Ch. 1: Statistics

Question 1

Data

Answer 1

Facts, especially numerical facts, collected together for reference or information.

Question 2

Statistics

Answer 2

• Aggregated data, summed up into one or a few numbers or an image, are statistics
• A way to turn data into useful information
• A tool for creating new understanding from a set of numbers. Ideally, the data will help us tell new stories.

Question 3

Information

Answer 3

Knowledge communicated concerning some particular fact.

Question 4

Population

Answer 4

• The group of all items of interest to a statistics practitioner.
• Frequently large sometimes infinite

Question 5

Parameter

Answer 5

A descriptive measure of a population

Question 6

Sample

Answer 6

• A set of data drawn from a population
• Potentially very large, but less than the population.
• If we could afford it, we’d directly look at the population, but populations tend to be big.
• A statistic is a descriptive measure of a sample

Question 7

Cross-sectional

Answer 7

A survey done in many places at the same time.
Ex: Tracking average temperatures this July in ten places on the US East Coast including Long Island, Baltimore, and Ocean County NJ

Question 8

Time-series

Answer 8

Done in the same place more than once

Ex: Counting the tons of tuna captured each year over a 20 year span

Question 9

Panel data

Answer 9

Is both time-series and cross-sectional. If I interview all of you now and every 5 years for the next 50 years, that would make a panel data set.

Question 10

Descriptive statistics

Answer 10

• Are methods of organizing, summarizing, and presenting data in a convenient and informative way.
• Describe the data set that’s being analyzed, but don’t allow us to draw any conclusions or make any inferences about the full set of data or the population as a whole.

Question 11

Inferential statistics

Answer 11

• A set of methods, but it is used to draw conclusions or inferences about characteristics of populations based on data from a sample.
• Statistics that are useful not just to describe a sample but to draw conclusions about a larger population

Question 12

Statistical inference

Answer 12

Is the process of making an estimate, prediction, or decision about a population based on a sample.

Question 13

Observational study

Answer 13

• Doesn’t involve messing with things: it’s just watching to see what’s already going on.
• Hard to identify cause & effect because confounding variables make it look there’s a relationship when there’s not

Question 14

Experiments

Answer 14

Involve researchers manipulating “explanatory” variables to check for effects on “outcome” variables.
- Ex: Stanley Milgram’s work on obedience

Question 15

Simple Random Sample

Answer 15

• Is chosen using a method that ensures that each different possible sample of the desired size has an equal chance of being chosen.
• Example: make a list of all possible choices and choose from them using random.org.
• Note that it is the selection process, and not the final sample, which determines whether the sample is a simple random sample.

Question 16

Stratified sampling

Answer 16

• Split the target population into groups and randomly sample the groups
• Each group is called a “stratum” (plural “strata”)
• Ex: In a class of 16 boys and 16 girls, if I chose a sample of 10 by randomly selecting 5 boys and 5 girls

Question 17

Cluster sampling

Answer 17

• Split the target population into groups and choose a few groups and do everyone in them
• Ex: Split the entire country into districts, and within two districts, administer proficiency tests to all Jiffy Lube auto technicians.

Question 18

Systematic sampling

Answer 18

1. Get a list of all possible observations you could sample. For example, say you have a list of all 10000 Dunkin Donuts stores.
2. Put the list in some set order. (Say by zip code.)
3. Choose the size of sample you want. (Say 50.)
4. Calculate the constant “k,” which is the total number of possible observations (10000 in our example) divided by sample size (50). In the example, 10000/ 50 = 200.
5. To get the first observation to sample, choose randomly (using random.org?) from the first k observations. In this example, say random.org tells us to choose the 16th observation.
6. Now choose every kth observation after that. So, we’d choose the 216th, the 416th, etc.

Question 19

Convenience sampling

Answer 19

Yields results that are about as good as convenience store food. Basically a “convenience” sample is just some people you grabbed because they walked by, or something like that: it’s not careful at all.

Question 20

Selection bias

Answer 20

• Leaving out some part of the population of interest.

- Ex: In 1948 a phone survey systematically left out those without a phone.

Question 21

Variable

Answer 21

• the set of everyone’s answers to one question
• columns
• Ex: name, address, phone number

Question 22

Observation

Answer 22

• set of responses from one respondent
• rows
• Ex: James, Hagerstown.

Question 23

Quantitative

Answer 23

Quantitative: if they are numbers you can do math with, like salaries or a GPA.

Question 24

Qualitative

Answer 24

Qualitative: Data that are not numbers, like a list of people’s hometowns, or phone numbers

Question 25

Nominal

Answer 25

• “naming” data

- Ex: Type of car owned by a household

Question 26

Ordinal

Answer 26

• “order” data

- Ex: Crash-test safety information on cars sold. Score is out of 5 stars

Question 27

Interval

Answer 27

• Data with meaningful numbers, and where zero means zero

- Ex: Mileage on a car