency Definition of Average
More informationSTAT 200 Chapter 1 Looking at Data - Distributions
STAT 200 Chapter 1 Looking at Data - Distributions What is Statistics? Statistics is a science that involves the design of studies, data collection, summarizing and analyzing the data, interpreting the
More informationChapter 3. Data Description
Chapter 3. Data Description Graphical Methods Pie chart It is used to display the percentage of the total number of measurements falling into each of the categories of the variable by partition a circle.
More informationDescriptive statistics
Patrick Breheny February 6 Patrick Breheny to Biostatistics (171:161) 1/25 Tables and figures Human beings are not good at sifting through large streams of data; we understand data much better when it
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 informationStatistics for Managers using Microsoft Excel 6 th Edition
Statistics for Managers using Microsoft Excel 6 th Edition Chapter 3 Numerical Descriptive Measures 3-1 Learning Objectives In this chapter, you learn: To describe the properties of central tendency, variation,
More informationØ Set of mutually exclusive categories. Ø Classify or categorize subject. Ø No meaningful order to categorization.
Statistical Tools in Evaluation HPS 41 Dr. Joe G. Schmalfeldt Types of Scores Continuous Scores scores with a potentially infinite number of values. Discrete Scores scores limited to a specific number
More informationLesson Plan. Answer Questions. Summary Statistics. Histograms. The Normal Distribution. Using the Standard Normal Table
Lesson Plan Answer Questions Summary Statistics Histograms The Normal Distribution Using the Standard Normal Table 1 2. Summary Statistics Given a collection of data, one needs to find representations
More informationEvaluate algebraic expressions for given values of the variables.
Algebra I Unit Lesson Title Lesson Objectives 1 FOUNDATIONS OF ALGEBRA Variables and Expressions Exponents and Order of Operations Identify a variable expression and its components: variable, coefficient,
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 informationCURRICULUM UNIT MAP 1 ST QUARTER. COURSE TITLE: Applied Algebra 1 GRADE: 9
1 ST QUARTER Unit 1: Exploring Rational Numbers WEEK 1-3 Objectives: Write equations and formulas to solve application problems Compare order and plot rational and irrational numbers, including square
More information1-1. Chapter 1. Sampling and Descriptive Statistics by The McGraw-Hill Companies, Inc. All rights reserved.
1-1 Chapter 1 Sampling and Descriptive Statistics 1-2 Why Statistics? Deal with uncertainty in repeated scientific measurements Draw conclusions from data Design valid experiments and draw reliable conclusions
More informationFoundations of Algebra/Algebra/Math I Curriculum Map
*Standards N-Q.1, N-Q.2, N-Q.3 are not listed. These standards represent number sense and should be integrated throughout the units. *For each specific unit, learning targets are coded as F for Foundations
More informationFRANKLIN UNIVERSITY PROFICIENCY EXAM (FUPE) STUDY GUIDE
FRANKLIN UNIVERSITY PROFICIENCY EXAM (FUPE) STUDY GUIDE Course Title: Probability and Statistics (MATH 80) Recommended Textbook(s): Number & Type of Questions: Probability and Statistics for Engineers
More informationDETAILED CONTENTS PART I INTRODUCTION AND DESCRIPTIVE STATISTICS. 1. Introduction to Statistics
DETAILED CONTENTS About the Author Preface to the Instructor To the Student How to Use SPSS With This Book PART I INTRODUCTION AND DESCRIPTIVE STATISTICS 1. Introduction to Statistics 1.1 Descriptive and
More informationAlgebra vocabulary CARD SETS Frame Clip Art by Pixels & Ice Cream
Algebra vocabulary CARD SETS 1-7 www.lisatilmon.blogspot.com Frame Clip Art by Pixels & Ice Cream Algebra vocabulary Game Materials: one deck of Who has cards Objective: to match Who has words with definitions
More informationF78SC2 Notes 2 RJRC. If the interest rate is 5%, we substitute x = 0.05 in the formula. This gives
F78SC2 Notes 2 RJRC Algebra It is useful to use letters to represent numbers. We can use the rules of arithmetic to manipulate the formula and just substitute in the numbers at the end. Example: 100 invested
More informationDescribing Distributions
Describing Distributions With Numbers April 18, 2012 Summary Statistics. Measures of Center. Percentiles. Measures of Spread. A Summary Statement. Choosing Numerical Summaries. 1.0 What Are Summary Statistics?
