
introduction to statistical analysis 
cogs 14b (winter '17) 

department of cognitive science
university of california, san diego 


Slides used by TA Sai during Friday's Review Session for Midterm 1
Slides used by TA Sai during Monday's 2/27 Review Session for Midterm 2 

instructor: 
prof. rafael núñez 
office
hours: 
thursday 8:309:30am ("the art of espresso" at mandeville) 
lectures: 
tuesday & thursday,
2:00pm3:20pm (mande B210) 

teaching assistant (TA): 
sai gullapally 
office
hours: 
thursday 3:304:30pm ("the art of espresso" at mandeville) 
section: 
friday 3:003:50pm (wlh 2112) 



instructional assistant (IA): 
tony chan 
office
hours: 
monday 2:003:00 (pete's coffee near the rock bear in warren) 
section: 
monday 1:001:50pm (wlh 2206) 

instructional assistant (IA): 
eric liu 
office
hours: 
tuesday 3:304:30pm (at "perk's coffee", next to the bookstore) 
section: 
wednesday 9:009:50am (wlh 2209) 


undergrad. coordinator : 
thanh maxwell 




description: 
The goal of this course is to provide the fundamental tools for the understanding of statistical analysis as it is used in the behavioral sciences and in cognitive science in particular. Special emphasis will be put on conceptual comprehension and critical thinking. 
textbook: 
Statistics (9th/10th Edition),
by Robert S. Witte & John S. Witte. Wiley, 2009, 2015.
There is a limited availibility of this book (or an earlier version) in reserve. 



lectures

Attending lectures is crucial. Lectures will cover all the necessary conceptual and theoretical material as well as a broad range of topics not always treated in textbooks.

sections 
Sections are an essential part of this course. This is the only place where we practice the calculations that you will need to do on the midterms and final exam. Importantly, sections provide oportunities for discussing and further digest the course material. 


homework: 
There will be several homework assignments throughout the quarter. Homeworks are not graded, but are essential to learn the material. Some of the problems in the midterms will be similar to those in the homework. You do not have to hand in the homework. Often, the solution to the homework questions will be discussed in sections. 


tests: 
There will be two midterms and a final exam. Midterms problems will be very similar to problems or examples covered in lecture or in discussion sections. You will be allowed to have a single sheet (8x11, one side) of HANDWRITTEN notes in each midterm. For the final exam, you will be allowed to bring two sheets of paper with handwritten (onesided) notes.


quizzes: 
There will be five unannounced short (and relatively simple) quizzes covering essential class material (the last two classes for any given quiz). The quizzes will be about 5 minutes long, and will take place during lectures. The best four quizzes (you can drop one) will count for a combined 8% of your final grade. 


grading and scale : 
55% of your course grade will come from the midterms (27.5% each), 37% will come from the final exam, 8% from the quizzes. Grades are not "curved" and will be based on the following scale:
A:85100 B:7084.99 C:5569.99 D:4054.99 F:039.99 



extra credit: 
You have the possibiliy to earn extra credit in this class by participating in an experiment via Sona (credit: 1% of final grade per hour of participation, with a maximum of 2 hours). 


academic dishonesty: 
This course does not tolerate academic dishonesty and follows UCSD policy on this matter. For more information click here. 



tentative
schedule: 




Date 

contents 
Textbook chapters, slides and supporting material



I. Descriptive Statistics


W1
Tu
1/10


Presentation, syllabus, schedule.
organization.
Introduction.
Measures of Central Tendency. Measures of Variability

(Ch. 1 summarizes certain contents of COGS14A)
Ch. 3 and Ch. 4
Mean as "balance point" ("proof")
Derivation of st. deviation formulae
Notes on IQR
Sums of Squares (SS)

W1
Th
1/12

Homework 1 
Distributions, Normal Distribution (Basics), skewness
z scores (with applications)

Ch. 5
Table: Standard Normal Curve
slides W1 
W2
Tu 1/17

Homework 2 
Correlation

Ch. 6
Derivation of Correlation formulae

W2
Th
1/19

Homework 1 solutions

Regression

Ch. 7
Derivation of Regression Line
slides W2 


II. Inferential Statistics


W3
Tu 1/24

Homework 2 solutions 
Generalizing Beyond Data. Populations and Samples. Probability

Ch. 8
slides W3a 
W3
Th 1/26

Homework 3

Logic of Inferential statistics.
Sampling distribution of the mean. Central limit theorem.

Ch. 9
slides W3b

W4
Tu 1/31

Homework 3 solutions 
Hypothesis Testing. Decision Theory.
z test

Ch. 10
ztest example1
ztest example2
slides W4 
W4
Th 2/2


Type I and Type II Errors.
Power

Ch. 11
Review session Midterm 1, Fr 2/3 (by TA Sai) 
W5
Tu 2/7


Midterm 1


W5
Th 2/9

Homework 4 
Estimation (Confidence intervals).
t test for one sample

Ch. 12 and Ch. 13
slides W5

W6
Tu
2/14 

Basics of experimentation. Testing for a difference between means: t test for two independent samples

Ch. 14
ttest example
t table
ttest 2 indep. samples example
SS (sum of Squares)

W6
Th 2/16

Homework 5

Singlefactor designs. Analysis of Variance (One Way, betweengroups design) Analysis of Variance (One Way).
Effect size; Multiple comparisons

Ch. 16
Ftable Part1 and Ftable Part2
slides W6

W7
Tu
2/21


Analysis of Variance (One Way, betweengroups design, Continued) Effect size; Multiple comparisons

Critical values of q for Tukey's HSD

W7
Th
2/23

Homework 6

t test for two related samples (repeated measures)
Analysis of Variance (Repeated measures).

Ch. 15; Ch. 17
ttest 2 related samples example
ANOVA Rep. measures Example
ANOVA Rep. measures TA Sai's slides
slides W7 
W8
Tu 2/28


Factorial designs. TwoWay Analysis of Variance. Interaction. TwoWay Analysis of Variance.

Ch. 18
Review session Midterm 2, Mo 2/27 (by TA Sai)

W8
Th 3/2


Midterm 2

slides W8

W9
Tu
3/7


Nonparametric tests: Chisquare

Ch. 19

W9
Th
3/9

Homework 7

Nonparametric test
s:
Tests for Ranked (Ordinal) data
MannWhitney U test
Wilcoxon T test

Ch. 20
MannWhitney U table (c. values)
Wilcoxon T table (c. values)
slides W9

W10
Tu
3/14


Overview and Review session

Chisquare test examples

W10
Th
3/16


Nonparametric tests:
KruskalWallis H test
 0  0  0 
one more little parametric test:
t test for a population correlation coefficient

Ch. 20 (Cont.)
Overview of statistical tests
slides W10 
Th
3/23


Final Exam
3:00pm  5:59pm (mande B210) 








