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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

3 credits

Fall 2018 Semester (course materials not yet available):

Textbook:  "Probability and Statistics for Engineering and Science with Examples in R ( Second Edition) " by Hongshik Ahn, Cognella, Inc., 2018, ISBN: 978-1-5165-3110-3

 

Spring 2018  Semesters (still 1st edition with minor typo errors corrected):

Textbook:  "Probability and Statistics for Engineering and Science with Examples in R ( First Edition) " by Hongshik Ahn, Cognella, Inc., 2016, ISBN: 978-1-5165-1398-7

The textbook is now available for purchase in both print and digital formats through the student e-commerce store https://store.cognella.com/90786.html

Print Price:  $62.95 / Digital Price:  $56.95

If you experience any difficulties, please email   orders@cognella.com  or call   800.200.3908 ext. 503 .

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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If you choose to purchase one textbook format for no (either print or evgfeadk



If you choose to purchase one textbook format now (either print or eBook) and decide you'd like to purchase the other format during the term, you can login to your Student Store account and purchase an add-on at a discounted rate.

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.

If you experience any difficulties while ordering your text, please email   orders@cognella.com  or call   800.200.3908 ext. 503 .

Orders are typically processed within 24 hours and shipping time will depend on selected shipping method and day it is shipped (order are not shipped on Sundays or holidays).  Any difficulties with your order should be addressed directly with the publisher, Cognella Inc., by email at orders@cognella.com or call 1-800-200-3908, ext. 503.

 

AMS 310 IS ALSO OFFERED DURING SUMMER SCHOOL. CHECK THE SUMMER SCHOOL BULLETIN FOR TIMES.

Topics

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 distributions
        * 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 testing, to”
        * 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.

 

 

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