MA 131

MA 131 - Statistics for Decision Making

This course will emphasize the use of computer software for the analysis of data and the performance of statistical tests.

  1. Introduction to Statistics
    a) Nature of statistical data
    b) Frequency distributions
    c) Graphical presentations

  2. Descriptive Statistics
    a) Mean, median, mode, fractiles
    b) Variance, standard deviation, range
    c) Chebyshev's Theorem
    d) The Empirical Rule
    e) z-score

  3. Probability
    a) Concepts and rules
    b) Law of Large Numbers
    c) Permutations and combinations
    d) Discrete and continuous random variables
    e) Binomial and normal probability distributions

  4. Sampling Distributions and Confidence Intervals
    a) Methods and error in sampling
    b) Central limit theorem
    c) Confidence intervals
    d) Determination of sample size

  5. Hypothesis Testing
    a) Large and small samples
    b) Critical value and p-value methods
    c) Tests for means and proportions
    d) Type I and Type II error

  6. Analysis of Variance, Correlation and Regression
    a) ANOVA
    b) Correlation - Pearson and Spearman
    c) Linear regression
    d )Multiple regression

  7. Chi-Square Distribution and Nonparametric Methods
    a) Goodness-of-fit tests
    b) Contingency table analysis
    c) Mann-Whitney U, Wilcoxon and Kruskal-Wallis tests


Learning Outcome 1: The student will demonstrate an understanding of different ways to collect, organize and describe data sets.


Learning Outcome 2: The student will demonstrate an understanding of the basic concepts of probability and be able to apply them to study probability distributions.

Learning Outcome 3: The student will be able to use sample statistics to make inferences about a population parameter.