This hands-on course introduces descriptive statistics, basic inferential statistics, linear regression, and probability concepts and calculations. Emphasis throughout the course will be placed on using statistical methods for the exploration and analysis of data sets. This introduction will enable students to use statistics for data analysis, will prepare them for “Data Analytics/Mining,” and covers topics appropriate for anyone seeking a first statistics course. Labs and exercises employ standard graphical methods to represent statistical data. Hypothesis tests, including ANOVA, are used to test for significant differences between multiple groups. Students will be introduced to the open source R Programming language, a statistical analysis tool used to extract meaningful information from a variety of scientific, industrial and commercial data sets. Upon successful completion, students will be able to carry out calculations, perform statistical decision making and solve problems with involving collected data. This course prepares the student to move on to MATH 3060 and is a required course for the Applied Data Analytics Certificate offered by BCIT Computing.
Refreshing your math skills prior to starting MATH 1060 is strongly recommended and we suggest taking MATH 0060. No class on Monday, February 18th (Family Day).
One seat remaining as of Nov 18, 2018 7:42 pm PST. Seats remaining may change at any time prior to registration and payment.
Upon successful completion of this course, the student will be able to:
Compute and understand the meaning of mean, median, range, standard deviation, variance, percentiles and standard scores.
Use and understand standard graphical methods of representing statistical data.
Use R to process and analyse data.
Perform basic probability calculations.
Solve problems with statistical variables that have a binomial, Poisson, normal or other probability distributions.
Describe the basic procedures for null hypothesis testing; define, explain, illustrate basic concepts such as null and alternate hypotheses, type 1 and 2 errors, level of significance, critical values, test statistic, rejection region, one-tailed and two-tailed tests; power; determination of sample size.
Carry out calculations and statistical tests related to sample means, sample proportions, chi-square distribution, and correlation and regression.
Use ANOVA to test hypotheses on differences between multiple groups.
Estimate with confidence intervals for population parameters (means, difference between means, difference between proportions).
Apply principles of linear correlations and regression and determine correlation coefficients between different data variables.
Use multiple regression to predict a response variable and determine the most significant predictor variables.
Effective as of Spring/Summer 2016
MATH 1060 is offered as a part of the following programs:
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