### Chapter 1: Representing Data

Opening

Chapter 1 Opening

Section 1.1

1.1.1

Visualizing Information

1.1.2

Histograms and Stem-and-Leaf Plots

1.1.3

Types of Data and Variables

Section 1.2

1.2.1

Choosing Mean or Median

1.2.2

Variance and Standard Deviation

1.2.3

Sample Variance and Sample Standard Deviation

1.2.4

Variance and Standard Deviation

Section 1.3

1.3.2

Percentiles

1.3.2

Z-Scores

1.3.3

Linear Transformations of Data

### Chapter 2: Two-Variable Quantitative Data

Opening

Chapter 2 Opening

Section 2.1

2.1.1

Scatterplots and Association

2.1.2

Line of Best Fit

2.1.3

Residuals

2.1.4

The Least Squares Regression Line

2.1.5

Using Technology to Find the LSRL

Section 2.2

2.2.1

The Correlation Coefficient

2.2.2

Behavior of Correlation and the LSRL

2.2.3

Residual Plots

2.2.4

Association is Not Causation

2.2.5

Interpreting Correlation in Context

### Chapter 3: Multivariable Categorical Data

Opening

Chapter 3 Opening

Section 3.1

3.1.1

Probability and Two-Way Frequency Tables

3.1.2

Association and Conditional Relative Frequency Tables

3.1.3

Probability Notation

3.1.4

Relative Frequency Tables and Conditional Probabilities

3.1.5

Analyzing False Positives

Section 3.2

3.2.1

Probability Tree Diagrams

3.2.2

Problem Solving with Categorical Data

### Chapter 4:Studies and Experiments

Opening

Chapter 4 Opening

Section 4.1

4.1.1

Survey Design I

4.1.2

Samples and the Role of Randomness

4.1.3

Sampling When Random is Not Possible

4.1.4

Observational Studies and Experiments

4.1.5

Survey Design II (optional)

