ALEX Classroom Resource

  

Absolute Extrema and Optimization Practice

  Classroom Resource Information  

Title:

 Absolute Extrema and Optimization Practice

URL:

 https://www.ck12.org/c/calculus/absolute-extrema-and-optimization/asmtpractice/absolute-extrema-and-optimization-Practice/?referrer=featured_content%3Freferrer%3Dconcept_details

Content Source:

 Other
CK-12
Type: Assessment

Overview:

This self-checking online assessment has 10 questions that will help students practice solving problems to identify and describe the relationship between variables as extrema. There are hints available on the screen, and there is an online scratchpad that students can use to work on the problems.

Content Standard(s):
Mathematics
MA2019 (2019)
Grade: 9-12
Mathematical Modeling
15. Use regression with statistical graphing technology to determine an equation that best fits a set of bivariate data, including nonlinear patterns.

Examples: global temperatures, stock market values, hours of daylight, animal population, carbon dating measurements, online streaming viewership

a. Create a scatter plot with a sufficient number of data points to predict a pattern.

b. Describe the overall relationship between two quantitative variables (increase, decrease, linearity, concavity, extrema, inflection) or pattern of change.

c. Make a prediction based upon patterns.
Unpacked Content
Evidence Of Student Attainment:
Students:
  • Can use graphing technology to plot a scatter diagram.
  • Can describe a relationship if one exists between points.
  • Can find a regression equation using graphing technology.
  • Can use the regression equation to make a prediction.
Teacher Vocabulary:
  • Regression Equation
  • "Best Fit"
  • Bivariate Data
  • Linear Pattern
  • Non-linear pattern
  • Scatter Plot
  • Quantitative Variable
  • Extrema
  • Inflection
Knowledge:
Students know:
  • how to plot points using graphing technology.
  • how to find a regression equation using graphing technology.
  • how to use a regression equation to make a prediction.
Skills:
Students are able to:
  • plot points.
  • Distinguish between linear and nonlinear functions.
  • Use graphing technology.
Understanding:
Students understand that:
  • Regression equations can be used to model data.
  • Graphing technology helps us find regression equations.
  • The regression equation can be used to make a prediction.
Diverse Learning Needs:
Essential Skills:
Learning Objectives:
MMOD.15.1: Define bivariate scatter plot, outlier, cluster, linear, nonlinear, positive and negative association, slope, intercept, linear, equation, concave up, concave down, and bivariate.
MMOD.15.2: Make a prediction based upon patterns.
MMOD.15.3: Describe patterns found in a scatter plot.
MMOD.15.4: Demonstrate how to label and plot information on a scatter plot (dot plot).
MMOD.15.5: Distinguish the difference between positive and negative correlation.
MMOD.15.6: When given data points, use technology to find the equation of a line.

Prior Knowledge Skills:
  • Define bivariate scatter plot, quantitative data, outlier, cluster, linear, nonlinear, and positive and negative association.
  • Describe patterns found in a scatter plot.
  • Demonstrate how to label and plot information on a scatter plot (dot plot).
  • Distinguish the difference between positive and negative correlation.
  • Recall how to describe the spread of the scatter plot (dot plot).
  • Create a scatter plot and line of best fit using data from a spreadsheet.
  • Organize and display bivariate quatitative data using a scatter plot, and extend from simple cases by hand to more complex cases involving a large data set using technology.
  • Create a scatter plot of data.
  • Calculate the fit of the function to the data by examining residuals.
Tags: absolute, extrema, function, graph, mathematical modeling, optimization
License Type: Custom Permission Type
See Terms: https://www.ck12info.org/terms-of-use/
For full descriptions of license types and a guide to usage, visit :
https://creativecommons.org/licenses
Accessibility
Comments

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  This resource provided by:  
Author: Hannah Bradley
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