Table of Contents DAY ONE ADDITIONAL MATERIALS MODULE 1

Size: px
Start display at page:

Download "Table of Contents DAY ONE ADDITIONAL MATERIALS MODULE 1"

Transcription

1 STATWAY INSTRUCTOR NOTES DAY ONE The objective of this lesson is to create a classroom community that will support and sustain students throughout the year-long course. It is also designed to introduce students to the norms and expectations around collaboration, discussion and support. DAY ONE Course Launch Productive Persistence Contract Activity ADDITIONAL MATERIALS As early as possible students should log on to the online platform MyStatway. The first time they do so, they will complete a background survey that will capture vital baseline information about student perception and knowledge. It s imperative that students complete this at the very beginning of the course. Once students have had the opportunity to complete assignments for a grade, you will want to complete the Syllabus Follow-Up Activity. MYSTATWAY Login Instructions 10 minutes PRODUCTIVE PERSISTENCE Syllabus Follow-Up Activity 20 minutes MODULE 1 The module begins with an introduction to the idea of statistical inference by having students conduct an in class experiment. In this vein students are guided through the research process, including developing a research question and deciding between an observational study and an experiment. The module concludes by exposing students to more detail about sampling methods and experimental design.

2 STATWAY INSTRUCTOR NOTES 2 has students conduct an in class experiment involving astrology. This sets the stage for a later formal development of inference by having students judge whether in class results are "unusual" relative to random variation. The topic then has students examine experiments and observational studies and the types of conclusions that can be drawn from each. LESSON The Statistical Analysis Process Lesson Supplement Astrology Investigation Lesson Applet Match, Excel file Lesson Productive Persistence Forming Groups Lesson Productive Persistence Working in Groups 1 hour 40 minutes LESSON EXTENSION Populations, Samples and Subjects and Mindset Activity Lesson Extension Supplement 1 Growth Mindset Article Lesson Extension Supplement 2 Mindset Questions LESSON Samples, Populations, and Types of Statistical Studies 1 hour 40 minutes introduces students to sampling methods. The required lesson shows students the importance of randomness in sampling as well as the dangers of methods such as convenience sampling and voluntary response sampling. Two optional lessons introduce systematic sampling and stratified sampling and show student sources of bias in observational studies.

3 STATWAY INSTRUCTOR NOTES 3 LESSON Random Sampling LESSON Other Sampling Strategies Optional LESSON Sources of Bias in Sampling Optional Lesson Supplement A Survey Questions Lesson Supplement B Survey Questions Topic 3 Topic 3 introduces students to experimental design. Students examine random assignment, direct control, control groups, and the placebo effect. An optional lesson shows how random assignment tends to create similar groups. LESSON Collecting Data by Conducting an Experiment Lesson Supplement The Gettysburg Address LESSON Populations, Samples and Subjects Lesson Supplement Dotplots 1 hour 40 minutes MODULE 2

4 STATWAY INSTRUCTOR NOTES 4 This module takes students into the third step of the research process, data analysis. The module starts with graphical summaries of data and then moves on to measures of center and spread. Module 2 also emphasizes the use of data analysis to compare distributions. focuses on how analyzing graphs can help with comparing distributions. Students compare dotplots and histograms and write summaries of the comparison between two distributions commenting on center, spread, and shape. LESSON Dotplots, Histograms, and Distributions for Quantitative Data LESSON Data Basketball Data, Excel file Lesson Supplement B Survey Questions LESSON Constructing Histograms for Quantitative Data LESSON Supplement Histograms focuses on the most common measures of center, mean and median. The effects of outliers and skewing on each are examined to help students understand when each measure is most useful. LESSON Quantifying the Center of a Distributiom Sample Mean and Sample Median Topic 3 Topic 3 introduces students to measures of dispersion based on the median. The students find the range, the quartiles, and the interquartile range. They draw boxplots as graphs of the fivenumber summaries and use the interquartile range to identify potential outliers.

5 STATWAY INSTRUCTOR NOTES 5 LESSON Quantifying Variability Relative to the Median 1 hour 15 minutes Topic 4 Topic 4 introduces another measure of spread, standard deviation. Students examine deviations from the mean in order to derive the standard deviation and learn to interpret its meaning. LESSON Quantifying Variability Relative to the Mean 1 hour 15 minutes MODULE 3 In this module, students transition from examining univariate numerical data to bivariate numerical data. Through exploration students learn to interpret and create scatterplots, as well as sketch lines (or curves) that best represent the data and use them to make predictions. Students use technology to create least squares regression lines and calculate correlation coefficients. They learn to interpret the parameters in the resulting model and understand the characteristics of the correlation coefficient and coefficient of determination. They use these along with the coefficient of determination, residual values, and residual plots to assess the fit of a line. In this topic students develop an understanding of the uses and value of scatterplots. They sketch lines of best fit and explore the role of the correlation coefficient in characterizing the strength and direction of linear relationships between explanatory and response variables. LESSON Introduction to Scatterplots and Bivariate Relationships LESSON Supplement Scatterplots 1 hour 30 minutes LESSON Developing an Intuitive Sense of Form, Direction and Strength of the Relationship Between Two Measurements