Section 4.2

4.2.1

Cause and Effect with Experiments

4.2.2

Experimental Design I

4.2.3

Experimental Design II

### Chapter 5: Density Functions and Normal Distributions

Opening

Chapter 5 Opening

Section 5.1

5.1.1

Relative Frequency Histograms and Random Variables

5.1.2

Introduction to Density Functions

5.1.3

The Normal Probability Density Function

Section 5.2

5.2.1

The Inverse Normal Function

5.2.2

The Standard Normal Distribution and z-Scores

5.2.3

### Chapter 6: Discrete Probability Distributions

Opening

Chapter 6 Opening

Section 6.1

6.1.1

Mean and Variance of a Discrete Random Variable

6.1.2

Linear Combinations of Independent Random Variables

6.1.3

Exploring the Variability of X – X

Section 6.2

6.2.1

Introducing the Binomial Setting

6.2.2

Binomial Probability Density Function

6.2.3

Exploring Binomial pdf and cdf

6.2.4

Shape, Center, and Spread of the Binomial Distribution

6.2.5

Normal Approximation to the Binomial Distribution

Section 6.3

6.3.1

Introduction to the Geometric Distribution

6.3.2

Binomial and Geometric Practice

### Chapter 7: Variability in Categorical Data Sampling

Opening

Chapter 7 Opening

Section 7.1

7.1.1

Introduction to Sampling Distributions

7.1.2

Simulating Sampling Distributions of Sample Proportions

7.1.3

Formulas for the Sampling Distributions of Sample Proportions

Section 7.2

7.2.1

Confidence Interval for a Population Proportion

7.2.2

Confidence Levels for Confidence Intervals

7.2.3

Changing the Margin of Error in Confidence Intervals

7.2.4

Evaluating Claims with Confidence Intervals

### Chapter 8: Drawing Conclusions From Categorical Data

Opening

Chapter 8 Opening

Section 8.1

8.1.1

Introduction to Hypothesis Testing

8.1.2

Hypothesis Tests for Proportions

8.1.3

Alternative Hypotheses and Two-Tailed Tests

Section 8.2

8.2.1

Types of Errors in Hypothesis Testing

8.2.2

Power of a Test

Section 8.3

8.3.1

The Difference Between Two Proportions

8.3.2

Two-Sample Proportion Hypothesis Tests

8.3.3

More Proportion Inference

### Chapter 9: Chi-Squared Inference Procedures

Opening

Chapter 9 Opening

Section 9.1

9.1.1

Introduction to the Chi-Squared Distribution

9.1.2

Chi-Squared Goodness of Fit

9.1.3

More Applications of Chi-Squared Goodness of Fit

Section 9.2

9.2.1

Chi-Squared Test for Independence

9.2.2

Chi-Squared Test for Homogeneity of Proportions

9.2.3

Practicing and Recognizing Chi-Squared Inference Procedures

### Chapter 10: Drawing Conclusions From Quantitative Data

Opening

Chapter 10 Opening

Section 10.1

10.1.1

Quantitative Sampling Distributions

10.1.2

More Sampling Distributions

Section 10.2

10.2.1

The Central Limit Theorem

10.2.2

Using the Normal Distribution with Means

Section 10.3

10.3.1

Introducing the t-Distribution

10.3.2

Calculating Confidence Intervals for μ

10.3.3

z-Tests and t-Tests for Population Means

### Chapter 11:Comparing Means and Identifying Tests

Opening

Chapter 11 Opening

Section 11.1

11.1.1

Paired and Independent Data from Surveys and Experiments

11.1.2

Paired Inference Procedures

11.1.3

Tests for the Difference of Two Means

11.1.4

Two-Sample Mean Inference with Experiments and Two-Sample Confidence Intervals

Section 11.2

11.2.1

Inference in Different Situations

11.2.2

Identifying and Implementing an Appropriate Test

### Chapter 12: Inference for Regression

Opening

Chapter 12 Opening

Section 12.1

12.1.1

Sampling Distribution of the Slope of the Regression Line

12.1.2

Inference for the Slope of the Regression Line

Section 12.2

12.2.1

Transforming Data to Achieve Linearity

12.2.2

Using Logarithms to Achieve Linearity

### Chapter 13: ANOVA and Beyond!

Opening

Chapter 13 Opening

Section 13.1

13.1.1

Modeling With the Chi-Squared Distribution

13.1.2

Introducing the F-Distribution

Section 13.2

13.2.1

One-Way ANOVA

Section 13.3

13.3.1

Sign Test: Introduction to Nonparametric Inference

13.3.2

Mood’s Median Test

### Chapter 1: Object Anatomy

Lesson 1.0

What Will I Learn?

