Introduction to descriptive statistics, laws of probability, distributions of continuous and discrete random variables, inferential statistics, correlation and linear regression. This course rigorously develops statistical theory and is intended for those students who will continue on in applied disciplines or wish to pursue more statistics courses.
- Descriptive Statistics.
- Laws of Probability.
- Distributions of Continuous and Discrete Random Variables.
- Sampling Distributions and the Central Limit Theorem.
- Estimation and Hypothesis Testing.
- Regression and Correlation.
Methods of Instruction
Lectures, in-class assignments and tutorials.
Means of Assessment
Evaluation will be carried out in accordance with Douglas College policy. The instructor will present a written course outline with specific evaluation criteria at the beginning of the semester. Evaluation will be based on the following criteria:
Students who complete the course successfully will be able to discuss and solve problems involving the following topics:
- different data types
- graphical representation of data
- numerical measures of a data set’s central and dispersive characteristics
- a sample space and events
- basic probability rules
- conditional probability
- Bayes’ theorem
- general properties of discrete and continuous random variables and their distributions
- expected value, mean and variance for a random variable with a given distribution
- binomial, hypergeometric and Poisson distributions
- normal, gamma and exponential distributions
- jointly distributed random variables
- covariance and correlation
- distributions for sample means and linear combinations of independent identically distributed random variables
- central limit theorem
- estimation of a population mean, difference of means, variance, proportion or a difference of proportions based on sample data
- qualification of a claim regarding a mean, difference of means, variance, proportion or a difference of proportions based on sample data
- scatter plot of bivariate data
- linear regression model for bivariate data
- correlation coefficient of bivariate data
- the use of a significant amount of, and sophisticated level of, technology (such as R, Minitab, SPSS, etc.)
MATH 1220 (must be taken before or concurrently with MATH 2260)
Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.
Below shows how this course and its credits transfer within the BC transfer system.
A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.
For more information on transfer visit the BC Transfer Guide and BCCAT websites.
If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.