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Mathematics 105

Introductory Statistics

Length of Course: 14 weeks

Classroom Hours per Week: 4 hours

Number of Credits: 3 credits

Prerequisites: BC High School Math 12 with grade C or higher, or BC High School Math 11 with grade B or higher, or Columbia College Math 100 or Math 110 with grade C- or higher

Corequisite(s): English 097 (Advanced Academic Preparatory English)

Course Description

An introductory course in statistics based on elementary algebra. The emphasis is on applications rather than theory.

Course Outline

Week Topics Covered
1 Introduction (Population versus sample, Basic terms, Types of variables, Sources of data, Summation notation)
2 Organizing and Graphing Data (Organizing and graphing qualitative data, Organizing and graphing quantitative data, Cumulative frequency distribution, Steam and leaf displays, Dot plots)
3 Numerical Descriptive Measures ( Measures of central tendency for ungrouped data, Dispersion, Mean, Variance, Standard deviation)
4 Probability (Experiment, Outcomes, Sample Space, Calculating Probability, Counting Rule, Marginal and Conditional Probabilities)
5 Probability (Mutually Exclusive Events, Independent versus Dependent Events, Complementary Events, Multiplication Rule, Addition Rule)
6 Probability Distributions (Random variables, Probability distribution of a discrete random variable, Mean and standard deviation of a discrete random variable, Factorials, Combinations, Permutations, The Binomial probability distribution)
7 Continuous Random Variables and the Normal Distribution (Definitions, Standardizing a Normal distribution, Applications)
8 Estimation of the Mean and Proportion (Introduction, Point and interval estimates, Estimation of a population mean, Estimation of a population proportion)
9 Hypothesis Tests About the Mean and Proportion (Fundamentals of hypothesis testing, Testing a claim about a mean using large and small samples, Testing a claim about a proportion, Confidence intervals)
10 Inferences from Two Samples (Inferences about two means: dependent samples, independent and large samples, independent and small samples, inferences about two proportions)
11 Simple Linear Regression (Model, Analysis, Standard deviation of random errors, Coefficient of determination, Inferences about B, Linear correlation)
12 Simple Linear Regression (Regression Analysis: A complete example, Using the regression model, Cautions in using regression
13 Review
14 Final Exam


Assignments 8%
Lab Work 5%
Quizzes 12%
Midterm Exam 25%
Final Exam 50%


Introductory Statistics, Ninth Edition, Prem S. Mann


Please refer to the BC Transfer Guide


Hayri Ardal, B.Sc.(Bogazici), Ph.D.(Simon Fraser)
Kim Peu Chew, B.Sc. (Nanjing), M.A., Ph.D. (British Columbia)
Ana Culibrk, B.Sc.,M.Sc. (Belgrade),M.Sc.(British Columbia)
Sam Ekambaram, B.Sc., M.Sc. (Madras), M.Sc., Ph.D. (Simon Fraser)
Peter Hurthig, B.Sc., M.Sc. (British Columbia)
Arman Ahmadieh, B.A., M.Sc. (Sharif University of Technology)
Rika Dong, B.Sc. (Simon Fraser), M.Sc. (Regina)
Himadri Ganguli, B.Sc., M.Sc. (Chennai), Ph.D. (Simon Fraser)