Unit 1: Logical and Proportional Reasoning<\/h1>\r\n\r\n \t- Analyze and solve problems involving absolute and relative change<\/li>\r\n \t
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\r\n \t
- Use laws of logic to evaluate the validity of arguments<\/li>\r\n<\/ul>\r\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\r\nModule 1B: Measurement and Dimensional Analysis<\/h2>\r\nModule 1C: Set Theory and Logic<\/h2>\r\n(optional) Module 1D: Historical Counting Systems<\/h2>\r\n(optional) Module 1E: Fractals<\/h2>\r\nUnit 2: Modeling<\/h1>\r\n\r\n \t- Create, use, and interpret graphs and equations that model real-world situations<\/li>\r\n \t
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\r\n \t
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\r\n<\/ul>\r\n
Module 2A: Modeling Growth<\/h2>\r\nModule 2B: Finance<\/h2>\r\n(optional) Module 2C: Graph Theory<\/h2>\r\n(optional) Module 2D: Voting Theory<\/h2>\r\nUnit 3: Probability and Statistics<\/h1>\r\n\r\n \t- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\r\n \t
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\r\n \t
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\r\n \t
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\r\n \t
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\r\n<\/ul>\r\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\r\nModule 3B: Statistics: Describing Data<\/h2>\r\nModule 3C: Probability<\/h2>\r\n(optional) Module 3D: Statistical Thinking<\/h2>\r\n<\/div>","rendered":"
![\"an](\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/1141\/2018\/01\/11222952\/binoculars2.png\")
<\/p>\n
The following list shows an outline of the topics covered in this course. To see all of the course pages, visit the\u00a0Table of Contents<\/a>.<\/p>\nThere are three broad categories of instructional units<\/span>:\u00a0<\/span>Logical and Proportional Reasoning, Modeling<\/span>,<\/span> and\u00a0<\/span>Probability and Statistics. The topics under each category<\/span> will\u00a0<\/span>allow<\/span> st<\/span>udents<\/span> to<\/span> demonstrate the course’s learning outcomes.\u00a0<\/span>Students will make use of<\/span> appropriate<\/span> technology and will be\u00a0<\/span>asked to explain their thinking both orally and in writing.<\/span><\/span><\/p>\n\nUnit 1: Logical and Proportional Reasoning<\/h1>\n\n- Analyze and solve problems involving absolute and relative change<\/li>\n
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\n
- Use laws of logic to evaluate the validity of arguments<\/li>\n<\/ul>\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\nModule 1B: Measurement and Dimensional Analysis<\/h2>\nModule 1C: Set Theory and Logic<\/h2>\n(optional) Module 1D: Historical Counting Systems<\/h2>\n(optional) Module 1E: Fractals<\/h2>\nUnit 2: Modeling<\/h1>\n\n- Create, use, and interpret graphs and equations that model real-world situations<\/li>\n
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\n
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\n<\/ul>\n
Module 2A: Modeling Growth<\/h2>\nModule 2B: Finance<\/h2>\n(optional) Module 2C: Graph Theory<\/h2>\n(optional) Module 2D: Voting Theory<\/h2>\nUnit 3: Probability and Statistics<\/h1>\n\n- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\n
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\n
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\n
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\n
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\n<\/ul>\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\nModule 3B: Statistics: Describing Data<\/h2>\nModule 3C: Probability<\/h2>\n(optional) Module 3D: Statistical Thinking<\/h2>\n<\/div>\n\n\t\t\t \n\t\t\t Candela Citations<\/h3>\n\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t\t\t CC licensed content, Original<\/div>- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>CC licensed content, Shared previously<\/div>
- Binoculars Icon. Authored by<\/strong>: Musmellow. Provided by<\/strong>: Noun Project. Located at<\/strong>: https:\/\/thenounproject.com\/term\/binoculars\/1234056\/<\/a>. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":678587,"menu_order":2,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Course Contents at a Glance\",\"author\":\"\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"cc\",\"description\":\"Binoculars Icon\",\"author\":\"Musmellow\",\"organization\":\"Noun Project\",\"url\":\"https:\/\/thenounproject.com\/term\/binoculars\/1234056\/\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-22","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/users\/678587"}],"version-history":[{"count":9,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions"}],"predecessor-version":[{"id":3359,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions\/3359"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/media?parent=22"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=22"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/contributor?post=22"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/license?post=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\r\nModule 1B: Measurement and Dimensional Analysis<\/h2>\r\nModule 1C: Set Theory and Logic<\/h2>\r\n(optional) Module 1D: Historical Counting Systems<\/h2>\r\n(optional) Module 1E: Fractals<\/h2>\r\nUnit 2: Modeling<\/h1>\r\n\r\n \t- Create, use, and interpret graphs and equations that model real-world situations<\/li>\r\n \t
