# Course Materials

Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/2994

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Now showing 1 - 4 of 4
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College Algebra Structured Notes Workbook
(Montana State University, 2024) Staebler, Heidi
This structured note packet / workbook is designed to be used for in-class instruction by instructors with a wide variety of experience levels in a College Algebra course that prepares students for 4 credit hour Precalculus and Survey of Calculus courses. It includes topics that are found in OER Intermediate Algebra and College Algebra texts and is designed to promote instruction that strikes a balance between promoting foundational skills, conceptual understanding, connections between ideas / representations, applications and modeling. Each section / lesson contains the following components: • Link(s) to online OER reference text section(s) / resource(s) • Sectional objectives and vocabulary words / phrases • Break-out boxes for key definitions / ideas / strategies • Instructional examples interspersed with You Try examples • Associated MyOpenMath homework problem set (pilot during fall 2024) There is not a one-to-one match between each section and a 50-minute class session.
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Stat 216 Course Pack Spring 2016: Activities and Notes
(2016-01) Robison-Cox, James
Course notes developed for introductory statistics course at Montana State University using active learning approach. Each activity is preceded by a reading.
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Intermediate Statistics with R
(2014-01) Greenwood, Mark C.
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.1 of the book.