Uploaded by Marcus Counts


Section 18 is a special section of MATH1530 but in the transcript it appears as MATH1530 same as for any other section.
Its main characteristic is that the concepts of statistical inference are introduced early in the semester instead of only at
the end, so that these concepts are seen over and over several times during the semester. It is especially recommended
for students majoring in Biology or in health related disciplines including pre-professional programs.
Section 088 is the section for the students in the honors program.
Course objective: To develop a basic understanding of probability and statistics and their application to different fields,
especially the health sciences and biology. At the end of the course students will be able to perform basic statistical
analysis using software.
Class schedule: T/R 11:15 am - 12:35 pm
Tuesdays: Gilbreath Hall 304 (classroom), Thursdays: Gilbreath Hall 305/6 (computer lab)
Instructor: Edith Seier, Ph.D. Office: Gilbreath 308B email: [email protected], phone 439-5812, webpage:
http://faculty.etsu.edu/seier )
Office hours of the instructor: Tuesday, Wednesday & Thursday 2-3 or by appointment.
Prerequisite: Two years of high school algebra
Textbook: Seier, E. and Joplin, K.H. (2011) Introduction to statistics- in a biological context. ISBN-10:
1463613377 (available from amazon.com).
Data and other material will be made available in D2L and from the web page
Software: Students DO NOT need to purchase any software. Minitab is available in all the ETSU labs and from home
through CITRIX http://xen.etsu.edu (instructions on how to install Citrix at home are available from D2L) . Students can
use mainly Minitab if they so prefer. Students will be exposed to the use of R. R is also available in all ETSU labs and
from Citrix. However, R is a free software that students can install at home for free from http://www.r-project.org if they
wish. Students do not need to write programs in R; they will be given commands to copy and paste to use with their data.
There are a couple of things such as randomization tests and bootstrapping that need to be done with R because Minitab
does not perform them.
Calculator: No special calculator is required, just bring any simple calculator that you already have.
Teaching method: The teaching method is a mixture of lectures using power-point (available from D2L) and active
learning through work in class and assignments to be worked with the computer. Interpretations are emphasized.
Quizzes are administered frequently in order to help the students to keep a good study pace.
Recommended strategy to succeed in the course:
 Come to class (please avoid doing email or texting during class)
 Study regularly (see recommendations below)
 Ask questions in class, by email, and during your instructor’s office hours. If you need extra help visit the Center
for Academic Achievement in the first floor of the Library.
 Complete your data analysis project
 We recommend that you separate 3 fixed slots of time during the week outside class to study:
Read the material in the book and review the power points
Go over the ‘Review Questions’ at the end of each chapter, they are intended to check the student’s
understanding of the material
Practice with the online quizzes
Work in any pending homework/lab or in practicing with additional exercises.
Study for the quizzes or exams.
Use the past quiz or exam to identify the topics you might have trouble with.
Course content: Basic definitions, descriptive statistics, inference by randomization. Probability basics, the binomial
distribution, using the binomial distribution to test hypotheses. Conditional probability in the context of medical diagnosis.
Normal and Chi-square distributions, chi-square tests of goodness of fit, independence and homogeneity, tests for
normality. Sampling distributions, confidence intervals and hypothesis testing for the population means and proportions.
Introduction to linear regression.
List of topics to cover:
1. Definitions: Hypotheses, variables, parameters and statistics (Ch. 1- sections 1.1-1.4).
2. Producing data using surveys and experiments; preparing data files (Ch. 1- sections 1.5-1.8).
3. Graphs and statistics for one quantitative variable (Ch. 2, Sec. 2.1-2.11).
4. Graphs and statistics for two or more quantitative variables: Scatter plots and correlation (Ch. 2, Sec. 2.12-2.13).
5. Graphs and statistics for categorical variables (Ch. 2, Sec. 2.14-2.15).
6. Randomization test (Ch. 3, Sec. 3.1).
7. Bootstrapping to build confidence intervals (Ch. 3, Sec. 3.2).
8. Introduction to probability and the binomial distribution (Ch. 4).
9. Testing hypotheses with the binomial distribution (Ch. 5).
10. Conditional probability and Bayes Rule using probability trees (Ch. 6, Sec. 6.1-6.4.1).
11. Normal and Chi-square distributions (Ch. 9, Sec. 9.1,9.3, 9.5).
12. Checking models and assumptions: Chi-square tests and test for normality (Ch. 10).
13. Sampling distributions and confidence intervals for the mean (Ch. 11, Sec. 11.1-11.2).
14. Testing hypotheses for means (t-tests) (Ch. 11, Sec. 11.3, 11.5).
15. Large sample inference for proportions (Ch. 11, Sec 11.4.1-11.4.4).
16. Introduction to linear regression (Ch. 12, sections 12.1-12.6).
There will be 10 in-class quizzes in order to keep a good pace of study and help getting ready for the exams. Practice
quizzes will be available from D2L to get ready for the in-class quizzes. There will be two partial exams and one
comprehensive final. There will be 4 labs to practice the application of statistical methods (3 of them require the use of
computers). There is also a final data analysis project using software. The assignment of points is the same in all the
MATH 1530 sections: 200 for final, 100 for final project, 50 for the online quizzes, and 650 points from the partial
Exam 1 (Topics 1-10A)
Exam 2 (Topics 10A-16)
Quizzes (20 points each)
Labs(4) *
Final exam (comprehensive:
Topics 1-16)
Final data analysis project
Online practice quizzes**
+ bonus points for attendance (60 max)
Tentative dates
Thursday, October 11
Tuesday, November 20
(see attached calendar)
(see attached calendar)
Tuesday, December 11, 8:00-10:00
Due the last day of class Thursday, Dec 6 at 11:15am
(open until day of the final December 11, at 7:00 am)
*Labs have to be turned in on time to receive credit. Labs and the final data analysis project will be uploaded to the
dropbox of D2L, no paper copy is required.
**You receive the 5 points for each practice quiz if you successfully complete 75% of the answers, you can take the
practice quizzes as many times as you want
*** The total number of points + bonus points for attendance is divided by 10 to get the final score in the course.
TUESDAY (classroom: Gilbreath 304)
THURSDAY (computer Lab: Gilbreath 305/306)
8/28 Read Chapter 1
8/30& Read Chapter 1
Topic 1 : Chap1-definitions,hypotheses
Topic 2 : Chap1-surveys & experiments
Discuss exercises2 &4 in Chapter 1
9/4 Read Sections 2.1-2.11
9/6 Read Sections 2.1-2.11
Finish Topic 2
Topic 3 (continuation) Learning how to produce plots and to calculate
Topic 3: Graphs and summaries for one quantitative
statistics with the computer
An introduction to Minitab and R
Quiz # 1 on Topic 1
9/11 Read Sections 2.12-2.13
9/13 Read Sections 2.14-2.15
Topic 4: Scatterplots, correlation
Topic 4 (scatter plots and correlation with statistical software)
Quiz # 2 on topic 2
Topic 5: Graphs and summaries for categorical variables
Assign Lab on Chapter 2
9/18 Read Section 3.1
9/20Read Section 3.2
Topic 6 Randomization test
Topic 7 The Bootstrap method
Quiz #3 on topic 3.
Practicing Randomization tests and bootstrap with the computer.
Quiz # 4 on topics 4 & 5
9/25 Read Chapter 4
9/27 Read Chapter 4
Topic 8 : Probability and Binomial distribution
Topic 8: Probability and Binomial distribution
Quiz # 5 on topics 6 and 7
Lab 1 on Chapter 2 is due
10/2Read Chapter 5
10/4 Read Chapter 5
Topic 9 Testing hypothesis with binomial
Topic 9 Testing hypothesis with binomial & concept of power.
Quiz # 6 on topic 8
10/9 Read Chapter 6
Quiz # 7 on Topics 9
EXAM 1 on Topics 1-10A
Topic 10 A -Conditional probability from 2-way tables
and formula
10/18 Topic 10B Conditional Probability: Bayes rule using probability
trees. Assign Lab 2 on topic 10
10/23 Read Sections 9.1-9.3
10/25 Read sections 9.3 & 9.5
Topic 11: Normal distribution
Topic 11: Normal and Chi-square distributions
Quiz # 8 on Topic 10 (A & B)
Lab 2 is due
10/30 Read Chapter 10
11/1 Read Chapter 10
Topic 12: Chisquare tests (goodness of fit,
Topic 12: finish Chisquare tests & normality tests
independence , homogeneity)
Assign Lab 3 on topic 12
Quiz #9 on Topic 11
11/6 Read Sections 11.1-11.2
11/8 Read Sections 11.1-11.2
Topic 13: Sampling distributions, confidence interval for Finish Topic 13 Lab 3 is due
the mean, sample size
Start Topic 14: Testing hypothesis for means
11/13Read sections 11.3-11.5
11/15 Read sections 11.3-11.5
Topic 14: testing hypothesis for means
Topic 14: Testing hypothesis for means
Quiz #10 on Topic 13
Assign Lab 4 on topic 14
EXAM 2 (on Topics 10A-14)
11/27 Topic 15. Large sample inference for proportions 11/29 Read sections 11.4.1-11.4.4
Read sections 11.4.1-11.4.4
Finish Topic 15 Lab 4 is due
12/4 Read sections 12.1-12.6
12/6 Read sections 12.1-12.6
Topic 16: Introduction to linear regression
Final exam (on topics 1-16) TUESDAY, DECEMBER 11 , 8:00-10:00
Information for ALL the sections of MATH 1530:
Related flashcards
Probability & Statistics

15 Cards MPremium

Create flashcards