Statistics for Data Analysis MATH 1060

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