6 STATWAY INSTRUCTOR NOTES 6 LESSON Introduction to the Correlation Coefficient and Its Properties In this topic students develop an understanding of the minimization of squared error in the method of least squares. Students learn how to interpret the values of the parameters in the least squares line in context and when it is appropriate to use the regression line for prediction. LESSON Using Lines to Make Prediction LESSON Least Squares Regression Line as Line of Best Fit 1 hour 40 minutes LESSON Investigating the Meaning of Numbers in the Equation of a Line LESSON Special Properties of the Least Squares Regression Line Optional Topic 3 In this topic students examine residuals and how they can be used to assess the fit of a line. LESSON Using Residuals to Determine If a Line is a Good Fit 1 hour 15 minutes LESSON Using Residuals to Determine if a Line is an Appropriate Model Optional

7 STATWAY INSTRUCTOR NOTES 7 MODULE 4 Building upon Module 3 students will develop an understanding that other models, in addition to linear models, can be used to describe bivariate relationships. In particular, students will explore the exponential model. They will examine and learn to interpret the initial value parameter in context and whether a model represents a growth or decay scenario. Note: There is only one topic in this module. LESSON Investigating Patterns in Data LESSON Exponential Models MODULE 5 This module concentrates on categorical variables, and in particular relationship between pairs of categorical variables. Students use two-way tables and stacked bar graphs to examine such relationships. They calculate marginal, joint, and conditional proportions and probabilities. The module concludes by having students construct hypothetical two-way tables to calculate conditional probabilities. Note: There is only one topic in this module. LESSON An Introduction to Two-Way Tables LESSON Supplement Soda Data, Word file LESSON Data Soda Data, Excel file LESSON 5.1.2

8 STATWAY INSTRUCTOR NOTES 8 Marginal, Joint, and Conditional Probabilities from Two-Way Tables LESSON Building Two-Way Tables to Calculate Probability MODULE 6 This module develops the concepts of probability and probability distributions. Students explore The Law of Large Numbers and develop an understanding of basic probability rules by working with tables. The lessons also include both discrete and continuous probability distributions. In this topic students conduct an experiment that guides their understanding of The Law of Large Numbers. This is followed by an informal introduction to the basic probability rules using tables. Students also explore discrete random variables, discrete distributions and their properties. LESSON Probability LESSON Probability Rules LESSON Simulation Optional LESSON Probability Distributions of Discrete Random Variables In this topic continuous random variables are defined and explored. Students examine the normal distribution and the standard normal distribution.

9 STATWAY INSTRUCTOR NOTES 9 LESSON Probability Distributions of Continuous Random Variables LESSON Z-Scores and Normal Distributions Lesson Supplement Empirical Rule LESSON Using Normal Distributions to Find Probabilities and Critical Values MODULE 7 This module introduces sampling distributions and inferences for population proportions. Through simulations, distributions of sample proportions are discovered to have a familiar shape. With this discovery, students are introduced to the processes of statistical inference. Confidence intervals and hypothesis tests are informal, and determined through simulation. Through simulations, students investigate sampling distributions of sample proportions. After the ideas of shape, center and spread are explored, students use trial and error to determine margins of error that correspond to given levels of confidence. Students learn to create and properly interpret confidence intervals for a population proportion. LESSON Sampling Distributions Lesson Supplement Reese s Pieces Simulation, Excel File LESSON 7.1.2

10 STATWAY INSTRUCTOR NOTES 10 Reasoning with Sampling Distributions Lesson Supplement Presidential Race Simulation, Excel file Lesson Supplement Mayoral Race Simulation, Excel file LESSON Confidence Intervals Lesson Supplement Obama Approval Simulation, Excel file Lesson Supplement Many Confidence Interval Simulation, Excel file introduces the logic and notation of hypothesis testing and the process for testing claims about a population proportion. Students use sampling distribution simulations to determine P- values that correspond to a particular observation. Proper conclusions and interpretations are discussed, along with the types of errors that can be made when conducting hypothesis tests. LESSON Testing a Hypothesis Lesson Supplement Euro Tossing Simulation, Excel file LESSON Introduction to Hypothesis Testing MODULE 8 This module extends the ideas of Module 7 by demonstrating the approximate normality of the sampling distribution of sample proportions, thus leading to the Central Limit Theorem for sample proportions. Once

11 STATWAY INSTRUCTOR NOTES 11 criteria for approximate normality are established, students use the normal distribution to determine critical values for confidence intervals and P-values for hypothesis tests for a single population proportion. bridges the gap between simulated sampling distributions of sample proportions to the theoretical continuous and normal sampling distribution of sample proportions. Critical- and P- values from simulations and the normal distribution are compared, and criteria for approximate normality are presented. LESSON The Central Limit Theorem for Sample Proportions Lesson Supplement Population Proportion Simulation, Excel File LESSON Finding Areas Under Sampling Distributions introduces confidence intervals for a population proportion. Margins of error are computed using normal distribution critical values, and students are led to understand how sample size and confidence level influence the margin of error. Emphasis is placed upon proper interpretation of confidence intervals. LESSON Intervals for a Population Proportion and the Normal Distribution Lesson Supplement Proportion and Interval Simulation, Excel file LESSON Constructing Confidence Intervals for Population Proportions 20 minutes

12 STATWAY INSTRUCTOR NOTES 12 Topic 3 Topic 3 introduces hypothesis testing for a single population proportion. The normal distribution is used to determine P-values, which are used to make decisions regarding null and alternate hypotheses. Correct interpretation of results is emphasized. LESSON Hypothesis Tests for Population Proportions LESSON Additional Hypothesis Tests for Population Proportions MODULE 9 This module begins with an investigation of the sampling distribution of differences between sample proportions. Criteria for approximate normality are established, along with formulas for the mean and standard error. With these, students use the normal distribution to create confidence intervals and test hypotheses regarding differences between two population proportions. introduces the sampling distribution of differences between two sample proportions. Criteria for normality are introduced, and formulas for the mean and standard error of the sampling distribution are developed. LESSON Sampling Distribution of Differences of Two Proportions LESSON Using Technology to Explore the Sampling Distribution of the Differences in Two Proportions Lesson Supplement Sampling Distribution Simulation, Excel file guides students in the construction of confidence intervals for differences between two population proportions. Margins of error are computed using the normal distribution and

13 STATWAY INSTRUCTOR NOTES 13 standard error, and the relationships between sample size, level of confidence, and margin of error are explored. Correct interpretation of confidence intervals for a difference between population proportions is stressed. LESSON Confidence Intervals for the Difference in Two Population Proportions LESSON Computing and Interpreting Confidence Intervals for the Difference in Two Population Proportions 30 minutes Topic 3 Topic 3 introduces hypothesis testing for the difference between two population proportions. Students learn to test hypotheses using P-values and make conclusions regarding the null and alternate hypotheses. Correct interpretation of results is emphasized. LESSON A Statistical Test for the Difference in Two Population Proportions 30 minutes LESSON Statistical Tests for the Difference Between Two Population Proportions MODULE 10 This module presents sampling distributions of sample means and the Central Limit Theorem for Sample Means. Sampling distributions of sample means are explored and used to construct confidence intervals for and perform hypothesis tests for population means. Paired data are used to make inferences on the population mean of differences, and data from independent samples are used to make inferences on the difference between two population means. In students explore sampling distributions of sample means from populations from a variety of distributions. Students use a simulation to determine that regardless of the

14 STATWAY INSTRUCTOR NOTES 14 population distribution, sampling distributions approach normality as the sample size increases. The lesson culminates with a presentation of the Central Limit Theorem for Sample Means. Students also explore how the mean and standard error of a sampling distribution relate to the mean and standard deviation of a population and to the sample size. LESSON Sampling Distribution of Sample Means Lesson Supplement Acorn Mass Table LESSON Central Limit Theorem for Sample Means In students learn the rationale for using the T-distribution. They are introduced to critical values in T-distributions and construct confidence intervals based on sample data collected in class. LESSON The T-Distribution and T-Statistics LESSON Confidence Intervals for a Population Mean Topic 3 In Topic 3 students conduct formal hypothesis tests for population means. LESSON Hypothesis Tests for Population Means Lesson Supplement T-Table Topic 4 In Topic 4 students learn to how to differentiate between dependent and independent samples and construct confidence intervals and conduct hypothesis tests for the population mean of

15 STATWAY INSTRUCTOR NOTES 15 paired differences. They also learn to compute confidence intervals and conduct hypothesis tests for the difference between two population means. LESSON Inferences from Paired Samples LESSON Hypothesis Tests from Paired Samples LESSON Inference from Independent Samples MODULE 11 This module presents categorical data analysis using the chi-square statistic. Students learn the processes of the chi-square goodness of fit test, the chi-square test for independence of two categorical variables and the chi-square test for homogeneity. In each case, students discover the logic behind the development of these tests, learn to conduct the tests and interpret their results in context. introduces the chi-square goodness of fit tests. Students are introduced to the chisquare test statistic, and learn the conditions under which it varies approximately according to the chi-square distribution. Test statistics and P-values are used to make conclusions regarding claims in goodness of fit tests. LESSON Introduction to Chi-Squared Tests for One-Way Tables LESSON Executing the Chi-Square Test for One-Way Tables (Goodness of Fit) LESSON

16 STATWAY INSTRUCTOR NOTES 16 The Chi-Square Distribution and Degrees of Freedom extends the use of the chi-square test statistic in tests for independence of categorical variables and homogeneity. LESSON Introduction to Chi-Square for Two-Way Tables LESSON Executing the Chi-Square Test for Independence in Two-Way Tables LESSON The Chi-Square Test for Homogeneity in Two-Way Tables MODULE 12 This module presents a contrast between statistical models and deterministic, mathematical models. Students use algebra to develop an understanding of linear equations and find exact linear models given two points. Students learn to solve 1st degree equations and inequalities algebraically and graphically. This module includes optional lessons on solving quadratic inequalities and exponential functions. Students learn the difference between situations that require statistical methods for modeling versus the more exact (and in some ways, simpler) algebraic methods. Building upon the understanding of slope developed in Module 3, students learn to find the slope between two points and the equation of the line in form. LESSON Statistical Models and Exact Mathematical Models of Linear Relationships 25 minutes LESSON Mathematical Linear Models

17 STATWAY INSTRUCTOR NOTES 17 LESSON Proportional Models Methods of solving 1st degree equations and inequalities are examined in this topic. Students learn how to solve equations and inequalities algebraically. They also learn how to solve inequalities graphically. LESSON Linear Models Answering Various Types of Questions Algebraically LESSON Solving Inequalities Topic 3 In this optional topic exponential and power models are explored. Students develop an understanding of the parameters of these models and are able to write equations given a description of a scenario. LESSON Multiple Representations of Exponential Models Optional 1 hour 40 minutes LESSON Power Models Optional STATWAY and the Carnegie Foundation logo are trademarks of the Carnegie Foundation for the Advancement of Teaching. A Pathway Through College Statistics may be used as provided in the CC BY license, but neither the Statway trademark nor the Carnegie Foundation logo may be used without the prior written consent of the Carnegie Foundation.

18 STATWAY INSTRUCTOR NOTES 18

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

STA 225: Introductory Statistics (CT)

STA 225: Introductory Statistics (CT) Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic

More information

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best

More information

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4 Chapters 1-5 Cumulative Assessment AP Statistics Name: November 2008 Gillespie, Block 4 Part I: Multiple Choice This portion of the test will determine 60% of your overall test grade. Each question is

More information

Math 96: Intermediate Algebra in Context

Math 96: Intermediate Algebra in Context : Intermediate Algebra in Context Syllabus Spring Quarter 2016 Daily, 9:20 10:30am Instructor: Lauri Lindberg Office Hours@ tutoring: Tutoring Center (CAS-504) 8 9am & 1 2pm daily STEM (Math) Center (RAI-338)

More information

Research Design & Analysis Made Easy! Brainstorming Worksheet

Research Design & Analysis Made Easy! Brainstorming Worksheet Brainstorming Worksheet 1) Choose a Topic a) What are you passionate about? b) What are your library s strengths? c) What are your library s weaknesses? d) What is a hot topic in the field right now that

More information

Mathematics subject curriculum

Mathematics subject curriculum Mathematics subject curriculum Dette er ei omsetjing av den fastsette læreplanteksten. Læreplanen er fastsett på Nynorsk Established as a Regulation by the Ministry of Education and Research on 24 June

More information

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic

More information

Statewide Framework Document for:

Statewide Framework Document for: Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance

More information

MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES

MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES THE PRESIDENTS OF THE UNITED STATES Project: Focus on the Presidents of the United States Objective: See how many Presidents of the United States

More information

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Informal Comparative Inference: What is it? Hand Dominance and Throwing Accuracy

Informal Comparative Inference: What is it? Hand Dominance and Throwing Accuracy Informal Comparative Inference: What is it? Hand Dominance and Throwing Accuracy Logistics: This activity addresses mathematics content standards for seventh-grade, but can be adapted for use in sixth-grade

More information

Algebra 2- Semester 2 Review

Algebra 2- Semester 2 Review Name Block Date Algebra 2- Semester 2 Review Non-Calculator 5.4 1. Consider the function f x 1 x 2. a) Describe the transformation of the graph of y 1 x. b) Identify the asymptotes. c) What is the domain

More information

Honors Mathematics. Introduction and Definition of Honors Mathematics

Honors Mathematics. Introduction and Definition of Honors Mathematics Honors Mathematics Introduction and Definition of Honors Mathematics Honors Mathematics courses are intended to be more challenging than standard courses and provide multiple opportunities for students

More information

Physics 270: Experimental Physics

Physics 270: Experimental Physics 2017 edition Lab Manual Physics 270 3 Physics 270: Experimental Physics Lecture: Lab: Instructor: Office: Email: Tuesdays, 2 3:50 PM Thursdays, 2 4:50 PM Dr. Uttam Manna 313C Moulton Hall umanna@ilstu.edu

More information

Grade 6: Correlated to AGS Basic Math Skills

Grade 6: Correlated to AGS Basic Math Skills Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and

More information

Office Hours: Mon & Fri 10:00-12:00. Course Description

Office Hours: Mon & Fri 10:00-12:00. Course Description 1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 4 credits (3 credits lecture, 1 credit lab) Fall 2016 M/W/F 1:00-1:50 O Brian 112 Lecture Dr. Michelle Benson mbenson2@buffalo.edu

More information

Mathematics. Mathematics

Mathematics. Mathematics Mathematics Program Description Successful completion of this major will assure competence in mathematics through differential and integral calculus, providing an adequate background for employment in

More information

School of Innovative Technologies and Engineering

School of Innovative Technologies and Engineering School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius

More information

Radius STEM Readiness TM

Radius STEM Readiness TM Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and

More information

The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.

The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design. Name: Partner(s): Lab #1 The Scientific Method Due 6/25 Objective The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.

