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.
- Introduction to Statistics
a) Nature of statistical data
b) Frequency distributions
c) Graphical presentations - Descriptive Statistics
a) Mean, median, mode, fractiles
b) Variance, standard deviation, range
c) Chebyshev's Theorem
d) The Empirical Rule
e) z-score - 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 - Sampling Distributions and Confidence Intervals
a) Methods and error in sampling
b) Central limit theorem
c) Confidence intervals
d) Determination of sample size - 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 - Analysis of Variance, Correlation and Regression
a) ANOVA
b) Correlation - Pearson and Spearman
c) Linear regression
d) Multiple regression - 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.