Lesson 1.1

Using BlueJ and Submitting Programs

Lesson 1.2

Lesson 1.3

Identifiers and Reserved Words

Lesson 1.4

Identifiers and More Data Types

Lesson 1.5

Writing Methods

Lesson 1.6

The Constructor

Lesson 1.7

Java Mathematics

Lesson 1.8

Four 4s
Writing Class

Lesson 1.9.1

Time Conversions

Lesson 1.9.2

DollarsNcents

### Chapter 2:Using Objects

Lesson 2.1.1

Instantiating Objects

Lesson 2.1.2

Four 4s V2

Lesson 2.2

System.out

Lesson 2.3

Error Types
User Interface

Lesson 2.4.1

Scanner

Lesson 2.4.2

Box Object

Lesson 2.4.3

Converter

Lesson 2.5

Car Dealership

### Chapter 3: Classes from Libraries

Lesson 3.1.1

Strings Methods

Lesson 3.1.2

Strings Indexes

Lesson 3.2

Rounding Numbers

Lesson 3.3

Random Numbers

Lesson 3.4

Aliases and References

Lesson 3.5

### Chapter 4: Iteration and Decisions

Lesson 4.1.1

Lesson 4.1.2

Multiple && ||

Lesson 4.1.3

Truth Tables
The while Loop

Lesson 4.2.1

while Loop Math

Lesson 4.2.2

while Loop Strings
The for Loop

Lesson 4.3.1

Word Analysis

Lesson 4.3.2

Sentence Analysis

Lesson 4.4

Nested Loops

Lesson 4.5

Working with GUIs

### Chapter 5: Arrays

Lesson 5.1

Arrays of Primitives
Array of Objects

Lesson 5.2.1

Library of Books

Lesson 5.2.2

Deck of Cards

Lesson 5.3

StuffMart Parking Lot

### Chapter 6: Two-Dimensional Arrays

Lesson 6.1.1

Introduction to Two-Dimensional Arrays

Lesson 6.1.2

Matrix Objects
Two-Dimensional Arrays of Strings

Lesson 6.2.1

Seating Chart

Lesson 6.2.2

Flags R Fun

### Chapter 7: The ArrayList and Sorting

Lesson 7.1

ArrayLists of Objects

Lesson 7.2

ArrayLists of Wrapped Primitives

Lesson 7.3

Box of Chocolates

Lesson 7.4

Sorting Activity

Lesson 7.5

Sorting ArrayLists

Lesson 7.6

Sorting Arrays

### Chapter 8: Inheritance and Polymorphism

Lesson 8.1

ArrayLists of Objects

Lesson 8.2

ArrayLists of Wrapped Primitives

Lesson 8.3

Box of Chocolates

Lesson 8.4

Sorting Activity

Lesson 8.5

Interfaces

### Chapter 9: Recursion

Lesson 9.1

Recursive Methods

Lesson 9.2

Stack Overflow
Recursive Applications

Lesson 9.3.1

Merge Sort

Lesson 9.3.2

Binary Search

### Chapter 10: Additional Projects and Review

Lesson 10.1

Craps

Lesson 10.2

StuffMart Parking Lot V2

Lesson 10.3

Tic Tac Toe

Lesson 10.4

Recursive Rectangles

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# Foundations for Implementation

This professional learning is designed for teachers as they begin their implementation of CPM. This series contains multiple components and is grounded in multiple active experiences delivered over the first year. This learning experience will encourage teachers to adjust their instructional practices, expand their content knowledge, and challenge their beliefs about teaching and learning. Teachers and leaders will gain first-hand experience with CPM with emphasis on what they will be teaching. Throughout this series educators will experience the mathematics, consider instructional practices, and learn about the classroom environment necessary for a successful implementation of CPM curriculum resources.

Page 2 of the Professional Learning Progression (PDF) describes all of the components of this learning event and the additional support available. Teachers new to a course, but have previously attended Foundations for Implementation, can choose to engage in the course Content Modules in the Professional Learning Portal rather than attending the entire series of learning events again.

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# Building on Instructional Practice Series

The Building on Instructional Practice Series consists of three different events – Building on Discourse, Building on Assessment, Building on Equity – that are designed for teachers with a minimum of one year of experience teaching with CPM instructional materials and who have completed the Foundations for Implementation Series.

# Building on Equity

In Building on Equity, participants will learn how to include equitable practices in their classroom and support traditionally underserved students in becoming leaders of their own learning. Essential questions include: How do I shift dependent learners into independent learners? How does my own math identity and cultural background impact my classroom? The focus of day one is equitable classroom culture. Participants will reflect on how their math identity and mindsets impact student learning. They will begin working on a plan for Chapter 1 that creates an equitable classroom culture. The focus of day two and three is implementing equitable tasks. Participants will develop their use of the 5 Practices for Orchestrating Meaningful Mathematical Discussions and curate strategies for supporting all students in becoming leaders of their own learning. Participants will use an equity lens to reflect on and revise their Chapter 1 lesson plans.

# Building on Assessment

In Building on Assessment, participants will apply assessment research and develop methods to provide feedback to students and inform equitable assessment decisions. On day one, participants will align assessment practices with learning progressions and the principle of mastery over time as well as write assessment items. During day two, participants will develop rubrics, explore alternate types of assessment, and plan for implementation that supports student ownership. On the third day, participants will develop strategies to monitor progress and provide evidence of proficiency with identified mathematics content and practices. Participants will develop assessment action plans that will encourage continued collaboration within their learning community.

# Building on Discourse

In Building on Discourse, participants will improve their ability to facilitate meaningful mathematical discourse. This learning experience will encourage participants to adjust their instructional practices in the areas of sharing math authority, developing independent learners, and the creation of equitable classroom environments. Participants will plan for student learning by using teaching practices such as posing purposeful questioning, supporting productive struggle, and facilitating meaningful mathematical discourse. In doing so, participants learn to support students collaboratively engaged with rich tasks with all elements of the Effective Mathematics Teaching Practices incorporated through intentional and reflective planning.