| Course ID: | STAT 3110. 3 hours. |
| Course Title: | Introduction to Statistics for Life Sciences |
Course
Description: | Applied approach to statistical investigation, focusing on real-
world decision making in the face of uncertainty. Introduction to
central limit theorem and sampling distributions from
probabilistic and simulations frameworks for inference, including
one- and two-sample inference for means, proportions, simple
regression, and categorical data. Consequences of Type I, Type II
errors. |
| Oasis Title: | Intro to Stat for Life Science |
| Duplicate Credit: | Not open to students with credit in STAT 3110E |
Semester Course
Offered: | Offered fall semester every year. |
| Grading System: | A-F (Traditional) |
|
| Course Objectives: | This course is intended for genetics and related science majors
who require a non-mathematical understanding of statistical
methods and their applications. Upon completion of the course,
students will be able to:
- distinguish among several statistical sampling methods
- describe a sample using appropriate graphical techniques
- describe a sample using appropriate numerical techniques
- identify the correct sampling distributions for the sample
statistic collected
- generate a simulated sampling distribution for the sample
statistic using statistical software or app
- identify the appropriate model assumptions for a given
inferential method
- recognize whether the model assumptions for a given
inferential method have been satisfied
- build interval estimates for the parameter of interest based
on the simulated sampling distribution (e.g., randomization,
permutation intervals)
- build a confidence interval for the parameter of interest
based on the theoretical sampling distribution
- interpret a confidence interval in the context of the
parameter of interest and the statistical question
- set up the null and alternative hypotheses for a given
statistical question
- define the parameters of a statistical question in context
- identify and calculate the test statistic required to
conduct the hypothesis test
- know when to check assumptions for equality of population
standard deviations and its impact on the standard error
- calculate the p-value based on the simulated sampling
distribution (e.g., randomization, permutation tests)
- calculate the p-value based on the theoretical sampling
distribution
- interpret a p-value in the context of the statistical
question
- draw a statistical conclusion in context, including
justification and a decision referring back to the hypotheses to
relate the results of a hypothesis test to that of an
equivalent confidence interval
- communicate the ramifications of Type I and Type II errors
for a given statistical question in context |
| Topical Outline: | The course will start with descriptive statistics and the
principles of sampling. The course will also cover confidence
intervals and hypothesis testing involving means and proportions
from one and two populations; calculation of type II errors,
power, and paired comparisons; simple linear regression;
introduction to categorical data. |
| Honor Code Reference: | Student taking this class will be expected to follow and adhere
to UGA's Honor Code which can be found at
https://ovpi.uga.edu/academic-honesty/academic-honesty-policy. |