Download PAM 2100 - Cornell University

PAM 2100 – Introduction to Statistics – Spring 2017
Syllabus
Professor: Tom Evans
Email: [email protected]
Office: 2309 MVR
Telephone: 255-7010
Office hours: Monday 4:30-5; Tu Noon-3pm; W 4:30-5pm, Th 10-Noon
Teaching Assist: Christine Stephan(cs2292)–Office Hrs:Tu 11-12:30pm;Th 12:30-2pm..MVR 3M48
Teaching Assist: Ishneet Kaur (ik288) Office Hours: M 4:30-6pm, W 10-11:30am…MVR 3M48
Teaching Assist: Subrina Shen (xs255) Office Hours: M & W 1-2:30pm… Uris 334
Section 201:
Section 202:
Section 203:
Section 205:
Section 206:
Section 204:
Th 10:10 - 11:00
Th 11:15 - 12:05
Th 12:20 - 1:10
F 10:10 - 11:00
F 11:15 - 12:05
F 1:25 - 2:15
Overview
The course introduces students to standard methods of describing and analyzing data, probability
theory, statistical inference, and ordinary least squares. Students will learn to describe data with
summary tables and charts, understand and apply probability theory to data, understand sampling
distributions, conduct hypothesis tests, estimate regressions, and interpret statistical findings.
Students will also learn to use the basics of Excel to analyze data.
Learning Outcomes
1. Describe large datasets using summary statistics including both central tendencies and
spread.
2. Use probability theory to evaluate the expected value of future events.
3. Use inferential statistics tools to conduct hypothesis tests about proportions, means, and
multiple means.
4. Estimate basic regressions using both bivariate and multivariate ordinary least squares.
5. Use Excel to conduct basic statistical, data, and graphical analysis.
Course Website
All course information, announcements, data sets, and so on, will be made online via Blackboard
(Cornell’s system of academic websites). You should enroll in the Blackboard website “Introduction
to Statistics: Spring 2017.”
Course Readings
The textbook for this course is “Introduction to Statistics and Data Analysis,” 5th edition, by Peck,
Olsen and Devore.
We will be learning the basics of Excel. Fortunately the Cornell Library has an online textbook for
data analysis with Excel. The book is called “Excel 2010 for Physical Sciences Statistics.” You can
either search the Cornell Library catalog, or go to this link:
http://link.springer.com/book/10.1007%2F978-3-319-00630-7
There are many other Excel books available through the library in electronic form. You will receive
a CISER account which will give you access to a cloud version of Excel.
You can get access to Microsoft Office, and Excel, for free through this link:
http://www.it.cornell.edu/services/office365/apps/student.cfm
Grading Determination
The following weights will be applied to determine your grade:
iClicker Questions
10%
Homework Assignments
20%
TA Assignments
10%
Prelim Exam #1
15% or 0
Prelim Exam #2
15% or 0
Final Exam
30% or 45%
iClicker Questions
I will present a multiple choice question at the start of most lectures (see the following schedule).
The questions are to be answered with an electronic clicker that must be purchased and registered.
The question will be a straightforward multiple choice question from the prior lecture. You will
receive one point for simply attempting the clicker question, and an additional point if you get the
question right, for a total of 2 points per question. If you miss a class you will receive zero points. I
will drop your lowest 4 scores. There won’t be any make-up questions. If you are late to class and
miss the clicker question you get a grade of zero. If you forget your clicker you get a grade of zero.
If you lose or break your clicker, or the batteries run low, you get a grade of zero.
Homework Assignments
I will assign 5 standard homework assignments. First, the assignments will be representative of the
types of questions you can expect to get on the prelims and final exam. Additionally, I will use the
assignments to force you to use Excel to answer statistics and data analysis questions. I will drop
your lowest homework grade. The remaining assignments will be equally weighted and count 5%
each towards your final grade. There will be no make up assignments or extensions for assignments.
If you miss an assignment you will simply get a grade of zero and presumably that will be the grade
that is dropped.
TA Assignments
Due to the size of the class the format of the class will be predominately lecture. However, there are
six TA sections of approximately 25 students each. In the section you will be able to ask questions.
The TA will also go over practice problems and help you with learning Excel. Finally, during most
weeks there will be problems that you will need to complete during the course of the section.
Additionally, occasionally I will hand out short assignments to be completed during regular class
times. These problems will be graded. 10% of your grade will be based on the TA assignments or
in class assignments. I will drop your three lowest grades.
Exams
There will be two preliminary and one final exam. All exams will be closed book and closed notes (I
will provide a page of formulae). The preliminary exams will be held in class during regular class
time. The sum of the mid-terms and final will count for 60% of your final grade. If your final exam
grade is lower than both midterms, the final exam will count for 30% of your grade and the
midterms will count 15% each. However, if your final exam grade is greater than either one of your
preliminary exam grades, your final exam will count for 45% of your final grade and your lowest
preliminary exam will be dropped. As a result, there won’t be any make up prelim exams. If you
miss a prelim your score of zero will simply be dropped and the final exam will count for 45% of
your final grade.
Regrading Procedures
While we certainly aim to eliminate grading errors, we are only human. If you believe that there is
an error on one of your submissions you will have one week after it has been handed back to
petition for a regrade. I will have the graders come to the end of class and you can discuss any error
you think has been made. If you are not satisfied with the graders response, I will stay and you can
then appeal to me. There will be one specific time for each assignment; you will not be permitted to
appeal your grade on an assignment afterwards.
Increasing you grades though because “you need it’ is out of the questions so please only request
regrades when you think an honest mistake has been made on our part.
