1. Chapter 1 Stats Starts Here, Data
Objectives: be able to –
Identify the Who, What, Why, When, Where and How associated with data
Identify different types of data variables

2. Statistics: An Overview
Everyday experiences:
Gallup polls, newspaper articles, lotteries, CPI, unemployment data, your admittance to NCSU (predicted GPA)
Basic stock data College data
Increasing in importance; used in more and more ways in many disciplines
NY Times: Statistics Sports Analytics
NCSU Sports Analytics

3. Broad Definition Many disciplines can be summarized in a few words:
Economics is about … Money (and why it is good)
Psychology: Why we think what we think
Biology: Life
Anthropology: Who?
History: What, where, and when?
Philosophy: Why?
Engineering: How?
Accounting: How much?
Statistics is about … Variation
The discipline of Statistics deals with the efficient collection and the analysis of data to solve real-world problems in the presence of variability.

4. More Specifically … Q. What is Statistics? Q. What are statistics?
A. Statistics is a way of reasoning, along with a set of tools and methods, designed to help us understand the world.
A. statistics are quantities calculated from data.

5. 2 Broad Areas of Applications
Descriptive statistics
utilizes numerical and graphical methods to summarize data, look for patterns and trends, present information
Descriptive statistics
lack a measure of reliability

6. Second Area Inferential statistics
Uses data to make estimates, decisions, predictions or other generalizations about a larger data set or population
Inferential statistics have a measure of reliability
Opinion Polling

7. Common Situations that Require Statistics
An opinion poll wants to know what fraction of the public approves of the president’s performance in office.
Will a new package design increase sales enough to pay the cost of implementing the new design Tropicana Disaster
Gov’t economists release monthly reports about the nation’s economic activity
Baseball salary arbitration (one or the other)

8. Three Simple Steps to Doing Statistics Correctly
QTM1310/ Sharpe
Three Simple Steps to Doing
Statistics Correctly
Think first. Plan: state what you are trying to investigate. What are the variables?
Show. This step contains the mechanics of calculating statistics and making graphical displays; this is an important step, but not the only part of the process.
Tell. Report your findings. 8 8

9. Data: numbers with a context
Section 1.2 Data
Data: numbers with a context

10. Data: values and their context
815, 930, 750, 919
What can you do with these?
Find the sum? Find the average? Seems reasonable if these are, for example, SAT scores.
BUT these are telephone area codes! Adding and averaging make no sense.

11. Know the context of the data
Who: items included in the data
What: variable(s) measured on each item
Why: purpose for collecting the data
Where: location(s) where data collected
When: last week? 1 year ago? last decade?
How: internet survey? (worthless); data provided by gov’t agency? (useful)

12. The characteristics recorded about each item are called variables.
Section 1.3 Variables
The characteristics recorded about each item are called variables.

13. Variable Types Qualitative (Categorical) Variables
Data that categorizes
Ex. Male/female, Democrat/Republican, yes/no, Chevy/Buick/Pontiac/Oldsmobile, Awful/Fair/Good/Very Good/Excellent
1a) Nominal (categorical): categorizes only
Buick, Chevy, Pontiac
1b) Ordinal: categories can be ranked or ordered
taste test; order of finish in a race

14. Variable Types (cont.)
Wendy’s is developing a new hamburger. A panel of taste-testers evaluates the new item.
Categories: Excellent
Very Good
Good
Poor
Gag
Ordinal - there is a natural ranking

15. Variable Types (cont.)
Wendy’s is developing a new hamburger. A panel of taste-testers evaluates the new item.
Categories: Excellent = 5
Very Good = 4
Good = 3
Poor = 2
Gag = 1
Ordinal - there is a natural ranking

16. Variable Types (cont.) Quantitative data
Data that is measured on a numerical scale
Ex. height, GPA, income, temperature, SAT
2a) interval data
no meaningful zero point; difference between 2 values meaningful; cannot meaningfully multiply or divide
Ex. temperature, SAT

17. Variable Types (cont.)
Ex. (cont.) 60o F not twice as warm as 30o F; the difference between 32o and 30o same as difference between 83o and 810, 2 degrees in each case. (No meaningful “zero”; 0 degrees not the absence of all heat)
Ratio data
zero point meaningful; can multiply and divide
Ex. income, height, GPA, pulse rate; $200 is twice as much as $100; $0 is the absence of all money

18. We collect these data from 50 students. Which variable is categorical?
Eye color
Head circumference
Hours of homework last week
Number of TV sets in home

19. Registration and Records collects data on NCSU students
Registration and Records collects data on NCSU students. Which one of the following is quantitative?
Class ( freshman, sophomore, etc.)
Grade point average
Whether the student took an AP class
Whether the student has taken the SAT

20. End of Chapter 1