More informationSTATISTICS. 1. Measures of Central Tendency
STATISTICS 1. Measures o Central Tendency Mode, median and mean For a sample o discrete data, the mode is the observation, x with the highest requency,. 1 N F For grouped data in a cumulative requency
More informationMgtOp 215 Chapter 3 Dr. Ahn
MgtOp 215 Chapter 3 Dr. Ahn Measures of central tendency (center, location): measures the middle point of a distribution or data; these include mean and median. Measures of dispersion (variability, spread):
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 informationDescriptive Statistics Example
Descriptive tatistics Example A manufacturer is investigating the operating life of laptop computer batteries. The following data are available. Life (min.) Life (min.) Life (min.) Life (min.) 130 145
More informationSlide 7.1. Theme 7. Correlation
Slide 7.1 Theme 7 Correlation Slide 7.2 Overview Researchers are often interested in exploring whether or not two variables are associated This lecture will consider Scatter plots Pearson correlation coefficient
More informationChapter 2: Tools for Exploring Univariate Data
Stats 11 (Fall 2004) Lecture Note Introduction to Statistical Methods for Business and Economics Instructor: Hongquan Xu Chapter 2: Tools for Exploring Univariate Data Section 2.1: Introduction What is
More informationMath Sec 4 CST Topic 7. Statistics. i.e: Add up all values and divide by the total number of values.
Measures of Central Tendency Statistics 1) Mean: The of all data values Mean= x = x 1+x 2 +x 3 + +x n n i.e: Add up all values and divide by the total number of values. 2) Mode: Most data value 3) Median:
More informationAlgebra , Martin-Gay
A Correlation of Algebra 1 2016, to the Common Core State Standards for Mathematics - Algebra I Introduction This document demonstrates how Pearson s High School Series by Elayn, 2016, meets the standards
More informationQUANTITATIVE DATA. UNIVARIATE DATA data for one variable
QUANTITATIVE DATA Recall that quantitative (numeric) data values are numbers where data take numerical values for which it is sensible to find averages, such as height, hourly pay, and pulse rates. UNIVARIATE
More informationSummarizing Measured Data
Summarizing Measured Data 12-1 Overview Basic Probability and Statistics Concepts: CDF, PDF, PMF, Mean, Variance, CoV, Normal Distribution Summarizing Data by a Single Number: Mean, Median, and Mode, Arithmetic,
More informationChapter2 Description of samples and populations. 2.1 Introduction.
Chapter2 Description of samples and populations. 2.1 Introduction. Statistics=science of analyzing data. Information collected (data) is gathered in terms of variables (characteristics of a subject that
More informationCK-12 Middle School Math Grade 8
CK-12 Middle School Math aligned with COMMON CORE MATH STATE STANDARDS INITIATIVE Middle School Standards for Math Content Common Core Math Standards for CK-12 Middle School Math The Number System (8.NS)
More informationNumerical Measures of Central Tendency
ҧ Numerical Measures of Central Tendency The central tendency of the set of measurements that is, the tendency of the data to cluster, or center, about certain numerical values; usually the Mean, Median
More informationChapter 7: Statistics Describing Data. Chapter 7: Statistics Describing Data 1 / 27
Chapter 7: Statistics Describing Data Chapter 7: Statistics Describing Data 1 / 27 Categorical Data Four ways to display categorical data: 1 Frequency and Relative Frequency Table 2 Bar graph (Pareto chart)