More information

State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210

State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210 1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210 Dr. Michelle Benson mbenson2@buffalo.edu Office: 513 Park Hall Office Hours: Mon & Fri 10:30-12:30

More information

AP Statistics Summer Assignment 17-18

AP Statistics Summer Assignment 17-18 AP Statistics Summer Assignment 17-18 Welcome to AP Statistics. This course will be unlike any other math class you have ever taken before! Before taking this course you will need to be competent in basic

More information

Shockwheat. Statistics 1, Activity 1

Shockwheat. Statistics 1, Activity 1 Statistics 1, Activity 1 Shockwheat Students require real experiences with situations involving data and with situations involving chance. They will best learn about these concepts on an intuitive or informal

More information

Sociology 521: Social Statistics and Quantitative Methods I Spring Wed. 2 5, Kap 305 Computer Lab. Course Website

Sociology 521: Social Statistics and Quantitative Methods I Spring Wed. 2 5, Kap 305 Computer Lab. Course Website Sociology 521: Social Statistics and Quantitative Methods I Spring 2012 Wed. 2 5, Kap 305 Computer Lab Instructor: Tim Biblarz Office hours (Kap 352): W, 5 6pm, F, 10 11, and by appointment (213) 740 3547;

More information

EGRHS Course Fair. Science & Math AP & IB Courses

EGRHS Course Fair. Science & Math AP & IB Courses EGRHS Course Fair Science & Math AP & IB Courses Science Courses: AP Physics IB Physics SL IB Physics HL AP Biology IB Biology HL AP Physics Course Description Course Description AP Physics C (Mechanics)

More information

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only.

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only. Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a

More information

Learning Disability Functional Capacity Evaluation. Dear Doctor,

Learning Disability Functional Capacity Evaluation. Dear Doctor, Dear Doctor, I have been asked to formulate a vocational opinion regarding NAME s employability in light of his/her learning disability. To assist me with this evaluation I would appreciate if you can

More information

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional

More information

Julia Smith. Effective Classroom Approaches to.

Julia Smith. Effective Classroom Approaches to. Julia Smith @tessmaths Effective Classroom Approaches to GCSE Maths resits julia.smith@writtle.ac.uk Agenda The context of GCSE resit in a post-16 setting An overview of the new GCSE Key features of a

More information

Instructor: Matthew Wickes Kilgore Office: ES 310

Instructor: Matthew Wickes Kilgore Office: ES 310 MATH 1314 College Algebra Syllabus Instructor: Matthew Wickes Kilgore Office: ES 310 Longview Office: LN 205C Email: mwickes@kilgore.edu Phone: 903 988-7455 Prerequistes: Placement test score on TSI or

More information

Introduction to the Practice of Statistics

Introduction to the Practice of Statistics Chapter 1: Looking at Data Distributions Introduction to the Practice of Statistics Sixth Edition David S. Moore George P. McCabe Bruce A. Craig Statistics is the science of collecting, organizing and

More information

Analysis of Enzyme Kinetic Data

Analysis of Enzyme Kinetic Data Analysis of Enzyme Kinetic Data To Marilú Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY

More information

Cal s Dinner Card Deals

Cal s Dinner Card Deals Cal s Dinner Card Deals Overview: In this lesson students compare three linear functions in the context of Dinner Card Deals. Students are required to interpret a graph for each Dinner Card Deal to help

More information

Mathematics Assessment Plan

Mathematics Assessment Plan Mathematics Assessment Plan Mission Statement for Academic Unit: Georgia Perimeter College transforms the lives of our students to thrive in a global society. As a diverse, multi campus two year college,

More information

learning collegiate assessment]

learning collegiate assessment] [ collegiate learning assessment] INSTITUTIONAL REPORT 2005 2006 Kalamazoo College council for aid to education 215 lexington avenue floor 21 new york new york 10016-6023 p 212.217.0700 f 212.661.9766

More information

12- A whirlwind tour of statistics

12- A whirlwind tour of statistics CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh

More information

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur)

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) 1 Interviews, diary studies Start stats Thursday: Ethics/IRB Tuesday: More stats New homework is available

More information

Statistical Studies: Analyzing Data III.B Student Activity Sheet 7: Using Technology

Statistical Studies: Analyzing Data III.B Student Activity Sheet 7: Using Technology Suppose data were collected on 25 bags of Spud Potato Chips. The weight (to the nearest gram) of the chips in each bag is listed below. 25 28 23 26 23 25 25 24 24 27 23 24 28 27 24 26 24 25 27 26 25 26

More information

1.11 I Know What Do You Know?

1.11 I Know What Do You Know? 50 SECONDARY MATH 1 // MODULE 1 1.11 I Know What Do You Know? A Practice Understanding Task CC BY Jim Larrison https://flic.kr/p/9mp2c9 In each of the problems below I share some of the information that

More information

Sociology 521: Social Statistics and Quantitative Methods I Spring 2013 Mondays 2 5pm Kap 305 Computer Lab. Course Website

Sociology 521: Social Statistics and Quantitative Methods I Spring 2013 Mondays 2 5pm Kap 305 Computer Lab. Course Website Sociology 521: Social Statistics and Quantitative Methods I Spring 2013 Mondays 2 5pm Kap 305 Computer Lab Instructor: Tim Biblarz Office: Hazel Stanley Hall (HSH) Room 210 Office hours: Mon, 5 6pm, F,

More information

Math 098 Intermediate Algebra Spring 2018

Math 098 Intermediate Algebra Spring 2018 Math 098 Intermediate Algebra Spring 2018 Dept. of Mathematics Instructor's Name: Office Location: Office Hours: Office Phone: E-mail: MyMathLab Course ID: Course Description This course expands on the

