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
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
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
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
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
Additional Practice Problems
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
6.2.5
Normal Approximation to the Binomial Distribution
Section 6.3
6.3.1
Introduction to the Geometric Distribution
6.3.2
Binomial and Geometric Practice
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
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
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
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
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
Section 11.2
11.2.1
Inference in Different Situations
11.2.2
Identifying and Implementing an Appropriate Test
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
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
Lesson 1.0
What Will I Learn?
Lesson 1.1
Using BlueJ and Submitting Programs
Lesson 1.2
Objects, Comments, and Identifiers
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
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
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
Binary, Hexadecimal Conversions
Lesson 4.1.1
Cascading if else
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
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
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
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
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
Lesson 9.1
Recursive Methods
Lesson 9.2
Stack Overflow
Recursive Applications
Lesson 9.3.1
Merge Sort
Lesson 9.3.2
Binary Search
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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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.
