100%(1)1 out of 1 people found this document helpful
This preview shows page 1 - 2 out of 4 pages.
Fall 2018, STA 2023 Introductory Statistics (3 credits) CRN#: 12330 Section #: 009 Instructor: Yonas Abraha Office location: SE 219 E-mail Address: [email protected]Office hours: MW 1:00 - 2:00 pm & M 5:30 - 6:30 pm. Lab location & hours: Online Lecturelocation & hours: Online Required Website: Introduction to Data Mining via LiMeS atNote: Purchase required.Prerequisite:MAC 1105 or MGF 1106 or MAC 2233 (grade of C or better in any)Course Description: STA 2023 is an introductory statistics course covering basic data analysis, data and chart manipulation, basic probability theories and simple simulations, T-tests, regression, multiple regression, confidence intervals and normal distribution. Laboratory required. Topics Covered: Basic and descriptive statistics: mean, median, standard deviation, range, pie and bar chart, basic data manipulation and sampling procedures. Probability: uniform, binomial, and normal distributions, sampling distributions and some basic probability computations. Inference: statistical/hypothesis testing, one-sided and two-sided t-test, confidence interval, interpretation of P-value. Basic regression: slope and intercept interpretation, correlation coefficient, P-value, standard error. Software: Required website: Introduction to Data Mining via LiMeS at . Go to Canvas course site for instructions to register with LiMeS. Note: Purchase required. Available via online and in the FAU bookstore. There is a 21-day trial period. Plan to pay before the trial period is over to avoid missing any assessments and a $10 handling fee. Technology:This course will be mostly conducted using the Excel software package on Windows-based machines. Students are required to obtain their own Excel software licenses or obtain it free at / Objectives, Learning Outcome Goals: This course aims to impart an understanding of elementary descriptive and inferential statistics. The emphasis will be on applied problem solving and interpretation of results, although computation will also be required. Students who successfully complete this course should be able to calculate and explain basic descriptive statistics as well as basics probability theories; generate and interpret tables and graphs using Excel; make estimates of unknown parameters and conduct hypothesis tests by performing t-tests on Excel; and using Excel to construct and interpret a simple linear regression model. Successful completion of this course counts toward the computational requirement of the Gordon Rule.