Chapter 1, Intro to Data

Question 1

What is a summary statistic?

Answer 1

a single number summarizing a large amount of data

Question 2

What is a proper data set called? and what makes is “proper”?

Answer 2

data matrix, each row corresponds to a unique case and each column corresponds to a variable.

Question 3

What is the formal name for a row?

Answer 3

case or observational unit

Question 4

What do columns represent? and what is important to know about them?

Answer 4

characteristics, called variables (imp to understand what each variable means, as well as units of measurement)

Question 5

What are 2 types of variables?

Answer 5

Numerical and Categorical

Question 6

What are the 2 kinds of numerical variable?

Answer 6

Discrete and continuous

Question 7

What are the 2 kinds of categorical variable?

Answer 7

Ordinal and nominal

Question 8

What is a discrete numerical variable?

Answer 8

a number value that can only be a whole number, e.g. population, since you can’t have half a person

Question 9

What is a continuous numerical variable?

Answer 9

a number value that can be in between whole numbers, e.g. an hourly pay rate.

Question 10

What is an ordinal categorical variable?

Answer 10

a categorical variable that involves an ordering, e.g. educational level attained

Question 11

What is a nominal categorical variable?

Answer 11

a categorical variable that doesn’t involve an ordering, e.g. color

Question 12

What are possible categorical variables called?

Answer 12

levels

Question 13

What makes 2 variables “associated” or “dependent”?

Answer 13

When they show some connection with one another.

Question 14

What is a scatterplot graph useful for?

Answer 14

Showing whether or not 2 variables are associated, as well as trends in the relationship

Question 15

What is a positive correlation between 2 variables?

Answer 15

a relationship where if one variable increases, the other also increases or vice versa

Question 16

What is a negative correlation between 2 variables

Answer 16

a relationship where if one variable increases, the other decreases or vice versa

Question 17

What are independent variables?

Answer 17

variables that aren’t associated

Question 18

What 2 words express whether or not one variable affects another?

Answer 18

an explanatory variable (might affect) a response variable

Question 19

What are the 2 primary types of data collection?

Answer 19

observational and experimental

Question 20

What makes a study observational? Any why use this method?

Answer 20

Research do not interfere directly with how the data arise. (Surveys, collect data from existing records, follow a cohort of similar individuals in studies of diseases). Can provide evidence of association between variables, but can’t show a causal connection. Can give rise to hypotheses to be checked using experiments.

Question 21

Why use an experiment?

Answer 21

to investigate the possibility of a causal connection

Question 22

What is a sample?

Answer 22

A subset of the population to be studied.

Question 23

Define anecdotal evidence. