ࡱ> on}k R"bjbjZ Z 7N8cb8cb^D D 8 lxDe(4L: eeeeeee$Dgi?eLL?e4Teee\ bPy:j^<eje0e^jjxbbjb`?e?eRTejD B : GROSSMONT COLLEGE COURSE OUTLINE OF RECORD Curriculum Committee Approval: 04/26/2022 GCCCD Governing Board Approval: 06/14/2022 MATHEMATICS 160 ELEMENTARY STATISTICS 1. Course Number Course Title Semester Units Math 160 Elementary Statistics 4 Semester Hours 4 hours lecture: 64-72 hours 128-144 outside-of-class hours 192-216 total hours 2. Course Prerequisite A C grade or higher or Pass in Math 096 or Math 103 or Math 108 or Math 110 or equivalent or appropriate placement beyond intermediate algebra Corequisite None Recommended Preparation None 3. Catalog Description This course provides an introduction to descriptive statistics, probability theory and inferential statistics. Topics include data collection; summary and graphical displays of data; measures of central tendency and variability; elementary probability theory; standard procedures involving the normal, binomial, students t, chi-square, and F distributions; confidence intervals and hypothesis testing; linear correlation and regression; and ANOVA. Students will learn technology for statistical analysis and interpret the relevance of the statistical findings. Applications come from various fields such as biology, business, economics, education, social sciences, health science, life sciences, and psychology. 4. Course Objectives The student will: Distinguish among different scales of measurement and their implications; Interpret data displayed in tables and graphically; Apply concepts of sample space and probability; Calculate measures of central tendency and variation for a given data set; Identify the standard methods of obtaining data and identify advantages and disadvantages of each; Calculate the mean and variance of a discrete distribution; Calculate probabilities using normal and t-distributions; Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem; Construct and interpret confidence intervals and calculate point estimates; Determine and interpret levels of statistical significance including p-values; Interpret the output of a technology-based statistical analysis; Identify the basic concept of hypothesis testing including Type I and II errors; Formulate hypothesis tests involving samples from one and two populations; Select the appropriate technique for testing a hypothesis and interpret the result; Use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics; and Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including biology, business, economics, education, health science, social sciences, life sciences, and psychology. 5. Instructional Facilities Standard classroom 6. Special Materials Required of Student Graphing calculator Statistical software 7. Course Content Summarizing data graphically and numerically: histograms, stem and leaf chart, dot plot, box and whisker plot, percentiles, frequency tables; Descriptive statistics: measures of central tendency, variation, relative position, and levels/scales of measurement; Sample spaces and probability: simple, compound, complementary, independent and mutually exclusive events; conditional probabilities; Random variables and expected value; Sampling and sampling distributions; Discrete distributions Binomial Continuous distributions Normal, Students t, chi-square, and F; The Central Limit Theorem; Estimation and confidence intervals for population mean, population standard deviation and proportions; Hypothesis Testing and inference, including t-tests for one and two populations, Chi-square test. Correlation and linear regression Analysis of variance (ANOVA) and contingency tables; Applications using data from disciplines including biology, business, economics, education, health science, psychology, life sciences, and social sciences. Include problems relevant to current events and students lived experiences. Statistical analysis using technology such as graphing calculators. Introduce contributions from a diverse group of mathematicians relevant to the content of the course. 8. Method of Instruction Employ a variety of teaching methods, including lectures, instructor presented examples, student-led discussions, collaborative learning, think-pair-share, formative assessments (e.g. exit slips), and multimedia presentations. These instructional techniques strive to include students lived experiences and different cultural and historical perspectives. 9. Methods of Evaluating Student Performance Homework Classwork Participation Group or individual project (e.g. technology-based statistical analysis using data from various disciplines) Quizzes Exams In-class or in-person proctored comprehensive final exam. 10. Outside Class Assignments Homework Take-home assessments Group or Individual Projects (e.g. technology-based statistical analysis using data from various disciplines) Instructional Videos 11. Representative Texts a. Representative Texts: Triola, Mario. Essentials of Statistics, 6th Edition, New York: Pearson, 2019. Lumen Learning,Concepts in Statistics.Lumen Learning, 2022 HYPERLINK "https://courses.lumenlearning.com/wm-concepts-statistics/" \t "_blank" https://courses.lumenlearning.com/wm-concepts-statistics/. Licensed under a Creative Commons Attribution 4.0. Department of Mathematics Los Medanos College,Math 110Stats with Instructor Resources. Los Medanos College,2020 HYPERLINK "https://4cd.instructure.com/courses/48111/modules" \t "_blank" https://4cd.instructure.com/courses/48111/modules.Licensed under a Creative Commons Attribution3.0 b. Supplementary texts and workbooks: None Addendum: Student Learning Outcomes Upon completion of this course, our students will be able to do the following: Categorize data set and use appropriate methods to find, summarize, and visually display statistics about the data set. Interpret visual display of statistical data Take sample statistics and use appropriate procedures, methods, and tests to make inferences about the population. Categorize probability problems and use appropriate theorems and formulas to solve them. Use the appropriate technology to analyze statistical problems. Interpret, communicate, and assess the validity of statistical processes and conclusions.     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