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\r\n \t
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\r\n<\/ul>\r\n
Module 2A: Modeling Growth<\/h2>\r\nModule 2B: Finance<\/h2>\r\n(optional) Module 2C: Graph Theory<\/h2>\r\n(optional) Module 2D: Voting Theory<\/h2>\r\nUnit 3: Probability and Statistics<\/h1>\r\n\r\n \t- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\r\n \t
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\r\n \t
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\r\n \t
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\r\n \t
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\r\n<\/ul>\r\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\r\nModule 3B: Statistics: Describing Data<\/h2>\r\nModule 3C: Probability<\/h2>\r\n(optional) Module 3D: Statistical Thinking<\/h2>\r\n<\/div>","rendered":"
<\/p>\n
The following list shows an outline of the topics covered in this course. To see all of the course pages, visit the\u00a0Table of Contents<\/a>.<\/p>\nThere are three broad categories of instructional units<\/span>:\u00a0<\/span>Logical and Proportional Reasoning, Modeling<\/span>,<\/span> and\u00a0<\/span>Probability and Statistics. The topics under each category<\/span> will\u00a0<\/span>allow<\/span> st<\/span>udents<\/span> to<\/span> demonstrate the course’s learning outcomes.\u00a0<\/span>Students will make use of<\/span> appropriate<\/span> technology and will be\u00a0<\/span>asked to explain their thinking both orally and in writing.<\/span><\/span><\/p>\n\nUnit 1: Logical and Proportional Reasoning<\/h1>\n\n- Analyze and solve problems involving absolute and relative change<\/li>\n
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\n
- Use laws of logic to evaluate the validity of arguments<\/li>\n<\/ul>\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\nModule 1B: Measurement and Dimensional Analysis<\/h2>\nModule 1C: Set Theory and Logic<\/h2>\n(optional) Module 1D: Historical Counting Systems<\/h2>\n(optional) Module 1E: Fractals<\/h2>\nUnit 2: Modeling<\/h1>\n\n- Create, use, and interpret graphs and equations that model real-world situations<\/li>\n
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\n
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\n<\/ul>\n
Module 2A: Modeling Growth<\/h2>\nModule 2B: Finance<\/h2>\n(optional) Module 2C: Graph Theory<\/h2>\n(optional) Module 2D: Voting Theory<\/h2>\nUnit 3: Probability and Statistics<\/h1>\n\n- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\n
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\n
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\n
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\n
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\n<\/ul>\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\nModule 3B: Statistics: Describing Data<\/h2>\nModule 3C: Probability<\/h2>\n(optional) Module 3D: Statistical Thinking<\/h2>\n<\/div>\n\n\t\t\t \n\t\t\t Candela Citations<\/h3>\n\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t\t\t CC licensed content, Original<\/div>- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>CC licensed content, Shared previously<\/div>
- Binoculars Icon. Authored by<\/strong>: Musmellow. Provided by<\/strong>: Noun Project. Located at<\/strong>: https:\/\/thenounproject.com\/term\/binoculars\/1234056\/<\/a>. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":678587,"menu_order":2,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Course Contents at a Glance\",\"author\":\"\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"cc\",\"description\":\"Binoculars Icon\",\"author\":\"Musmellow\",\"organization\":\"Noun Project\",\"url\":\"https:\/\/thenounproject.com\/term\/binoculars\/1234056\/\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-22","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/users\/678587"}],"version-history":[{"count":9,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions"}],"predecessor-version":[{"id":3359,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions\/3359"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/media?parent=22"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=22"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/contributor?post=22"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/license?post=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Module 1C: Set Theory and Logic<\/h2>\r\n(optional) Module 1D: Historical Counting Systems<\/h2>\r\n(optional) Module 1E: Fractals<\/h2>\r\nUnit 2: Modeling<\/h1>\r\n\r\n \t- Create, use, and interpret graphs and equations that model real-world situations<\/li>\r\n \t
