Course: MAT 150 – Statistics for Life Sciences
Prerequisites: LAC 022 or SAS 022 or UL4 mathematics placement
Textbook: OpenIntro Statistics, 4ed, D. Diez, M. Cetinkaya-Rundel, C. Barr, Creative Commons, freely available: OpenIntro Statistics
Supplementary Text: Introduction to Modern Statistics, M. Cetinkaya-Rundel, J. Hardin, Creative Commons, free web-based: Introduction to Modern Statistics
Name: Michael Arciero
Office: Decary 343
Email: marciero@une.edu
Office Hours: By appointment. Please let me know in advance via email that you plan to come.
In this course, students apply principles of research design and statistics to analyze and interpret data and draw conclusions about experimental situations relevant to the sciences. Topics include random sampling, graphic and numeric descriptive data analysis, the normal distribution, hypothesis testing, t-tests, analysis of variance, correlation, and regression. Students will use statistical software to examine data graphically and perform statistical analyses.
At the conclusion of this course, students will be able to:
Distinguish among different types of data.
Describe a variety of random sampling methods.
Summarize data verbally, numerically, symbolically, and/or graphically to effectively communicate the results of data analysis.
Perform inferential hypothesis testing, including t-tests, analysis of variance, correlation, and regression.
Recognize some sources of bias and limitations of statistical analysis and inferences.
Homework will be assigned weekly and completed on Brightspace.
There will be two exams.
Your grade in this course will be determined by the following criteria:
Homework | 60% |
Exams | 40% (20% each) |
Grade | Points Grade | Point Average (GPA) |
A | 94 – 100% | 4.00 |
A- | 90 – 93% | 3.75 |
B+ | 87 – 89% | 3.50 |
B | 84 – 86% | 3.00 |
B- | 80 – 83% | 2.75 |
C+ | 77 – 79% | 2.50 |
C | 74 – 76% | 2.00 |
C- | 70 – 73% | 1.75 |
D | 64 – 69% | 1.00 |
F | 00 – 63% | 0.00 |
Week of |
Text Section |
Topics |
---|---|---|
Jan 13 |
1.1 |
Welcome; Case studies, examples |
(one class) |
1.2 |
Data basics; Types of variables |
Jan 20 |
1.2 |
Data basics; Types of variables (cont) |
(one class) |
1.3 |
Sampling principles and strategies |
Jan 27 |
1.4 |
Experiments |
|
2.1 |
Examining numerical data |
Feb 3 |
2.2 |
Considering categorical data |
|
2.3 |
Case study |
Feb 10 |
3.1 |
Defining probability |
|
3.2 |
Conditional probability |
Feb 17 |
3.4 |
Random variables |
|
3.5 |
Continuous distributions |
Feb 24 |
4.1 |
Normal distribution |
|
4.3 |
Binomial distribution |
Mar 3 |
5.1 |
Point estimates and sampling variability |
|
5.2 |
Confidence intervals for a proportion |
Mar 10 |
|
Exam 1 |
|
5.3 |
Hypothesis testing for a proportion |
Mar 17 |
|
Spring Break |
Mar 24 |
6.1 |
Inference for a single proportion |
|
6.2 |
Difference of two proportions |
Mar 31 |
7.1 |
One-sample means with the t-distribution |
|
7.2 |
Paired data |
Apr 7 |
7.3 |
Difference of two means |
|
7.4 |
Power calculations for difference of two means* |
Apr 14 |
7.5 |
Comparing many means with ANOVA |
|
7.5 |
ANOVA |
Apr 21 |
8.1 |
Fitting a line, residuals, and correlation |
|
8.2 |
Least squares regression |
Apr 28 |
9.1 |
Linear Regression, cont |
May 5 |
|
Final Exam |
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