(Business and Enrollment Management Division) Descriptive and inferential statistics. Collection, analysis, and presentation of business and economic data using probability techniques, hypothesis testing, and predictive strategies to facilitate decision-making. Topics include measures of central tendency and deviation, probability and sampling distributions; statistical inference, correlation, and linear regression analysis of variance, chi-square, and t-tests, and, application of technology for statistical analysis, including the interpretation of the relevance of the statistical findings. No credit if taken after STAT 018, STAT 050, or STAT 050H. Total of 54 hours lecture and 54 hours laboratory.

(Social Sciences Division) Data-analysis concepts and procedures used in the social sciences. Topics include measures of central tendency and dispersion; probability and sampling distributions, methods of evaluating hypotheses and testing inferential statistics (t-tests, chi-square, ANOVA, correlation and regression). Applications to the social sciences using calculators and spreadsheets. No credit if taken after STAT 015 or STAT C1000 or STAT C1000H. Total of 72 hours lecture.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. No credit if taken after STAT C1000H, STAT 015, STAT 018, STAT 050, or STAT 050H. Formerly: STAT 050. Total of 72 hours lecture.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. This is an honors course. Students are expected to complete additional assignments to demonstrate stronger analytical skills and critical analysis of articles published in peer-reviewed journals and primary scientific literature. No credit if taken after STAT C1000, STAT 015, STAT 018, STAT 050, or STAT 050H. Formerly STAT 050H. Total of 72 hours lecture.

Implement statistical methods, procedures, and analysis in Python, a high-level computer language for statistical programming. Techniques and tools include Python libraries, data frames, graphs, gradient descent, modeling, and simulations. Statistics topics include principles of exploratory data analysis, statistics visualization, probability distributions, hypothesis tests, regression, and optimization using real-world data and examples from biology, GIS, social science, marketing, finance, physics, and other STEM fields. Total 54 hours laboratory.

(Mathematics Division) Review of core prerequisite skills, competencies, and concepts for statistics with an emphasis on critical thinking and problem solving. Intended for students who are concurrently enrolled in STAT C1000: Elementary Statistics. Topics include learning strategies and mathematical knowledge necessary for successful completion of Elementary Statistics. Total of 36 hours laboratory.