Office Hours/Getting Help
Due to the size of the class it will be impossible for all of you to make my office hours a productive
forum for you to ask questions. However, I will hold office hours for you if you need to discuss
personal issues or need to bring up problems with the class. The two TA’s will be holding weekly
office hours where you can get individual help on the course material, or if you need help with the
research project. You can go to either of the TA office hours that are most convenient to you.
Academic Integrity
Each student in the course is expected to abide by the Cornell University Code of Academic
Integrity: (see http://cuinfo.cornell.edu/Academic/AIC.html.) Any work submitted by a student in
this course for academic credit must be the student’s own work.
Disability Accommodations
If you have a disability that requires accommodation, especially additional time for exams, please
bring a copy of your approval letter to my attention as early as possible in the semester so that I can
make arrangements.
Outside Learning Resources
For students who are having difficulty in the class here are two suggested outside resources:
1) Cornell operates the Learning Strategies Center for statistics. Here is the link:
http://lsc.cornell.edu/Sidebars/statistics_lab.html
2) Online videos from Khan Academy: https://www.khanacademy.org/math/probability
Class Schedule:
W
Jan 25
First day of class
M
W
Jan 30
Feb 1
Practice iClicker
iC1
M
W
Feb 6
Feb 8
iC2
iC3
M
W
Feb 13
Feb 15
iC4
Homework 1
M
W
Feb 20
Feb 22
February Break…no class
iC5
M
W
Feb 27
Mar 1
iC6
iC7
M
W
Mar 6
Mar 8
iC8
Homework 2
M
W
Mar 13
Mar 15
Prelim 1
iC9
M
W
Mar 20
Mar 22
iC10
iC11
M
W
Mar 27
Mar 29
iC12
Homework 3
M
W
M
W
Apr 3
Apr 5
Apr 10
Apr 12
Spring Break…no class
Spring Break…no class
iC13
iC14
M
W
Apr 17
Apr 19
iC15
Homework 4
M
W
Apr 24
Apr 26
Prelim 2
iC16
M
W
May 1
May 3
iC17
iC18
M
W
May 8
May 10
Homework 5
Last Day of Classes
Final Exam: To be determined – at the University scheduled time and place.
Order of Topics
Chapter 1: Introduction
1.1 Why Study Statistics?
1.2 The Nature and Role of Variability
1.3 Statistics and the Data Analysis Process
1.4 Types of Data and Some Simple Graphical Displays
Chapter 3: Graphical Methods for Describing Data
3.1 Displaying Categorical Data: Comparative Bar Charts and Pie Charts
3.3 Displaying Numerical Data: Frequency Distributions and Histograms
3.4 Displaying Bivariate Numerical Data
3.5 Interpreting and Communicating the Results of Statistical Analyses
Chapter 4: Numerical Methods for Describing Data
4.1 Describing the Center of a Data Set
4.2 Describing Variability in a Data Set
4.3 Summarizing a Data Set: Boxplots
4.4 Interpreting Center and Variability: Chebyshev’s Rule, the Empirical Rule, and z Scores
4.5 Interpreting and Communicating the Results of Statistical Analyses
Percent and percentage point
Ratio vs fraction
Chapter 6: Probability
6.1 Chance Experiments and Events
6.2 Definition of Probability
6.3 Basic Properties of Probability
6.4 Conditional Probability
6.5 Independence
6.6 Some General Probability Rules
6.7 Estimating Probabilities Empirically Using Simulation
Bayes Theorem
Counting rules
Expected Value
Chapter 7: Random Variables and Probability Distributions
7.1 Random Variables
7.2 Probability Distributions for Discrete Random Variables
7.3 Probability Distributions for Continuous Random Variables
7.4 Mean and Standard Deviation of a Random Variable
7.5 Binomial and Geometric Distributions
7.6 Normal Distributions
7.8 Using the Normal Distribution to Approximate a Discrete Distribution
Expected value again
Chapter 8: Sampling Variability and Sampling Distributions
8.1 Statistics and Sampling Variability
8.2 The Sampling Distribution of a Sample Mean
8.3 The Sampling Distribution of a Sample Proportion
Chapter 2: Collecting Data Sensibly
2.1 Statistical Studies
2.2 Sampling
2.3 Simple Comparative Experiments
2.4 More on Experimental Design
Chapter 9: Estimation Using a Single Sample
9.1 Point Estimation
9.2 Large-Sample Confidence Interval for a Population Proportion
9.3 Confidence Interval for a Population Mean
9.4 Interpreting and Communicating the Results of Statistical Analyses
Chapter 10: Hypothesis Testing Using a Single Sample
10.1 Hypotheses and Test Procedures
10.2 Errors in Hypothesis Testing
10.3 Large-Sample Hypothesis Tests for a Population Proportion
10.4 Hypothesis Tests for a Population Mean
10.5 Power and Probability of Type II Error
10.6 Interpreting and Communicating the Results of Statistical Analyses
Chapter 11: Comparing Two Populations or Treatments
11.1Inferences Concerning the Difference between Two Population or Treatment Means Using
Independent Samples
11.2Inferences Concerning the Difference between Two Population or Treatment Means Using
Paired Samples
11.3Large-Sample Inferences Concerning the Difference between Two Population or Treatment
Proportions
11.4Interpreting and Communicating the Results of Statistical Analyses
Chapter 5: Summarizing Bivariate Data
5.1 Correlation
5.2 Linear Regression: Fitting a Line to Bivariate Data
5.3 Assessing the Fit of a Line
Chapter 13: Simple Linear Regression and Correlation: Inference
13.1 Simple Linear Regression Model
13.2 Inferences About the Slope of the Population Regression Line
13.3 Checking Model Adequacy
13.4 Inferences Based on the Estimated Regression Line (Optional)
13.5 Inferences About the Population Correlation Coefficient (Optional)
Chapter 14: Multiple Regression Analysis
14.1 Multiple Regression Models