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Welcome to Common Core High School Mathematics Leadership

Welcome to Common Core High School Mathematics Leadership. Summer Institute 2014. Session 7 • 24 June 2014 Residuals: how good is a model?. Today’s Agenda. Homework review and discussion Grade 9, Lessons 12 & 13: Relationships Between Two Numerical Variables

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Welcome to Common Core High School Mathematics Leadership

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  1. Welcome to Common Core High School Mathematics Leadership Summer Institute 2014 Session 7 • 24 June 2014 Residuals: how good is a model?

  2. Today’s Agenda • Homework review and discussion • Grade 9, Lessons 12 & 13: Relationships Between Two Numerical Variables • Grade 9, Lesson 15: Interpreting Residuals from a Line • Reflecting on CCSSM standards aligned to lessons 12, 13 & 15. • Break • Group presentations: • Homework and closing remarks

  3. Activity 1 homework review and discussion Table discussion: Day 6 homework tasks • Consider the survey questions and data set provided as a result of an extensive survey project at Rufus King High School in 1990. Use the template and the data to to investigate if two variables are likely to be associated or not for this sample. Be prepared to use your summary to develop a poster for indicating how a your statistical study involving categorical variables. • Reflection on teaching:Association is defined by a difference in conditional relative frequencies, yet how large the difference needed to hypothesize a connection is still not clear to students. This becomes more important as we move to the next level of students’ work in statistics (inferential statistics). At this point, how might you help students put into perspective when an association suggests that two categorical categories are connected.

  4. Learning Intentions and Success Criteria We are learning to… • Recognize and describe linear and nonlinear relationships in bivariate numerical data • Model these relationships with functions • Estimate how accurately a given model fits the statistical data

  5. Learning Intentions and Success Criteria We will be successful when we can: • Recognize linear, quadratic, and exponential patterns in bivariate numerical data, and describe those patterns using correct mathematical and statistical language • Find the least-squares regression line for a data set that appears to show a linear association • Use residuals to estimate the strength of an hypothesized linear association

  6. Activity 2 Lessons 12 & 13: relationships between two numerical variables Modeling associations in numerical data EngageNY/Common Core Grade 9, Lessons 12 & 13

  7. Activity 2 Lessons 12 & 13: relationships between two numerical variables Prior knowledge Already in Grade 8, students encountered scatter plots, and modeled data that showed a linear association by informally fitting a line to the data. In Grade 9, Module 1, students have encountered linear, quadratic, and exponential functions and their graphs.

  8. Activity 2 Lessons 12 & 13: relationships between two numerical variables (From Grade 8) Three questions to ask when you look at a scatter plot: • Does it look like there is a relationship between the two variables used to make the scatter plot? • If there is a relationship, does it appear to be linear? • If the relationship appears to be linear, is the relationship a positive linear relationship or a negative linear relationship?

  9. Activity 2 Lessons 12 & 13: relationships between two numerical variables • In pairs, discuss Lesson 12, Exercises 1, 2 & 3. • Discuss Lesson 13, Example 1 and Exercises 1-6. • Discuss Lesson 13, Example 2 and Exercises 7-9.

  10. Activity 3 Lesson 15: interpreting residuals from a line Estimating the degree of accuracy of a linear model EngageNY/Common Core Grade 9, Lesson 15

  11. Activity 3 Lesson 15: interpreting residuals from a line Individually study the table and scatter plot in Example 1 • Informally (as in Grade 8), find a trend line on the scatter plot • Find (or estimate) an equation for your trend line How would you compare your trend line to those of other at your table. Specifically, how could you decide whether your trend line was a better or worse fit to the data than the others?

  12. Activity 3 Lesson 15: interpreting residuals from a line • At your tables, complete Exercises 1-4, using graphing calculators (or other technology if any is available) to find the least-squares regression line • What advantages might there be in using the least-squares line? • What disadvantages might there be in using this line?

  13. Activity 3 Lesson 15: interpreting residuals from a line • At your tables, complete Exercises 5 & 6

  14. Activity 3 Lesson 15: interpreting residuals from a line • At your tables, study Example 2, and complete Exercises 7 & 8

  15. Activity 4reflecting on standards aligned to lessons 12, 13 & 15 Review the following CCSSM School content standards: S-ID.6 S-ID.7 • Where did you see these standards in the lessons you have just completed? • What would you look for in students’ work to suggest that they have made progress towards these standards?

