### AMS 102, Elements of Statistics

*Catalog Description*: The use and misuse of statistics in real-life situations. Basic statistical measures
of central tendency and dispersion; frequency distributions; elements of probability,
binomial and normal distributions; small- and large-sample hypothesis testing, confidence
intervals, and chi-square test; and regression. (May not be taken by students with
credit for AMS
110 , 110,
310 , or
311 ; ECO 320; PSY 201 or SOC 311,312.).

*Prerequisite*: Satisfaction of Entry Skill in Mathematics.

3 credits

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Coursework found on Pearson Publishing Storefront via MyPearsonStore.com (recommended
for students who DO NOT use a voucher):
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Students can go directly to the URL at: http://www.mypearsonstore.com/stores/1323774173 (includes bundled package: Loose leaf text and 6-month access card)

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Coursework found on the Stony Brook Amazon Site (for students who DO use a voucher):
*

Listed on Amazon as "Elementary Statistics Using Excel Package for Stony Brook University"; ISBN: 9781323774175

(This textbook is not available as "used" and may only be purchased as "new" as it is specifically customized for Stony Brook University students.)

MathXL will be used for online homework in this course.

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For students who do not want a physical textbook, you may purchase 6-month access
to MathXL directly at
www.MathXL.com. Y
*

**our instructor will provide a MathXL Course ID during your first day of class.**

**Topics**

1. Introduction and Concept of Randomized Experiments – 5 class hours

2. Elements of Probability Theory - 6 hours

3. Tests of Hypothesis - 4 class hours

4. Conditional Probability and its Applications - 3 class hours

5. Confidence Intervals - 4 class hours

6. Student's t test - 3 class hours

7. Cross-Tabulation Analysis - 5 class hours

8. Linear Regression - 6 class hours

9. Examinations and Review – 6 class hours.

**Learning Outcomes for AMS 102, Elements of Statistics**

1.) Describe and apply the process of statistical investigations from conception through
conclusion. This process involves:

* formulating questions and collecting data;

* analyzing data and drawing inferences;

* interpreting results and communicating conclusions.

2.) Demonstrate facility with, and a solid conceptual understanding of, the key tools
of data analysis, including:

* histograms;

* box plots, stem-and-leaf plots and other graphical displays;

* measures of central tendency;

* measures of dispersion.

3.) Demonstrate knowledge of elements of probability and key probability distributions,
including:

* probability of an event, sample space, equi-probable outcomes;

* conditional probability and Bayes’ theorem;

* binomial distribution;

* normal distribution.

4.) Demonstrate facility with, and a solid conceptual understanding of, the key tools
of statistical inference, including:

* z-scores;

* estimation of intervals;

* testing hypotheses, including Type 1 and Type 2 errors.

5.) Perform important statistical procedures, such as:

* t-test;

* linear regression.

6.) Work with technology to:

* analyze data graphically;

* analyze data numerically;

* analyze data inferentially.

7.) Decide which statistical methods to use in which situations:

* recognizing which statistics tests apply in a situation;

* checking the necessary conditions for those methods to be valid.

8.) Use statistics to address the research question at hand.

* interpret the results of statistical analyses to answer the research question;

* communicate conclusions that follow from the statistical analyses of the
question.

9.) Demonstrate an appreciation of the power and scope of statistical thinking for addressing research questions in a variety of scientific disciplines and in everyday life.