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\r\n \t
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\r\n<\/ul>\r\n
Module 2A: Modeling Growth<\/h2>\r\nModule 2B: Finance<\/h2>\r\n(optional) Module 2C: Graph Theory<\/h2>\r\n(optional) Module 2D: Voting Theory<\/h2>\r\nUnit 3: Probability and Statistics<\/h1>\r\n\r\n \t- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\r\n \t
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\r\n \t
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\r\n \t
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\r\n \t
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\r\n<\/ul>\r\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\r\nModule 3B: Statistics: Describing Data<\/h2>\r\nModule 3C: Probability<\/h2>\r\n(optional) Module 3D: Statistical Thinking<\/h2>\r\n<\/div>","rendered":"
<\/p>\n
The following list shows an outline of the topics covered in this course. To see all of the course pages, visit the\u00a0Table of Contents<\/a>.<\/p>\nThere are three broad categories of instructional units<\/span>:\u00a0<\/span>Logical and Proportional Reasoning, Modeling<\/span>,<\/span> and\u00a0<\/span>Probability and Statistics. The topics under each category<\/span> will\u00a0<\/span>allow<\/span> st<\/span>udents<\/span> to<\/span> demonstrate the course’s learning outcomes.\u00a0<\/span>Students will make use of<\/span> appropriate<\/span> technology and will be\u00a0<\/span>asked to explain their thinking both orally and in writing.<\/span><\/span><\/p>\n\nUnit 1: Logical and Proportional Reasoning<\/h1>\n\n- Analyze and solve problems involving absolute and relative change<\/li>\n
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\n
- Use laws of logic to evaluate the validity of arguments<\/li>\n<\/ul>\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\nModule 1B: Measurement and Dimensional Analysis<\/h2>\nModule 1C: Set Theory and Logic<\/h2>\n(optional) Module 1D: Historical Counting Systems<\/h2>\n(optional) Module 1E: Fractals<\/h2>\nUnit 2: Modeling<\/h1>\n\n- Create, use, and interpret graphs and equations that model real-world situations<\/li>\n
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\n
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\n<\/ul>\n
Module 2A: Modeling Growth<\/h2>\nModule 2B: Finance<\/h2>\n(optional) Module 2C: Graph Theory<\/h2>\n(optional) Module 2D: Voting Theory<\/h2>\nUnit 3: Probability and Statistics<\/h1>\n\n- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\n
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\n
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\n
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\n
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\n<\/ul>\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\nModule 3B: Statistics: Describing Data<\/h2>\nModule 3C: Probability<\/h2>\n(optional) Module 3D: Statistical Thinking<\/h2>\n<\/div>\n\n\t\t\t \n\t\t\t Candela Citations<\/h3>\n\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t\t\t CC licensed content, Original<\/div>- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>CC licensed content, Shared previously<\/div>
- Binoculars Icon. Authored by<\/strong>: Musmellow. Provided by<\/strong>: Noun Project. Located at<\/strong>: https:\/\/thenounproject.com\/term\/binoculars\/1234056\/<\/a>. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":678587,"menu_order":2,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Course Contents at a Glance\",\"author\":\"\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"cc\",\"description\":\"Binoculars Icon\",\"author\":\"Musmellow\",\"organization\":\"Noun Project\",\"url\":\"https:\/\/thenounproject.com\/term\/binoculars\/1234056\/\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-22","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/users\/678587"}],"version-history":[{"count":9,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions"}],"predecessor-version":[{"id":3359,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions\/3359"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/media?parent=22"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=22"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/contributor?post=22"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/license?post=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
(optional) Module 1E: Fractals<\/h2>\r\nUnit 2: Modeling<\/h1>\r\n\r\n \t- Create, use, and interpret graphs and equations that model real-world situations<\/li>\r\n \t
