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 8:30-9:30am ("the art of espresso" at mandeville)

lectures:

tuesday & thursday, 2:00pm-3:20pm (mande B-210)

 

teaching assistant (TA):

sai gullapally

office hours:

thursday 3:30-4:30pm ("the art of espresso" at mandeville)

section:

friday 3:00-3:50pm (wlh 2112)

 

 

 

instructional assistant (IA):

tony chan

office hours:

monday 2:00-3:00 (pete's coffee near the rock bear in warren)

section:

monday 1:00-1:50pm (wlh 2206)

 

instructional assistant (IA):

eric liu

office hours:

tuesday 3:30-4:30pm (at "perk's coffee", next to the bookstore)

section:

wednesday 9:00-9:50am (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 (one-sided) notes.

 

 

quizzes:

There will be five un-announced 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:85-100 B:70-84.99 C:55-69.99 D:40-54.99 F:0-39.99

 

 

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

z-test example1
z-test 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

 

t-test example

t table

t-test 2 indep. samples example

SS (sum of Squares)

 

W6

Th 2/16

Homework 5

Single-factor designs. Analysis of Variance (One Way, between-groups design) Analysis of Variance (One Way).
Effect size; Multiple comparisons

Ch. 16

 

F-table Part1 and F-table Part2

slides W6

 

W7

Tu 2/21

Analysis of Variance (One Way, between-groups 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

 

 

 

 

t-test 2 related samples example

ANOVA Rep. measures Example

ANOVA Rep. measures TA Sai's slides

slides W7

W8

Tu 2/28

 

Factorial designs. Two-Way Analysis of Variance. Interaction. Two-Way 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

Non-parametric tests: Chi-square

Ch. 19


W9

Th 3/9

Homework 7

Non-parametric test s: Tests for Ranked (Ordinal) data
Mann-Whitney U test

Wilcoxon T test

 


Ch. 20

 

Mann-Whitney U table (c. values)

Wilcoxon T table (c. values)

slides W9

 

 

 

W10

Tu 3/14

Overview and Review session

Chi-square test examples

 

 

 

W10

Th 3/16

Non-parametric tests:

Kruskal-Wallis 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 B-210)