More information

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering

Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Spring 2014 SYLLABUS Michigan State University STT 430: Probability and Statistics for Engineering Time and Place: MW 3:00-4:20pm, A126 Wells Hall Instructor: Dr. Marianne Huebner Office: A-432 Wells Hall

More information

CAAP. Content Analysis Report. Sample College. Institution Code: 9011 Institution Type: 4-Year Subgroup: none Test Date: Spring 2011

CAAP. Content Analysis Report. Sample College. Institution Code: 9011 Institution Type: 4-Year Subgroup: none Test Date: Spring 2011 CAAP Content Analysis Report Institution Code: 911 Institution Type: 4-Year Normative Group: 4-year Colleges Introduction This report provides information intended to help postsecondary institutions better

More information

Level 1 Mathematics and Statistics, 2015

Level 1 Mathematics and Statistics, 2015 91037 910370 1SUPERVISOR S Level 1 Mathematics and Statistics, 2015 91037 Demonstrate understanding of chance and data 9.30 a.m. Monday 9 November 2015 Credits: Four Achievement Achievement with Merit

More information

Using Calculators for Students in Grades 9-12: Geometry. Re-published with permission from American Institutes for Research

Using Calculators for Students in Grades 9-12: Geometry. Re-published with permission from American Institutes for Research Using Calculators for Students in Grades 9-12: Geometry Re-published with permission from American Institutes for Research Using Calculators for Students in Grades 9-12: Geometry By: Center for Implementing

More information

VOL. 3, NO. 5, May 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

VOL. 3, NO. 5, May 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved. Exploratory Study on Factors that Impact / Influence Success and failure of Students in the Foundation Computer Studies Course at the National University of Samoa 1 2 Elisapeta Mauai, Edna Temese 1 Computing

More information

OFFICE SUPPORT SPECIALIST Technical Diploma

OFFICE SUPPORT SPECIALIST Technical Diploma OFFICE SUPPORT SPECIALIST Technical Diploma Program Code: 31-106-8 our graduates INDEMAND 2017/2018 mstc.edu administrative professional career pathway OFFICE SUPPORT SPECIALIST CUSTOMER RELATIONSHIP PROFESSIONAL

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Stopping rules for sequential trials in high-dimensional data

Stopping rules for sequential trials in high-dimensional data Stopping rules for sequential trials in high-dimensional data Sonja Zehetmayer, Alexandra Graf, and Martin Posch Center for Medical Statistics, Informatics and Intelligent Systems Medical University of

More information

Mathacle PSet Stats, Concepts in Statistics and Probability Level Number Name: Date:

Mathacle PSet Stats, Concepts in Statistics and Probability Level Number Name: Date: 1 st Quarterly Exam ~ Sampling, Designs, Exploring Data and Regression Part 1 Review I. SAMPLING MC I-1.) [APSTATSMC2014-6M] Approximately 52 percent of all recent births were boys. In a simple random

More information

How the Guppy Got its Spots:

How the Guppy Got its Spots: This fall I reviewed the Evobeaker labs from Simbiotic Software and considered their potential use for future Evolution 4974 courses. Simbiotic had seven labs available for review. I chose to review the

More information

Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010)

Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010) Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010) Jaxk Reeves, SCC Director Kim Love-Myers, SCC Associate Director Presented at UGA

More information

Ryerson University Sociology SOC 483: Advanced Research and Statistics

Ryerson University Sociology SOC 483: Advanced Research and Statistics Ryerson University Sociology SOC 483: Advanced Research and Statistics Prerequisites: SOC 481 Instructor: Paul S. Moore E-mail: psmoore@ryerson.ca Office: Sociology Department Jorgenson JOR 306 Phone:

More information

Dublin City Schools Mathematics Graded Course of Study GRADE 4

Dublin City Schools Mathematics Graded Course of Study GRADE 4 I. Content Standard: Number, Number Sense and Operations Standard Students demonstrate number sense, including an understanding of number systems and reasonable estimates using paper and pencil, technology-supported

More information

Centre for Evaluation & Monitoring SOSCA. Feedback Information

Centre for Evaluation & Monitoring SOSCA. Feedback Information Centre for Evaluation & Monitoring SOSCA Feedback Information Contents Contents About SOSCA... 3 SOSCA Feedback... 3 1. Assessment Feedback... 4 2. Predictions and Chances Graph Software... 7 3. Value

More information

Measures of the Location of the Data

Measures of the Location of the Data OpenStax-CNX module m46930 1 Measures of the Location of the Data OpenStax College This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 The common measures

More information

Certified Six Sigma Professionals International Certification Courses in Six Sigma Green Belt

Certified Six Sigma Professionals International Certification Courses in Six Sigma Green Belt Certification Singapore Institute Certified Six Sigma Professionals Certification Courses in Six Sigma Green Belt ly Licensed Course for Process Improvement/ Assurance Managers and Engineers Leading the

More information

Statistics and Probability Standards in the CCSS- M Grades 6- HS

Statistics and Probability Standards in the CCSS- M Grades 6- HS Statistics and Probability Standards in the CCSS- M Grades 6- HS Grade 6 Develop understanding of statistical variability. -6.SP.A.1 Recognize a statistical question as one that anticipates variability

