Chapter 1
Sections 1 & 2
Aug. 21, 2012
What is statistics?
 Conducting studies to collect, organize, summarize, analyze
and draw conclusions from data.
 Why study statistics?
 Pg. 3 Read numbers 1-3, briefly summarize with your peer
partner why study stats
Vocabulary…..
 Variables: a characteristic or attribute that can assume
different values
 Random variables: values are determined by chance
 Data: the values the variables assume
 Data set: collection of data values
 Data value (datum): each value in the data set
Two main areas:
 Descriptive statistics: describes a situation
 Inferential statistics: makes inferences from samples to
populations
 Population: all subjects that are being tested
 Sample: group of subjects selected from a population
Example:
Our class is a population. The brown haired students
are a sample of the population.
 Consider 17, 21, 44, and 76. Are those data?
 A Consumer Reports article on energy bars gave the brand
name, flavor, price, number of calories, and grams of protein
and fat.
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Who?
What?
When?
Where?
How?
Why?
Try it…..
 Pg. 5-6 Applying the concepts 1-1
 Read the paragraph on attendance and grades and answer the
questions using complete sentences.
Bellringer
 Read “The Worst Day for Weight Loss” on the top of p. 11.
Then answer the questions below.
1) What are the variables under study?
2) What are the data in the study?
3) Are descriptive, inferential, or both types of statistics used?
4) What is the population under study?
5) Was a sample collected? If so, from where?
6) What is the relationship between the variables?
Section 2
Variables and Types of data
Sort the data into at least 3 groups based
on the type of data. Be able to explain to
the class why you grouped the data that
way.
Types of Data
 Qualitative (categorical) variables: can be placed in distinct
categories (ex. gender, geographic location, race)
 Quantitative variables: numerical; can be ordered or ranked
(ex. Age, heights, weights)
 Discrete variables: countable variables (can be assigned values
0,1,2,3….) (ex. Students in a room, number of children)
 Continuous variables: infinite number of values (ex.
Temperature, time)
Levels
of
measurement
 These four rankings from lowest to highest are Nominal, Ordinal, Interval, and
Ratio; Or it’s easy to remember as NOIR.
 Nominal:
Code
 Ordinal:
Data is in name only. There is no ranking system.
ex. Names, Ethnicity, Eye Color, Political Parties, Zip
There is a ranking but no numbers are used and the
boundaries are unclear.
ex. Small, Large; Poor, Fair, Good
 Interval:
point.
 Ratio:
zero.
The values are numerical and therefore can be
ranked. There is no absolute zero as a starting
ex. IQ, Temperature, SAT scores, Time (clock)
The values are numerical and there is a starting point of
ex. Speed, Money, Time (duration), Age, Weight
 A plane technician wants to find out what the average weight of
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carry-on luggage is for a passenger on a plane. He decides to weigh
the luggage of each passenger in line at a counter. The weights are
25, 15,4, 14,13,and 8 pounds each.
What would be the population?
all the carry-on luggage for planes
What would be the Sample?
the luggage of the people in line.
What would be the variable?
The weight of the luggage
What would be the data?
25, 15, 4, 14, 13, and 8 in pounds
What kind of statistic would this be?
Inferential
What kind of variable are we measuring?
Quantitative
-continuous
 A report on the Boston Marathon listed each runner’s
gender, country, age, and time.
 Who?
 What?
 When?
 Where?
 How?
 Why?
 Variables?
 Qualitative?
Quantitative?
Applying the Concepts
Pg. 9
Read the information about transportation industry and
answer the questions using complete sentences.
Section 3
Four basic sampling techniques
 Random sampling: using chance methods or random
numbers
 Ex. Random number generator
 Systematic sampling: numbering each subject and then
selecting every kth person
 Ex. 200 people in the room, asking every 5th person
 Stratified sampling: Dividing population into groups
according to some characteristic that is important to the
study.
 Ex. Asking 5 freshman, sophomores, juniors, and seniors to rate
the cafeteria food as good, average, or poor.
Sampling cont.
 Cluster sampling: Divide the population into groups called
clusters and randomly selecting some of the clusters and
using all members of that cluster
 Ex.
• X
X
X
X
X
Summarized table
 Pg. 13
 Summarized table of sampling methods
Applying the concepts
 Pg. 13
 Read the paragraph. Brainstorm with your group the answers to
the questions. Be prepared to have a discussion with the class.
Class Activity!
 Come up with as many examples of the different types of
sampling as you can. Work with your peer partner. Make sure
to pay close attention and describe what your population is
and who your subjects are. Be ready to discuss them with the
class.
Bellringer
 Study the vocabulary terms from sections 1-3 in chapter 1.
 Time for a quiz!
How did you study
Notes 10 students
Book 4 students
Cards 4 students
Section 1.4
Observational and Experimental Studies
Observational Studies
 Researcher simply observes what is happening or what has
happened in the past and draws conclusions based on these
observations.
 Ex. A research study comparing the risk of developing lung
cancer, between smokers and non-smokers.
 There is no experiment
 Findings can be flawed due to health background, diet, and
exercise.
Experimental Studies
 One of the variables is manipulated to try and determine
how the manipulation influences other variables
 Independent variable (explanatory variable)- the one being
manipulated (changed)
 Dependent variable (outcome variable)- variable studied to see
if it has changed due to the change in the independent variable.
 Ex. Placing different bill amounts on the ground and concluding
which one is more likely to be picked up.
Problems with experimental studies
 Hawthorne effect: subject who know they are participating in
an experiment change behavior in ways that affect the study
 Confounding of variables: influences the dependent variable
but is not separated from the independent variable
 Ex. Exercise program testing the program results but not
controlling what the participates diet consists of
Time to think…..
Read starting at the last paragraph of p.14-16.
1) What are advantages and disadvantages of each type of study?
2) What is the Hawthorne Effect? Why do you think this
happens?
3) What is a confounding variable? How is this a problem in
experimental studies?
4) Why might two studies on the same subject yield conflicting
results?
Do #1-7 on p. 16
Section 1.5
Uses and Misuses of Statistics
Read “Suspect Samples” p. 17
 Complete the table for suspect samples
Type of Misuse
Suspect samples
Ambiguous averages
Changing the subject
Detached statistics
Implied connections
Misleading graphs
Faulty survey
questions
What does it
mean?
What are the
problems?
Read Ambiguous Averages and
Changing the Subject p. 17-18
Read
 Detached Statistics
 Implied Connections
Read
 Misleading graphs and Faulty Survey Questions
Collect some data…..
 What conclusions can we assume from the data collected
from the class?
 Is are assumption a use or misuse of statistics?