More informationEvaluate the expression if x = 2 and y = 5 6x 2y Original problem Substitute the values given into the expression and multiply
Name EVALUATING ALGEBRAIC EXPRESSIONS Objective: To evaluate an algebraic expression Example Evaluate the expression if and y = 5 6x y Original problem 6() ( 5) Substitute the values given into the expression
More informationREVIEW 8/2/2017 陈芳华东师大英语系
REVIEW Hypothesis testing starts with a null hypothesis and a null distribution. We compare what we have to the null distribution, if the result is too extreme to belong to the null distribution (p
More informationRevised: 2/19/09 Unit 1 Pre-Algebra Concepts and Operations Review
2/19/09 Unit 1 Pre-Algebra Concepts and Operations Review 1. How do algebraic concepts represent real-life situations? 2. Why are algebraic expressions and equations useful? 2. Operations on rational numbers
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 informationArea Formulas. Linear
Math Vocabulary and Formulas Approximate Area Arithmetic Sequences Average Rate of Change Axis of Symmetry Base Behavior of the Graph Bell Curve Bi-annually(with Compound Interest) Binomials Boundary Lines
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 informationAIM HIGH SCHOOL. Curriculum Map W. 12 Mile Road Farmington Hills, MI (248)
AIM HIGH SCHOOL Curriculum Map 2923 W. 12 Mile Road Farmington Hills, MI 48334 (248) 702-6922 www.aimhighschool.com COURSE TITLE: Statistics DESCRIPTION OF COURSE: PREREQUISITES: Algebra 2 Students will
More informationMATH 117 Statistical Methods for Management I Chapter Three
Jubail University College MATH 117 Statistical Methods for Management I Chapter Three This chapter covers the following topics: I. Measures of Center Tendency. 1. Mean for Ungrouped Data (Raw Data) 2.
More informationMS Algebra 1 Scope and Sequence Quarter 1 Overview
Quarter 1 Overview Equations, Inequalities, Absolute Value Students must master creating, solving, and In 7 th grade, students will have developed a Write, solve, and interpret multi-step equations analyzing
More informationDescribing Distributions With Numbers
Describing Distributions With Numbers October 24, 2012 What Do We Usually Summarize? Measures of Center. Percentiles. Measures of Spread. A Summary Statement. Choosing Numerical Summaries. 1.0 What Do
More informationGraphical Techniques Stem and Leaf Box plot Histograms Cumulative Frequency Distributions
Class #8 Wednesday 9 February 2011 What did we cover last time? Description & Inference Robustness & Resistance Median & Quartiles Location, Spread and Symmetry (parallels from classical statistics: Mean,
More informationSociology 6Z03 Review I
Sociology 6Z03 Review I John Fox McMaster University Fall 2016 John Fox (McMaster University) Sociology 6Z03 Review I Fall 2016 1 / 19 Outline: Review I Introduction Displaying Distributions Describing
More informationECLT 5810 Data Preprocessing. Prof. Wai Lam
ECLT 5810 Data Preprocessing Prof. Wai Lam Why Data Preprocessing? Data in the real world is imperfect incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate
More information1. How will an increase in the sample size affect the width of the confidence interval?
Study Guide Concept Questions 1. How will an increase in the sample size affect the width of the confidence interval? 2. How will an increase in the sample size affect the power of a statistical test?
