# Master Syllabus

 Administrative Unit: Computer and Mathematical Sciences Department Course Prefix and Number: MATH 250 Course Title: Statistics I
Number of:
 Credit Hours 3
 Lecture Hours 3
 Lab Hours 0
 Catalog Description: Introduction to descriptive and inferential statistics. Topics include collection of data, numerical and graphical descriptive methods, linear correlation and regression, probability concepts and distributions, confidence intervals and hypothesis testing for means and proportions. G.E. Prerequisite: Grade of C or higher in MATH 104, or a score of 20 or higher on the math portion of the ACT or a score of 480 or higher on the math portion of the SAT or a passing score on the Columbia College math placement exam. Prerequisite(s) / Corequisite(s): Prerequisite: Grade of C or higher in MATH 104, or a score of 20 or higher on the math portion of the ACT or a score of 480 or higher on the math portion of the SAT or a passing score on the Columbia College math placement exam. Course Rotation for Day Program: Offered Fall and Spring. Text(s): Most current editions of the following:A TI-84 calculator is required for this course. This calculator wil be allowed on most assessment opportunities in this course. Fundamentals of StatisticsBy Sullivan (Prentice Hall) RecommendedEssential Statistics: Exploring the World Through DataBy Gould and Ryan (Pearson) Recommended Course Learning Outcomes Construct and interpret appropriate graphical displays of qualitative and quantitative data. Describe distributions of quantitative data in terms of shape, center, and spread. Use appropriate methods to explore and describe the relationship between two qualitative or two quantitative variables. Compute and interpret probabilities and conditional probabilities and use probabilities to determine if events are independent. Solve applied problems involving discrete and continuous random variables, including the binomial and normal random variables. Compute and interpret point estimates and interval estimates for a mean or proportion. Test hypotheses for a single mean or proportion using P-values and interpret the results. Major Topics/Skills to be Covered: Many topics can be included in the course but the following are essential. Classify data as qualitative or quantitative (discrete or continuous). Distinguish between observational studies and experiments. Construct frequency distributions for qualitative data and frequency distributions, histograms, stem and leaf plots, and boxplots for quantitative data. Describe distributions of quantitative data in terms of shape, center, and spread. Explore the relationship between two qualitative variables using two-way tables. Explore the relationship between two quantitative variables using scatterplots. Compute and interpret regression lines and correlation coefficients. Compute simple probabilities using the addition rule, the multiplication rule, and complements. Determine if events are independent using conditional probabilities. Construct discrete probability distributions. Compute and interpret the mean (expected value) and standard deviation of a discrete random variable. Compute and interpret probabilities of binomial experiments. Compute probabilities and find percentile values of normally distributed variables. Determine if a data set is approximately normally distributed. Describe the sampling distribution for a sample mean or proportion and compute probabilities associated with these distributions. Compute point estimates and interval estimates for a mean or proportion. Determine the sample size necessary for estimating a mean or proportion to within a given margin of error. Test hypotheses for a mean or proportion using P-values. Use technology wherever possible to simplify computations. Recommended maximum class size for this course: 30 Library Resources: Online databases are available at the Columbia College Stafford Library.  You may access them using your CougarTrack login and password when prompted.
Prepared by: Suzanne Tourville Date: November 16, 2016
NOTE: The intention of this master course syllabus is to provide an outline of the contents of this course, as specified by the faculty of Columbia College, regardless of who teaches the course, when it is taught, or where it is taught. Faculty members teaching this course for Columbia College are expected to facilitate learning pursuant to the course learning outcomes and cover the subjects listed in the Major Topics/Skills to be Covered section. However, instructors are also encouraged to cover additional topics of interest so long as those topics are relevant to the course's subject. The master syllabus is, therefore, prescriptive in nature but also allows for a diversity of individual approaches to course material.