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\r\n \t
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\r\n<\/ul>\r\n
Module 2A: Modeling Growth<\/h2>\r\nModule 2B: Finance<\/h2>\r\n(optional) Module 2C: Graph Theory<\/h2>\r\n(optional) Module 2D: Voting Theory<\/h2>\r\nUnit 3: Probability and Statistics<\/h1>\r\n\r\n \t- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\r\n \t
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\r\n \t
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\r\n \t
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\r\n \t
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\r\n<\/ul>\r\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\r\nModule 3B: Statistics: Describing Data<\/h2>\r\nModule 3C: Probability<\/h2>\r\n(optional) Module 3D: Statistical Thinking<\/h2>\r\n<\/div>","rendered":"
<\/p>\n
The following list shows an outline of the topics covered in this course. To see all of the course pages, visit the\u00a0Table of Contents<\/a>.<\/p>\nThere are three broad categories of instructional units<\/span>:\u00a0<\/span>Logical and Proportional Reasoning, Modeling<\/span>,<\/span> and\u00a0<\/span>Probability and Statistics. The topics under each category<\/span> will\u00a0<\/span>allow<\/span> st<\/span>udents<\/span> to<\/span> demonstrate the course’s learning outcomes.\u00a0<\/span>Students will make use of<\/span> appropriate<\/span> technology and will be\u00a0<\/span>asked to explain their thinking both orally and in writing.<\/span><\/span><\/p>\n\nUnit 1: Logical and Proportional Reasoning<\/h1>\n\n- Analyze and solve problems involving absolute and relative change<\/li>\n
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\n
- Use laws of logic to evaluate the validity of arguments<\/li>\n<\/ul>\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\nModule 1B: Measurement and Dimensional Analysis<\/h2>\nModule 1C: Set Theory and Logic<\/h2>\n(optional) Module 1D: Historical Counting Systems<\/h2>\n(optional) Module 1E: Fractals<\/h2>\nUnit 2: Modeling<\/h1>\n\n- Create, use, and interpret graphs and equations that model real-world situations<\/li>\n
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\n
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\n<\/ul>\n
Module 2A: Modeling Growth<\/h2>\nModule 2B: Finance<\/h2>\n(optional) Module 2C: Graph Theory<\/h2>\n(optional) Module 2D: Voting Theory<\/h2>\nUnit 3: Probability and Statistics<\/h1>\n\n- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\n
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\n
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\n
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\n
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\n<\/ul>\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\nModule 3B: Statistics: Describing Data<\/h2>\nModule 3C: Probability<\/h2>\n(optional) Module 3D: Statistical Thinking<\/h2>\n<\/div>\n\n\t\t\t \n\t\t\t Candela Citations<\/h3>\n\t\t\t\t\t \n\t\t\t\t\t\t \n\t\t\t\t\t\t\t CC licensed content, Original<\/div>- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>CC licensed content, Shared previously<\/div>
- Binoculars Icon. Authored by<\/strong>: Musmellow. Provided by<\/strong>: Noun Project. Located at<\/strong>: https:\/\/thenounproject.com\/term\/binoculars\/1234056\/<\/a>. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":678587,"menu_order":2,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Course Contents at a Glance\",\"author\":\"\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"cc\",\"description\":\"Binoculars Icon\",\"author\":\"Musmellow\",\"organization\":\"Noun Project\",\"url\":\"https:\/\/thenounproject.com\/term\/binoculars\/1234056\/\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-22","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/users\/678587"}],"version-history":[{"count":9,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions"}],"predecessor-version":[{"id":3359,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions\/3359"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/media?parent=22"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=22"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/contributor?post=22"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/license?post=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \r\n \t
- Create, use, and interpret graphs and equations that model real-world situations<\/li>\r\n \t
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\r\n \t
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\r\n<\/ul>\r\n
Module 2A: Modeling Growth<\/h2>\r\n
Module 2B: Finance<\/h2>\r\n
(optional) Module 2C: Graph Theory<\/h2>\r\n
(optional) Module 2D: Voting Theory<\/h2>\r\n
Unit 3: Probability and Statistics<\/h1>\r\n
- \r\n \t
- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\r\n \t
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\r\n \t
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\r\n \t
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\r\n \t
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\r\n<\/ul>\r\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\r\n
Module 3B: Statistics: Describing Data<\/h2>\r\n
Module 3C: Probability<\/h2>\r\n
(optional) Module 3D: Statistical Thinking<\/h2>\r\n<\/div>","rendered":"
<\/p>\nThe following list shows an outline of the topics covered in this course. To see all of the course pages, visit the\u00a0Table of Contents<\/a>.<\/p>\n
There are three broad categories of instructional units<\/span>:\u00a0<\/span>Logical and Proportional Reasoning, Modeling<\/span>,<\/span> and\u00a0<\/span>Probability and Statistics. The topics under each category<\/span> will\u00a0<\/span>allow<\/span> st<\/span>udents<\/span> to<\/span> demonstrate the course’s learning outcomes.\u00a0<\/span>Students will make use of<\/span> appropriate<\/span> technology and will be\u00a0<\/span>asked to explain their thinking both orally and in writing.<\/span><\/span><\/p>\n
\nUnit 1: Logical and Proportional Reasoning<\/h1>\n
- \n
- Analyze and solve problems involving absolute and relative change<\/li>\n
- Interpret and compare ratios in authentic contexts (e.g., news articles or advertisements)<\/li>\n
- Use laws of logic to evaluate the validity of arguments<\/li>\n<\/ul>\n
Module 1A: Problem Solving and Proportional Reasoning<\/h2>\n
Module 1B: Measurement and Dimensional Analysis<\/h2>\n
Module 1C: Set Theory and Logic<\/h2>\n
(optional) Module 1D: Historical Counting Systems<\/h2>\n
(optional) Module 1E: Fractals<\/h2>\n
Unit 2: Modeling<\/h1>\n
- \n
- Create, use, and interpret graphs and equations that model real-world situations<\/li>\n
- Identify assumptions, parameters, and limitations in creating and assessing real-world models<\/li>\n
- Choose, create, and interpret linear, exponential, logarithmic and logistic models of real-world problems<\/li>\n<\/ul>\n
Module 2A: Modeling Growth<\/h2>\n
Module 2B: Finance<\/h2>\n
(optional) Module 2C: Graph Theory<\/h2>\n
(optional) Module 2D: Voting Theory<\/h2>\n
Unit 3: Probability and Statistics<\/h1>\n
- \n
- Evaluate claims based on empirical, theoretical, and subjective probabilities<\/li>\n
- Use data displays and models to determine probabilities, including conditional probabilities, and use them to draw conclusions<\/li>\n
- Use statistical information from studies, surveys, and polls to make informed decisions<\/li>\n
- Summarize and interpret datasets with regard to shape, center and spread. Be able to compare data sets<\/li>\n
- Use technology to summarize and interpret univariate, bivariate, and multivariable data using appropriate graphical displays and numerical summary statistics. Be able to describe strengths, limitations, and bias in graphical displays<\/li>\n<\/ul>\n
Module 3A: Statistics: Collecting and Classifying Data<\/h2>\n
Module 3B: Statistics: Describing Data<\/h2>\n
Module 3C: Probability<\/h2>\n
(optional) Module 3D: Statistical Thinking<\/h2>\n<\/div>\n\n\t\t\t
\n\t\t\t Candela Citations<\/h3>\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCC licensed content, Original<\/div>- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>CC licensed content, Shared previously<\/div>
- Binoculars Icon. Authored by<\/strong>: Musmellow. Provided by<\/strong>: Noun Project. Located at<\/strong>: https:\/\/thenounproject.com\/term\/binoculars\/1234056\/<\/a>. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":678587,"menu_order":2,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Course Contents at a Glance\",\"author\":\"\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"cc\",\"description\":\"Binoculars Icon\",\"author\":\"Musmellow\",\"organization\":\"Noun Project\",\"url\":\"https:\/\/thenounproject.com\/term\/binoculars\/1234056\/\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-22","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/users\/678587"}],"version-history":[{"count":9,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions"}],"predecessor-version":[{"id":3359,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/revisions\/3359"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapters\/22\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/media?parent=22"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=22"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/contributor?post=22"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/ct-state-quantitative-reasoning\/wp-json\/wp\/v2\/license?post=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Course Contents at a Glance. Provided by<\/strong>: Lumen Learning. License<\/strong>: CC BY: Attribution<\/a><\/em><\/li><\/ul>