More information

SAT MATH PREP:

SAT MATH PREP: SAT MATH PREP: 2015-2016 NOTE: The College Board has redesigned the SAT Test. This new test will start in March of 2016. Also, the PSAT test given in October of 2015 will have the new format. Therefore

More information

Developing an Assessment Plan to Learn About Student Learning

Developing an Assessment Plan to Learn About Student Learning Developing an Assessment Plan to Learn About Student Learning By Peggy L. Maki, Senior Scholar, Assessing for Learning American Association for Higher Education (pre-publication version of article that

More information

GUIDE TO THE CUNY ASSESSMENT TESTS

GUIDE TO THE CUNY ASSESSMENT TESTS GUIDE TO THE CUNY ASSESSMENT TESTS IN MATHEMATICS Rev. 117.016110 Contents Welcome... 1 Contact Information...1 Programs Administered by the Office of Testing and Evaluation... 1 CUNY Skills Assessment:...1

More information

UNIT ONE Tools of Algebra

UNIT ONE Tools of Algebra UNIT ONE Tools of Algebra Subject: Algebra 1 Grade: 9 th 10 th Standards and Benchmarks: 1 a, b,e; 3 a, b; 4 a, b; Overview My Lessons are following the first unit from Prentice Hall Algebra 1 1. Students

More information

Foothill College Summer 2016

Foothill College Summer 2016 Foothill College Summer 2016 Intermediate Algebra Math 105.04W CRN# 10135 5.0 units Instructor: Yvette Butterworth Text: None; Beoga.net material used Hours: Online Except Final Thurs, 8/4 3:30pm Phone:

More information

Course Name: Elementary Calculus Course Number: Math 2103 Semester: Fall Phone:

Course Name: Elementary Calculus Course Number: Math 2103 Semester: Fall Phone: Course Name: Elementary Calculus Course Number: Math 2103 Semester: Fall 2011 Instructor s Name: Ricky Streight Hours Credit: 3 Phone: 405-945-6794 email: ricky.streight@okstate.edu 1. COURSE: Math 2103

More information

Statistical Analysis of Climate Change, Renewable Energies, and Sustainability An Independent Investigation for Introduction to Statistics

Statistical Analysis of Climate Change, Renewable Energies, and Sustainability An Independent Investigation for Introduction to Statistics 5/22/2012 Statistical Analysis of Climate Change, Renewable Energies, and Sustainability An Independent Investigation for Introduction to Statistics College of Menominee Nation & University of Wisconsin

More information

A Program Evaluation of Connecticut Project Learning Tree Educator Workshops

A Program Evaluation of Connecticut Project Learning Tree Educator Workshops A Program Evaluation of Connecticut Project Learning Tree Educator Workshops Jennifer Sayers Dr. Lori S. Bennear, Advisor May 2012 Masters project submitted in partial fulfillment of the requirements for

More information

GDP Falls as MBA Rises?

GDP Falls as MBA Rises? Applied Mathematics, 2013, 4, 1455-1459 http://dx.doi.org/10.4236/am.2013.410196 Published Online October 2013 (http://www.scirp.org/journal/am) GDP Falls as MBA Rises? T. N. Cummins EconomicGPS, Aurora,

More information

Enhancing Students Understanding Statistics with TinkerPlots: Problem-Based Learning Approach

Enhancing Students Understanding Statistics with TinkerPlots: Problem-Based Learning Approach Enhancing Students Understanding Statistics with TinkerPlots: Problem-Based Learning Approach Krongthong Khairiree drkrongthong@gmail.com International College, Suan Sunandha Rajabhat University, Bangkok,

More information

Lesson M4. page 1 of 2

Lesson M4. page 1 of 2 Lesson M4 page 1 of 2 Miniature Gulf Coast Project Math TEKS Objectives 111.22 6b.1 (A) apply mathematics to problems arising in everyday life, society, and the workplace; 6b.1 (C) select tools, including

More information

The Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools

The Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools The Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools Megan Toby Boya Ma Andrew Jaciw Jessica Cabalo Empirical

More information

Green Belt Curriculum (This workshop can also be conducted on-site, subject to price change and number of participants)

Green Belt Curriculum (This workshop can also be conducted on-site, subject to price change and number of participants) Green Belt Curriculum (This workshop can also be conducted on-site, subject to price change and number of participants) Notes: 1. We use Mini-Tab in this workshop. Mini-tab is available for free trail

More information

Math 181, Calculus I

Math 181, Calculus I Math 181, Calculus I [Semester] [Class meeting days/times] [Location] INSTRUCTOR INFORMATION: Name: Office location: Office hours: Mailbox: Phone: Email: Required Material and Access: Textbook: Stewart,

More information

S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y

S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y Department of Mathematics, Statistics and Science College of Arts and Sciences Qatar University S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y A m e e n A l a

More information

THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY

THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY William Barnett, University of Louisiana Monroe, barnett@ulm.edu Adrien Presley, Truman State University, apresley@truman.edu ABSTRACT

More information

Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice

Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice Title: Considering Coordinate Geometry Common Core State Standards

More information

The Good Judgment Project: A large scale test of different methods of combining expert predictions

The Good Judgment Project: A large scale test of different methods of combining expert predictions The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain

More information

School Size and the Quality of Teaching and Learning

School Size and the Quality of Teaching and Learning School Size and the Quality of Teaching and Learning An Analysis of Relationships between School Size and Assessments of Factors Related to the Quality of Teaching and Learning in Primary Schools Undertaken

More information

Why Did My Detector Do That?!

