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Chapter 1
Getting Started
Understanding Basic Statistics Fifth Edition
By Brase and Brase Prepared by Jon Booze
© Cengage Learning. All rights reserved. 1 | 2
What is Statistics?
• Many definitions could apply. A few ideas about the subject:
- “The science of studying variation.”- “Description of and inference from data.”- The art of making decisions from data in
the face of error.”
What should be clear is that it’s all about data, analysis, and explaining and understanding variation.
© Cengage Learning. All rights reserved. 1 | 3
What is Statistics?
• Collecting data• Organizing data• Analyzing data• Interpreting data
• Statistician’s Creed: “In God we trust….all others, bring data!”
© Cengage Learning. All rights reserved. 1 | 4
Individuals and Variables
• Individuals are people or objects included in the study. (Also known as the “experimental units” or “subjects.”)
• Variables are characteristics of the individual to be measured or observed. They change from individual to individual…or over time.)
© Cengage Learning. All rights reserved. 1 | 5
Variables
• Qualitative Variable – The variable describes an individual through grouping or categorization.
(e.g. hair color, religion, college major, birth city, etc.)
• Quantitative Variable – The variable is numerical, so operations such as adding and averaging make sense.(e.g. weight, height, temperature of liquid, length of time, etc.)
© Cengage Learning. All rights reserved. 1 | 6
Data
• Population Data – The data are from every individual of interest.
• Sample Data – The data are from only some of the individuals of interest. That is, a sample is a subset of our population.
© Cengage Learning. All rights reserved. 1 | 7
DataWhich of the following Venn diagrams shows the
relationship between population data and sample data?
a). b).
c). d).
S P
S
P S
P
P
S
© Cengage Learning. All rights reserved. 1 | 8
Two VERY Important Terms
• Parameter – is a numerical measure that describes an aspect of a population
• Statistic – is a function of data from a sample.
• Remember the Mnemonic Device….
“P”: PARAMETER corresponds to POPULATION
“S”: STATISTIC corresponds to SAMPLE
© Cengage Learning. All rights reserved. 1 | 9
Two VERY Important Terms
Just a quick note: With one exception that we will get later, we typically represent:
• PARAMETERS – using lowercase Greek characters (e.g. μ, σ, ρ, etc.)
• STATISTICS – using standard Arabic characters (e.g. s, s2, r, etc.)
© Cengage Learning. All rights reserved. 1 | 10
Levels of Measurement
• Nominal Level – The data consists of names, labels, or categories.
• Ordinal Level – The data can be ordered, but the differences between data values are meaningless.
© Cengage Learning. All rights reserved. 1 | 11
Levels of Measurement
• Interval Level – The data can be ordered and the differences between data values are meaningful.
• Ratio Level – The data can be ordered, differences and ratios are meaningful, and there is a meaningful zero value.
NOIR: Nominal Ordinal Interval Ratio
© Cengage Learning. All rights reserved. 1 | 12
Levels of Measurement
Classify on our “NOIR” scale:1. Age of a person.2. Distance travelled from home to work.3. Grades recorded on an A,B,C,D,F scale.4. Undergraduate majors’ fields of study at EUP.5. Temperature of a liquid in degrees Fahrenheit. 6. Religious Affiliation of voters. 7. Rating of oral presentations on a 1,2,3,...,9
scale.8. Number of children in a family.
© Cengage Learning. All rights reserved. 1 | 13
Levels of Measurement
Classify on our “NOIR” scale:9. Response to a question on a Likert Scale
(Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree).
10. Whether a person in a study is in the control group or the experimental group.
© Cengage Learning. All rights reserved. 1 | 14
Levels of Measurement
Solutions:
1. Ratio2. Ratio3. Ordinal4. Nominal5. Interval6. Nominal7. Ordinal
8. Ratio9. Ordinal10. Nominal
© Cengage Learning. All rights reserved. 1 | 15
Two Approaches to Statistics
• Descriptive Statistics: Organizing, summarizing, and graphing information from samples.
• Inferential Statistics: Using information from a sample to draw conclusions about a population.
© Cengage Learning. All rights reserved. 1 | 16
Sampling Techniques• Simple Random Sampling, Sample size = n
– Each member of the population has an equal chance of being selected.
– Each sample of size n has an equal chance of being selected.
• Stratified sampling Population
Subgroup 4
Subgroup 1Subgroup 2Subgroup 3
sample
© Cengage Learning. All rights reserved. 1 | 17
Sampling Techniques • Cluster sampling
– Population is naturally divided into pre-existing segments.
– Make a random selection of clusters, then select all members of each cluster.
• Systematic sampling– Number every member of the population.– Select every kth member.
• Convenience sampling - Collect sample data from a readily-available population database.
© Cengage Learning. All rights reserved. 1 | 18
Sampling Techniques Which of the five sampling designs is being
employed? (Simple Random, Stratified, Cluster, Systematic, or Convenience?)
1. A politician wants to survey his constituents. To do so, he polls 100 democrats in his district, 125 republicans, and 20 independents.
2. Wal-Mart would like to perform demographic analysis of its shoppers. So starting at 8:00am, the Wal-Mart greeter is asked to survey every 20th customer who enters the store.
© Cengage Learning. All rights reserved. 1 | 19
Sampling Techniques 3. A doctor is assessing a new treatment
technique. She utilizes this treatment on all asthma patients she sees for a one month period.
4. A company wishes to survey its employees. There are 800 employees, an a random number is assigned to each. Fifty employees are selected using a random number table.
5. Residence life wants to examine student interests. Of the seven dorms, three dorms are randomly selected. Every student in the dorms selected is surveyed.
© Cengage Learning. All rights reserved. 1 | 20
Sampling Techniques Solutions to Sampling Technique Problems.
1. Stratified2. Systematic (1-in-k)3. Convenience4. Simple Random Sample (SRS)5. Cluster
© Cengage Learning. All rights reserved. 1 | 21
Census vs. Sample
• In a census, measurements or observations are obtained from the entire population (uncommon and often impractical).
• In a sample, measurements or observations are obtained from part of the population (common).
© Cengage Learning. All rights reserved. 1 | 22
Observational Studies and Experiments
• Observational Study – Measurements are obtained in a way that does not change the response or the variable being measured. (No treatment is applied.)
• Experiment – A treatment is applied in order to observe its effect on the variable being measured. The research controls this primary variable.
© Cengage Learning. All rights reserved. 1 | 23
Experiment
• Used to determine the effect of a treatment.
• Experimental design needs to control for other possible causes of the effect.
– Placebo effect. – Lurking variables.
• To minimize these confounds, create one or more control groups that receive no treatment.
© Cengage Learning. All rights reserved. 1 | 24
Experiment Designs
• Randomization – A random process is used to assign individuals to a treatment group or to a control group.
• Double-Blinding – minimizes the unintentional transfer of bias between researcher and subject.
© Cengage Learning. All rights reserved. 1 | 25
Surveys• Collecting data from respondents by asking them
questions.
Survey Pitfalls• Nonresponse → undercoverage of population.• Truthfulness – respondents sometimes lie.• Faulty recall of respondent• Hidden bias – due to poor question wording.• Vague wording – “sometimes”, “often”, “seldom”• Interviewer influence – who is asking the
questions and in what manner.• Voluntary response – relatively interested
individuals are more likely to participate.