SI 644

1
SI 644

• Introductory Statistics and Data Analysis

2
Welcome and Outline

• This is 644
• Im Paul Resnick
• Today
• Course Intro
• Course Logistics
• Chapter 1 topic intro

3
Course Objectives

• Characterize population data
• Draw conclusions and inferences
• Critique others claims
• Look for correlations while controlling for
  confounds

4
Statistical Tasks for Informationists

• Decide what to preserve
• Evaluate user interface alternatives
• Redesign services based on usage history
• Assess demand for products or services
• Estimate cost of service provision
• Evaluate program outcomes
• Evaluate product and policy effectiveness
• Interpret industry trend data
• Assess policy compliance
• Present government data to lay audiences
• Conduct academic research

5
Course Coverage

• Descriptive statistics
• Inferential Statistics
• Sampling distributions confidence intervals,
  hypothesis tests and p-values
• Estimating population mean
• Comparing population means
• Analysis of variance
• Univariate and multivariate OLS Regression
• Analysis of categorical data
• Data Collection
• Experimental design

6
Books

• McClave and Sincich, 9th edition
• Student solution manual (odd numbered problems)

7
Course Format

• Before class
• Read textbook
• Do assigned exercises, ungraded
• In class
• Socratic review of basics
• Present tricky points, supplemental material
• Work through examples

8
Grading

• Class prep and participation 10
• 3 problem sets 30
• Midterm (Oct. 20, 630-830PM) 20
• Final (Dec. 21, 4-6PM) 40

9
Math

• No calculus required
• Some references in passing for the benefit of
  those who know it
• Algebra required
• Some links to online resources in syllabus
• More mathematically rigorous approaches to this
  material are available on campus
• Statistics department
• Economics department

10
Software

• Everything we cover can be done with Excel
• Data analysis toolpak should be installed in DIAD
  Lab
• For real data analysis, many advantages to a
  statistical package
• I use (and love) stata

11
Study Groups
12
Study Groups

• Yes

13
Questions on Logistics?
14
Learning Objectives Chapter 1

• 1. Define Statistics
• 2. Describe the Uses of Statistics
• 3. Distinguish Descriptive Inferential
  Statistics
• Define Population, Sample, Parameter,
  Statistic
• Identify data types
• Identify data sources

15
What is Statistics?
16
What is Statistics?

• The practice (science?) of data analysis
• Summarizing data and drawing inferences about the
  larger population from which it was drawn

17
Statistical Methods
Statistical
Methods
Descriptive
Inferential
Statistics
Statistics
18
Descriptive Statistics

• 1. Involves
• Collecting Data
• Presenting Data
• Characterizing Data
• 2. Purpose
• Describe Data

50
25
0
Q1
Q2
Q3
Q4
?X 30.5 S2 113
19
Inferential Statistics

• 1. Involves
• Estimation
• Hypothesis Testing
• 2. Purpose
• Make Decisions Based on Population Characteristics

Population?
20
Key Terms

• 1. Population (Universe)
• All Items of Interest
• 2. Sample
• Portion of Population
• 3. Parameter
• Summary Measure about Population
• 4. Statistic
• Summary Measure about Sample

21
Key Terms

• 1. Population (Universe)
• All Items of Interest
• 2. Sample
• Portion of Population
• 3. Parameter
• Summary Measure about Population
• 4. Statistic
• Summary Measure about Sample

• P in Population Parameter
• S in Sample Statistic

22
Data Types

• Quantitative
• Discrete
• Continuous
• Qualitative
• Nominal (categorical)
• Ordinal (rank ordered categories)

23
Exercise 1.13

• Data types
• Bacteria count
• Occupations of shoppers
• Marital status
• Time (in months) since last auto maintenance

24
Exercise Data About Us

• Quantitative
• Discrete
• Continuous
• Qualitative
• Nominal (categorical)
• Ordinal (rank ordered categories)
• Fill out the index card with your data well
  use it Thursday

25
Data Sources

• Published source
• Designed experiment
• Survey
• Observational study
• Exercise data sources of these types youve
  encountered

26
Sampling

• Representative sample
• Same characteristics as the population
• Random sample
• Every subset of the population has an equal
  chance of being selected

27
Exercise 1.21
28
End of Chapter
Any blank slides that follow are blank
intentionally.