{"id":342,"date":"2024-06-05T04:59:00","date_gmt":"2024-06-05T04:59:00","guid":{"rendered":"https:\/\/research.ced.ncsu.edu\/ptmt\/?page_id=342"},"modified":"2024-09-30T10:56:28","modified_gmt":"2024-09-30T10:56:28","slug":"teaching-statistics-investigations","status":"publish","type":"page","link":"https:\/\/research.ced.ncsu.edu\/ptmt\/teaching-statistics-investigations\/","title":{"rendered":"Teaching Statistics Investigations"},"content":{"rendered":"\n
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There are 4 chapters in this set of materials that are similar to Chapters 1-4 in the Data Analysis and Probability materials<\/strong><\/a>. These new materials address the same statistical concepts using the same large multivariate data sets as the former DAP materials, but have been reorganized and enhanced to take advantage of online apps and a free online data tool, CODAP. These new chapters align with updated standards and using online data tools can increase teachers\u2019 and students\u2019 usability and access in schools. These new materials were developed as part of the ESTEEM project<\/strong><\/a>, aiming to enhance statistics teacher education through developing e-materials. A major emphasis is a focus on the statistical investigation process using larger multivariate data sets and taking advantage of multiple linked ways to visualize data using tables, graphs, and maps. CODAP<\/a> <\/strong>is being developed through the Concord Consortium, in partnership with the ESTEEM project. CODAP has similar functionality to TinkerPlots and Fathom and is continuously being updated with new features. Check out an introductory video<\/strong><\/a> to see some of what CODAP can do!<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Complete a 5 minute Getting Started with CODAP<\/strong><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Choose a Chapter Below<\/strong><\/h3>\n\n\n\n
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1. Center, Spread, and Comparing Distributions<\/strong><\/a><\/h3>\n\n\n\n

Teachers engage in exploratory data analysis to examine national school expenditure data using CODAP, explore measures of center with an applet, and consider how the use of technology affects the teaching and learning of statistics.<\/p>\n<\/div>\n\n\n\n

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2. Students\u2019 Reasoning About Comparing Distributions<\/strong><\/a><\/h3>\n\n\n\n

Teachers engage in a task to compare graduation rates with a sample of data from colleges in NC in CODAP, and anticipate how middle school students might approach the task. Teachers then view a video case of teachers and students working with TinkerPlots on the task, analyze students\u2019 work and interactions with the tool, and consider implications for engaging students in multivariate datasets with dynamic data tools.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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3. Developing Conceptual Understanding of Measures of Variability<\/strong><\/a><\/h3>\n\n\n\n

Variability is at the heart of statistics, and understanding and measuring it is an important aspect of analyzing data. This chapter is not a statistical investigation. Instead, teachers use free online apps to explore the mathematical structures of common measures of variability.<\/p>\n<\/div>\n\n\n\n

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4. Using Multivariate Data to Investigate Fuel Economy in Vehicles<\/strong><\/a><\/h3>\n\n\n\n

Teachers use CODAP to investigate fuel economy using a random sample of 300 vehicles manufactured in 2015 that includes many different attributes such as fuel efficiency in the City and Highway, annual fuel costs, whether the vehicle is a hybrid, the transmission type, and vehicle type. This multivariate data set in CODAP allows for investigating questions concerning subgroups of data and bivariate relationships.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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There are 4 chapters in this set of materials that are similar to Chapters 1-4 in the Data Analysis and Probability materials. These new materials address the same statistical concepts using the same large multivariate data sets as the former DAP materials, but have been reorganized and enhanced to take advantage of online apps and […]<\/p>\n","protected":false},"author":66,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"ncst_custom_author":"","ncst_show_custom_author":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-342","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/pages\/342","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/users\/66"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/comments?post=342"}],"version-history":[{"count":28,"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/pages\/342\/revisions"}],"predecessor-version":[{"id":1834,"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/pages\/342\/revisions\/1834"}],"wp:attachment":[{"href":"https:\/\/research.ced.ncsu.edu\/ptmt\/wp-json\/wp\/v2\/media?parent=342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}