MTH 2450 - Business Statistics and Analytics

• Formulate events from statements in various applications
• Apply Venn diagrams to identify sets or compute probability of events
• Evaluate conditional probability and implement multiplication rule
• Apply Bayes’ theorem
• Evaluate measures of central tendency and dispersion of a set of observations
• Recognize and utilize binomial and normal distributions
• Discern joint distributions of random variables
• Differentiate dependent random variables from independent random variables
• Evaluate Pearson and Spearman’s rank correlation coefficients
• Visualize and identify outliers or any anomaly in data
• Provide point estimation for sample mean and sample variance, and interval estimation for population mean and population proportion
• Distinguish type I and type II errors and provide examples
• Perform hypothesis tests about one population mean, population proportion, and population variance
• Perform hypothesis tests about difference of two population means, population proportions, and population variances
• Perform one-way and two-way ANOVA
• Conduct multiple comparison tests
• Distinguish between linear relationship, nonlinear relationship, and no relationship for a sample of observations
• Compute and interpret sample correlation coefficient
• Construct simple linear regression and multiple linear regression from sample points
• Validate the fit of regressions and identify multicollinearity
• Perform Pearson’s chi-square test for goodness of fit, chi-square test for independence, Mann-Whitney test, and Kruskal-Wallis test for equivalence of populations
• Interpret reported statistics
• Analyze results from their own experiments

• Evaluation of algebraic formulas

• Multiplication rules and Bayes’ theorem
• Independence of events
• Discrete random variables and continuous random variables
• Probability distributions
• Measures of central tendency and dispersion
• One-sample hypothesis testing and statistical inference
• Two-sample hypothesis testing and statistical inference
• Analysis of variance
• Linear regressions
• Nonparametric statistics