University Introductory Statistics Worksheets

Which sets match your course code

University Introductory Statistics is not one course with one number. The material is split across different courses in the public calendars of Montreal universities, and each orders and weights it its own way. Find your code below for the sets that cover its material, then check your own outline for what your exam includes.

• MAST 221 — Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions
• MAST 333 — Descriptive Statistics, Continuous Distributions and the Normal Model, Statistics Sampling Distributions, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests, Simple Linear Regression and Correlation
• MAT1185 — Descriptive Statistics, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests
• MAT1720 — Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions
• MAT2080 — Descriptive Statistics, Statistics Sampling Distributions, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests, Simple Linear Regression and Correlation
• MAT350 — Descriptive Statistics, Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests, Simple Linear Regression and Correlation
• MATH 10603 — Descriptive Statistics, Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions
• MATH 203 — Descriptive Statistics, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests
• STAT 249 — Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model
• STT1700 — Descriptive Statistics, Probability Rules and Conditional Probability, Discrete Random Variables, Continuous Distributions and the Normal Model, Statistics Sampling Distributions, Confidence Intervals, Hypothesis Tests, Two-Sample Inference and Chi-Square Tests, Simple Linear Regression and Correlation

What University Introductory Statistics covers

This is the first statistics course a science, management, biology or engineering program asks for, and it runs in one long arc. It opens with description: types of variables, frequency tables, histograms, the mean, median and mode, the standard deviation, quartiles and boxplots. Probability follows, from sample spaces and the addition rule to conditional probability, independence and tree diagrams, and it is put to work on random variables: mass functions, expected value and variance, the binomial model, and then densities, the normal table and the normal approximation to the binomial.

The second half is inference, and every set is built the same way. The sampling distribution of a mean or a proportion and the central limit theorem come first, because nothing later works without them; then confidence intervals, then hypothesis tests with their p-values and error risks, then the two-sample and chi-square procedures, and finally the least-squares line, the coefficient of determination and inference for a slope. Each set asks for the same three things: choose the procedure, check that its conditions hold, and interpret the result in the words of the question — an answer that stops at a number is unfinished.

The course stands on Secondary 5 mathematics only, and no sheet is calculus-based. No question asks for a derivative or an integral: a probability under a density is an area found with rectangles and triangles or read from a distribution function that is given, and a mean comes from symmetry or a supplied formula. Nothing is done from software output, and analysis of variance, multiple regression and inference on variances are left out. The courses that carry this material at university split it differently — some stop at the central limit theorem, some teach the probability half as a separate course, and some reach regression only if time allows. The list of course codes on this page says which sets each one covers.