AMS 310, Survey of Probability and Statistics
Catalog Description: A survey of data analysis, probability theory, and statistics. Stem and leaf displays,
box plots, schematic plots, fitting straight line relationships, discrete and continuous
probability distributions, conditional distributions, binomial distribution, normal
and t distributions, confidence intervals, and significance tests. May not be taken
for credit in addition to ECO 320.
Prerequisite: AMS 161 or MAT 126, 132, 142
Fall 2018 Semester:
Textbook (Paperback): "Probability and Statistics for Engineering and Science with Examples in R ( Second Edition) " by Hongshik Ahn, Cognella, Inc., 2019, ISBN: 978-1-5165-3110-3
The textbook is available for purchase in both paperback print and digital formats through the student e-commerce store https://store.cognella.com/90884
Additionally, the publisher has a binder-ready version: ISBN: 978-1-5165-3111-0. You may purchase the binder-ready format via the link: https://store.cognella.com/81544-2A-BR-001
Print Price: $69.95 / Digital and Binder Ready Price: $62.95
The text includes course material we will reference and use in class regularly, so you should purchase your own copy. Please keep in mind our institution adheres to copyright law. Course materials should never be copied or duplicated in any manner.
AMS 310 IS ALSO OFFERED DURING SUMMER SCHOOL. CHECK THE SUMMER SCHOOL BULLETIN FOR TIMES.
1. Descriptive Statistics (Chapter 1) -- 4 class hours
2. Probability (Chapter 2) -- 5 class hours
3. Discrete Distributions (Chapter 3) -- 7 class hours
4. Continuous Distributions (Chapter 4) -- 6 class hours
5. Multiple Random Variables (Chapter 5) -- 3 class hours
6. Sampling Distributions (Chapter 6) -- 2 class hours
7. Point Estimation and Testing, Introduction (Chapter 7) -- 2 class hours
8. Inferences Based on One Sample (Chapter 8) -- 4 class hours
9. Inferences Based on Two Samples (Chapter 9) -- 2 class hours
10. Examinations and Review -- 7 class hours
Learning Outcomes for AMS 310, Survey of Probability and Statistics
1.) Learn and apply descriptive statistical tools in data analysis
* distinguish between different types of data;
* use of graphical tools to summarize a given data set;
* use of numerical methods to summarize a data set.
* identify the best method to highlight the interesting features in a data set.
2.) Demonstrate and apply an understanding of the basic concepts in probability theory
* describe the sample space and particular outcomes for some random experiments.
* use the basic counting techniques to calculate the number of experimental outcomes.
* calculate probabilities of simple events by working with sets that represents them.
* apply the axioms of probability to calculate probabilities of compound events.
* demonstrate an understanding of the differences between various concepts such as disjoint and independence.
* compute conditional probabilities.
* use the law of total probability and Bayes’ rule to calculate probability of complex events.
3.) Demonstrate an understanding of the basic concepts in random variables and their
* use random variables to model the outcomes of simple experiments.
* describe the properties of probability mass function and cumulative distribution functions.
* calculate the means and variances of discrete random variables.
* learn and apply commonly used discrete distributions such as binomial, geometric, Poisson, and hypergeometric distributions.
* contrast discrete and continuous random variables.
* describe the properties of continuous density functions and their cumulative distribution functions.
* calculate the means and variances of continuous random variables.
* learn and apply commonly used density functions such as exponential and normal densities.
* learn and apply the general properties of the expectation and variance operators.
* demonstrate an understanding of the connections and differences between different distribution functions, e.g., normal approximation to binomial, Poisson approximation to binomial, and the difference between binomial and hypergeometric distributions.
4.) Use the sampling distribution of a statistic, in particular, the sample mean to:
* tell the difference between a sample and a population
* identify the similarities and differences between the normal distribution and the t-distribution.
* understand and apply the basic concepts in estimation theory such as estimators, bias, variance, and efficiency.
* construct point estimators (using strong law of large numbers) and interval estimators (in particular, confidence intervals) for estimating the mean of a population.
* understand and apply confidence intervals.
* apply the central limit theorem in solving probability questions involving averages from arbitrary distributions.
5.) Use the basic concepts and ideas in inferential statistics, such as hypothesis
* identify the basic components in a classical hypothesis test, including parameters of interest, the null and alternative hypothesis, the rejection region, and test statistics.
* formulate a given problem as a hypothesis testing problem.
* calculate the p-value of a test statistic.
* conduct the inference for the mean of a population when the underlying variance is either known or unknown.
* explain the two types of errors and calculate their associated probabilities.