Lecture Unit 1
Stats Starts Here
Objectives: be able to –
 Identify the Who, What, Why, When, Where
and How associated with data
 Identify different types of data variables
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
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.
More Specifically …
Q. What is Statistics?
A. Statistics is a way of reasoning, along with a set of
tools and methods, designed to help us understand the
world.
Q. What are statistics?
A. statistics are quantities calculated from data.
2 Broad Areas of Applications
1.
Descriptive statistics
utilizes numerical and graphical methods to
summarize data, look for patterns and
trends, present information
Descriptive statistics
lack a measure of reliability
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
Common Situations that
Require Statistics
An opinion poll wants to know what
fraction of the public approves of the
president’s performance in office.
2. Will a new package design increase sales
enough to pay the cost of implementing the
new design Tropicana Disaster
3. Gov’t economists release monthly reports
about the nation’s economic activity
 Large groups of people or things
 Time, cost, inconvenience
1.
Three Simple Steps to Doing
Statistics Correctly
Plan first. Know where you’re headed and why.
Do. The mechanics of calculating statistics and making
graphical displays are important, but the computations are
usually the least important part of the process.
Report what you’ve learned.
8
SECTION 1.2
Types of Data
Data: numbers with a context
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.

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)

Data Types
Qualitative Data
Data that categorizes
Ex. Male/female, Democrat/Republican,
yes/no, Chevy/Buick/Pontiac/Oldsmobile,
Awful/Fair/Good/Very Good/Excellent
1.
1a) Nominal (categorical): categorizes only
Buick, Chevy, Pontiac
1b) Ordinal: categories can be ranked or ordered
taste test; order of finish in a race
DataTypes (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

DataTypes (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

Data Type Dictates Statistical
Procedures
Quantitative data
Data that is measured on a numerical scale
Ex. height, GPA, income, temperature, SAT
1.
2a) interval data
no meaningful zero point; difference
between 2 values meaningful; cannot
meaningfully multiply or divide
Ex. temperature, SAT
DataTypes
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
Why Do We Care About the Type of Variable We Have?
The type of data we have dictates the statistical procedures (graphics,
summaries, inference techniques) that we can use.
Summaries of categorical data:
Proportions, counts, tables, bar charts
 Example: student opinion of quality of
NCSU campus food.
Excellent: 10%, Very Good: 12%
Good: 25%, Fair: 35%, Poor: 18%

OPINION PERCENT
Excellent
10%
Very Good
12%
Good
25%
Fair
35%
Poor
18%
Summaries of quantitative data:
Averages, medians, stand. dev., histograms
Example: maximum speed (mph) of 198
roller coasters from around the world.
average: 57.1,
median: 55.9,
standard deviation:
18.5 mph


We collect these data from 50 students.
Which variable is categorical?
A.
B.
C.
D.
Eye color
Head circumference
Hours of homework
last week
Number of TV sets
in home
0%
A.
0%
B.
0%
C.
0%
D.
Registration and Records collects data on
NCSU students. Which one of the
following is quantitative?
1. Class ( freshman, sophomore, etc.)
2. Grade point average
3. Whether the student took an AP class
4. Whether the student has taken the
SAT
0%
1
0%
2
0%
3
0%
4
End of Section 1.2