AMS 110, Probability and Statistics for Life Sciences
Catalog Description: A survey of probability theory and statistical techniques with applications to biological
and biomedical situations. Topics covered include Markov chain models; binomial, Poisson
normal, exponential and chi-square random variables; tests of hypotheses; confidence
intervals; t-tests; analysis of variance, regression and contingency tables. May not
be taken for credit in addition to
AMS 310 .
Prerequisites: AMS 151 or MAT 125 or 131 or 141
Antirequisite: May not be taken by students with credit for AMS 102 or AMS 310
Mandatory Course Materials for Fall 2019 and Winter 2020:
For information on how to order course materials for AMS 110, please see the attached: AMS 110
AMS 110 IS ALSO OFFERED DURING SUMMER SCHOOL. CHECK THE SUMMER SCHOOL BULLETIN FOR TIMES.
1. Descriptive Statistics – 3 class hours
2. Basic Concepts of Probability – 6 class hours
3. Discrete Probability Distributions – 7 class hours
4. Continuous Probability Distributions – 6 class hours
5. Statistical Estimation – 6 class hours
6. Hypothesis Testing – 7 class hours
7. Regression and Correlation Methods – 3 class hours
8. Mid-Term Test and Review – 4 class hours
Learning Outcomes for AMS 110, Probability and Statistics for the Life Sciences
(Most of the material in AMS 110 is similar to AMS 102 but covered in greater depth.)
1.) Describe and apply the process of statistical investigations from conception through
conclusion, with particular emphasis to life science applications. 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:
* 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,
* probability of an event, sample space, equi-probable outcomes;
* conditional probability and Bayes’ theorem;
* binomial distribution;
* normal distribution
* chi squared distribution.
4.) Demonstrate facility with, and a solid conceptual understanding of, the key tools
of statistical inference, including:
* estimation of intervals;
* testing hypotheses, including Type 1 and Type 2 errors.
5.) Perform important statistical procedures, such as:
* the chi square 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 biomedical 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.