More informationPractical Statistics for the Analytical Scientist Table of Contents
Practical Statistics for the Analytical Scientist Table of Contents Chapter 1 Introduction - Choosing the Correct Statistics 1.1 Introduction 1.2 Choosing the Right Statistical Procedures 1.2.1 Planning
More informationIntroduction to Statistics
Introduction to Statistics Data and Statistics Data consists of information coming from observations, counts, measurements, or responses. Statistics is the science of collecting, organizing, analyzing,
More informationCURRICULUM MAP. Course/Subject: Honors Math I Grade: 10 Teacher: Davis. Month: September (19 instructional days)
Month: September (19 instructional days) Numbers, Number Systems and Number Relationships Standard 2.1.11.A: Use operations (e.g., opposite, reciprocal, absolute value, raising to a power, finding roots,
More informationMIDTERM EXAMINATION (Spring 2011) STA301- Statistics and Probability
STA301- Statistics and Probability Solved MCQS From Midterm Papers March 19,2012 MC100401285 Moaaz.pk@gmail.com Mc100401285@gmail.com PSMD01 MIDTERM EXAMINATION (Spring 2011) STA301- Statistics and Probability
More informationGlades Middle School Summer Math Program
Summer Math Program Attention Cougars, It s time for SUMMER MATH!! Research studies have shown that during an extended summer vacation, children can lose an average of 2.6 months of knowledge. This is
More informationData: the pieces of information that have been observed and recorded, from an experiment or a survey
SESSION 13: STATISTICS KEY CONCEPTS: Collecting, organising and representing data Measures of central tendency Measures of dispersion X-PLANATION Data: the pieces of information that have been observed
More informationReview of Statistics 101
Review of Statistics 101 We review some important themes from the course 1. Introduction Statistics- Set of methods for collecting/analyzing data (the art and science of learning from data). Provides methods
More information2.1 Measures of Location (P.9-11)
MATH1015 Biostatistics Week.1 Measures of Location (P.9-11).1.1 Summation Notation Suppose that we observe n values from an experiment. This collection (or set) of n values is called a sample. Let x 1
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 informationStatistics I Chapter 2: Univariate data analysis
Statistics I Chapter 2: Univariate data analysis Chapter 2: Univariate data analysis Contents Graphical displays for categorical data (barchart, piechart) Graphical displays for numerical data data (histogram,
More informationAlgebra I. Mathematics Curriculum Framework. Revised 2004 Amended 2006
Algebra I Mathematics Curriculum Framework Revised 2004 Amended 2006 Course Title: Algebra I Course/Unit Credit: 1 Course Number: Teacher Licensure: Secondary Mathematics Grades: 9-12 Algebra I These are
More informationMean, Median, Mode, and Range
Mean, Median, Mode, and Range Mean, median, and mode are measures of central tendency; they measure the center of data. Range is a measure of dispersion; it measures the spread of data. The mean of a data
More informationBiostatistics for biomedical profession. BIMM34 Karin Källen & Linda Hartman November-December 2015
Biostatistics for biomedical profession BIMM34 Karin Källen & Linda Hartman November-December 2015 12015-11-02 Who needs a course in biostatistics? - Anyone who uses quntitative methods to interpret biological
More informationClassical Test Theory (CTT) for Assessing Reliability and Validity
Classical Test Theory (CTT) for Assessing Reliability and Validity Today s Class: Hand-waving at CTT-based assessments of validity CTT-based assessments of reliability Why alpha doesn t really matter CLP
More informationDescriptive Statistics
Descriptive Statistics CHAPTER OUTLINE 6-1 Numerical Summaries of Data 6- Stem-and-Leaf Diagrams 6-3 Frequency Distributions and Histograms 6-4 Box Plots 6-5 Time Sequence Plots 6-6 Probability Plots Chapter
More informationMarquette University MATH 1700 Class 5 Copyright 2017 by D.B. Rowe
Class 5 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 2017 by D.B. Rowe 1 Agenda: Recap Chapter 3.2-3.3 Lecture Chapter 4.1-4.2 Review Chapter 1 3.1 (Exam
More informationAlgebra 1 Duxbury Middle School > > Grade 8 > Mathematics > Algebra 1 > Iacadoro, Stephanie Tuesday, February 28, 2017, 11:58AM
Algebra 1 Duxbury Middle School > 2016-2017 > Grade 8 > Mathematics > Algebra 1 > Iacadoro, Stephanie Tuesday, February 28, 2017, 11:58AM Unit Unit 0: Preparing for Algebra * for 282 only (Week 1, 9 Skills
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 informationALGEBRAIC PRINCIPLES
ALGEBRAIC PRINCIPLES Numbers and Operations Standard: 1 Understands and applies concepts of numbers and operations Power 1: Understands numbers, ways of representing numbers, relationships among numbers,
More informationLecture 3. The Population Variance. The population variance, denoted σ 2, is the sum. of the squared deviations about the population
Lecture 5 1 Lecture 3 The Population Variance The population variance, denoted σ 2, is the sum of the squared deviations about the population mean divided by the number of observations in the population,
More information