Why Did My Detector Do That?! Why Did My Detector Do That?! Predicting Keystroke-Dynamics Error Rates Kevin Killourhy and Roy Maxion Dependable Systems Laboratory Computer Science Department Carnegie Mellon University 5000 Forbes Ave,

More information

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017 Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics

More information

Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice

Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice Megan Andrew Cheng Wang Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice Background Many states and municipalities now allow parents to choose their children

More information

Syllabus ENGR 190 Introductory Calculus (QR)

Syllabus ENGR 190 Introductory Calculus (QR) Syllabus ENGR 190 Introductory Calculus (QR) Catalog Data: ENGR 190 Introductory Calculus (4 credit hours). Note: This course may not be used for credit toward the J.B. Speed School of Engineering B. S.

More information

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Instructor: Mario D. Garrett, Ph.D.   Phone: Office: Hepner Hall (HH) 100 San Diego State University School of Social Work 610 COMPUTER APPLICATIONS FOR SOCIAL WORK PRACTICE Statistical Package for the Social Sciences Office: Hepner Hall (HH) 100 Instructor: Mario D. Garrett,

More information

Technical Manual Supplement

Technical Manual Supplement VERSION 1.0 Technical Manual Supplement The ACT Contents Preface....................................................................... iii Introduction....................................................................

More information

The relationship between national development and the effect of school and student characteristics on educational achievement.

The relationship between national development and the effect of school and student characteristics on educational achievement. The relationship between national development and the effect of school and student characteristics on educational achievement. A crosscountry exploration. Abstract Since the publication of two controversial

More information

TabletClass Math Geometry Course Guidebook

TabletClass Math Geometry Course Guidebook TabletClass Math Geometry Course Guidebook Includes Final Exam/Key, Course Grade Calculation Worksheet and Course Certificate Student Name Parent Name School Name Date Started Course Date Completed Course

More information

This Performance Standards include four major components. They are

This Performance Standards include four major components. They are Environmental Physics Standards The Georgia Performance Standards are designed to provide students with the knowledge and skills for proficiency in science. The Project 2061 s Benchmarks for Science Literacy

More information

Math 150 Syllabus Course title and number MATH 150 Term Fall 2017 Class time and location INSTRUCTOR INFORMATION Name Erin K. Fry Phone number Department of Mathematics: 845-3261 e-mail address erinfry@tamu.edu

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

San José State University Department of Marketing and Decision Sciences BUS 90-06/ Business Statistics Spring 2017 January 26 to May 16, 2017

San José State University Department of Marketing and Decision Sciences BUS 90-06/ Business Statistics Spring 2017 January 26 to May 16, 2017 San José State University Department of Marketing and Decision Sciences BUS 90-06/30174- Business Statistics Spring 2017 January 26 to May 16, 2017 Course and Contact Information Instructor: Office Location:

More information

Characteristics of Functions

Characteristics of Functions Characteristics of Functions Unit: 01 Lesson: 01 Suggested Duration: 10 days Lesson Synopsis Students will collect and organize data using various representations. They will identify the characteristics

More information

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Sanket S. Kalamkar and Adrish Banerjee Department of Electrical Engineering

More information

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District Report Submitted June 20, 2012, to Willis D. Hawley, Ph.D., Special

More information

Practical Research. Planning and Design. Paul D. Leedy. Jeanne Ellis Ormrod. Upper Saddle River, New Jersey Columbus, Ohio

Practical Research. Planning and Design. Paul D. Leedy. Jeanne Ellis Ormrod. Upper Saddle River, New Jersey Columbus, Ohio SUB Gfittingen 213 789 981 2001 B 865 Practical Research Planning and Design Paul D. Leedy The American University, Emeritus Jeanne Ellis Ormrod University of New Hampshire Upper Saddle River, New Jersey

More information

Universityy. The content of

Universityy. The content of WORKING PAPER #31 An Evaluation of Empirical Bayes Estimation of Value Added Teacher Performance Measuress Cassandra M. Guarino, Indianaa Universityy Michelle Maxfield, Michigan State Universityy Mark

More information

SURVIVING ON MARS WITH GEOGEBRA

SURVIVING ON MARS WITH GEOGEBRA SURVIVING ON MARS WITH GEOGEBRA Lindsey States and Jenna Odom Miami University, OH Abstract: In this paper, the authors describe an interdisciplinary lesson focused on determining how long an astronaut

More information

Student s Edition. Grade 6 Unit 6. Statistics. Eureka Math. Eureka Math

Student s Edition. Grade 6 Unit 6. Statistics. Eureka Math. Eureka Math Student s Edition Grade 6 Unit 6 Statistics Eureka Math Eureka Math Lesson 1 Lesson 1: Posing Statistical Questions Statistics is about using data to answer questions. In this module, the following four

More information