Introduction to formulating statistical problems and analyzing data using open-source software. Graphical and numerical descriptives. combinatorial formulae, Bayes' Theorem, probability, Discrete and continuous distributions, means and variances. Point and interval estimates, and hypothesis tests for one- and two-samples using Central Limit Theorem, and permutation tests. Precludes additional credit for BIT 2000, BIT 2009, BIT 2100 (no longer offered), BIT 2300 (no longer offered), ECON 2201 (no longer offered), ECON 2210, ENST 2006, GEOG 2006, STAT 2507, STAT 2601, STAT 2606 (no longer offered), and STAT 3502. May not be counted for credit in any program if taken after successful completion of STAT 2655. Prerequisite(s): An Ontario Grade 12 university- preparation Mathematics or equivalent, or permission of the Institute for Data Science. Lectures three hours a week, laboratory one hour a week.
Introduction to formulating statistical problems and analyzing data using open-source software. Graphical and numerical descriptives. combinatorial formulae, Bayes' Theorem, probability, Discrete and continuous distributions, means and variances. Point and interval estimates, and hypothesis tests for one- and two-samples using Central Limit Theorem, and permutation tests. Precludes additional credit for BIT 2000, BIT 2009, BIT 2100 (no longer offered), BIT 2300 (no longer offered), ECON 2201 (no longer offered), ECON 2210, ENST 2006, GEOG 2006, STAT 2507, STAT 2601, STAT 2606 (no longer offered), and STAT 3502. May not be counted for credit in any program if taken after successful completion of STAT 2655. Prerequisite(s): An Ontario Grade 12 university- preparation Mathematics or equivalent, or permission of the Institute for Data Science. Lectures three hours a week, laboratory one hour a week.
