Math 10 General Course Information
5 quarter credit units
Math 10 is an introduction to
elementary statistical techniques
Students will explore and analyze data using graphical and numerical techniques.
Students will learn to use appropriate statistical models to draw conclusions
from data and to interpret the results of their analyses. Topics
covered include descriptive statistics, probability, inferential
statistics including confidenceintervals and hypothesis tests,
linear regression and correlation. Students
will use technology as appropriate, including the graphing calculator and Excel.
Learning and understanding statistics requires active participation in
collection and analysis of data. Homework problems assigned from the text
involve analysis of data presented in the problems. In addition labs and/or
projects are assigned that encourage hands on particpation with students
working together in groups. Labs and/or projects involve the collection of
data and use of graphing calculator and/or spreadsheet technology,
Math Prerequisite: Completion of Intermediate Algebra Math 114 with grade of C or better; or qualifying score on Intermediate Algebra Placement Test within past calendar year, or equivalent placement.
English Advisory: English Writing 100 & Reading 100 (or Language Arts 100), or ESL 172 & 173. Although this is a Math course, English reading comprehension and writing are very important in Math 10.
Course Outline is available at the following URL http://ecms.deanza.edu/outlineprogresspublic.html?catalogID=2175
Student Learning Outcomes (SLOs)
*Organize, analyze, and utilize appropriate methods to draw conclusions based on sample data by constructing and/or evaluating tables, graphs, and numerical measures of characteristics of data.
*Identify, evaluate, interpret and describe data distributions through the study of sampling distributions and probability theory.
*Collect data, interpret, compose and defend conjectures, and communicate the results of random data using statistical analyses such as interval and point estimates, hypothesis tests, and regression analysis.