  16. Activity 4reflecting on standards aligned to lessons 12, 13 & 15 S-ID.6: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. a. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models. b. Informally assess the fit of a function by plotting and analyzing residuals. c. Fit a linear function for a scatter plot that suggests a linear association. S-ID.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.

  17. Activity 4reflecting on standards aligned to lessons 12, 13 & 15 Read MP5, the fifth CCSSM standard for mathematical practice. • Recalling that the standards for mathematical practice describe student behaviors, how did you engage in this practice as you completed the lesson? • What instructional moves or decisions did you see occurring during the lesson that encouraged greater engagement in MP5? • Are there other standards for mathematical practice that were prominent as you and your groups worked on the tasks?

  18. Activity 4reflecting on standards aligned to lessons 12, 13 & 15 CCSSM MP.5 Engageny MP.5 MP.5 Use appropriate tools strategically Students visualize data distributions and relationships between numerical variables using graphing software. They select and analyze models that are fit using appropriate technology to determine whether or not the model is appropriate. Students use visual representations of data distributions from technology to answer statistical questions. MP.5 Use appropriate tools strategically Mathematically proficient students consider the available tools when solving a mathematical problem. These tools might include pencil and paper, concrete models, a ruler, a protractor, a calculator, a spreadsheet, a computer algebra system, a statistical package, or dynamic geometry software. Proficient students are sufficiently familiar with tools appropriate for their grade or course to make sound decisions about when each of these tools might be helpful, recognizing both the insight to be gained and their limitations. For example, mathematically proficient high school students analyze graphs of functions and solutions generated using a graphing calculator. They detect possible errors by strategically using estimation and other mathematical knowledge. When making mathematical models, they know that technology can enable them to visualize the results of varying assumptions, explore consequences, and compare predictions with data. Mathematically proficient students at various grade levels are able to identify relevant external mathematical resources, such as digital content located on a website, and use them to pose or solve problems. They are able to use technological tools to explore and deepen their understanding of concepts.

  19. Activity 4reflecting on standards aligned to lessons 12, 13 & 15 • How can you distinguish between linear and nonlinear associations in data by looking at a scatter plot? • What are the characteristics of linear, quadratic, and exponential relationships and/or associations in data? • How can you tell whether a linear model is a good fit to data? Closing questions for lessons 12, 13 & 15

  20. Learning Intentions and Success Criteria We are learning to… • Recognize and describe linear and nonlinear relationships in bivariate numerical data • Model these relationships with functions • Estimate how accurately a given model fits the statistical data

  21. Learning Intentions and Success Criteria We will be successful when we can: • Recognize linear, quadratic, and exponential patterns in bivariate numerical data, and describe those patterns using correct mathematical and statistical language • Find the least-squares regression line for a data set that appears to show a linear association • Use residuals to estimate the strength of an hypothesized linear association

  22. Break

  23. Activity 5 group presentations Today’s group presentations: Grade 6, Lesson 3 Grade 6, Lesson 4

  24. Activity 5 group presentations Norms for analyzing teaching • Make notes to remind yourself of important moments in the lesson that you would like to comment on • Base your observations on observed and experienced facts rather than opinions • Use non-evaluative languageI noticed…I wondered…

  25. Activity 5 group presentations Giving feedback • Warm feedbackSomething you would like to see again in the teacher’s next lesson • Cool feedbackSomething you would like the instructor to change and that they could change in the next lesson • Challenging feedbackSomething you would like the instructor to think about that might change over a longer period of time

  26. Activity 6 Homework and Closing Remarks • Complete the problem set for Lesson 15. • Extending the mathematics:In Lesson 15, we saw how residuals can be used to estimate the degree to which a linear model fits a given data set. How could residuals be used to estimate the fit of a linear model? How might you use residuals to help you determine which type of model (linear, exponential, quadratic, …) is most appropriate for a given data set? • Reflecting on teaching:Does your current curriculum include as much statistics content in Grade 9 as the Engageny materials? If not, how might you arrange to align your curriculum more closely to the CCSSM standards?

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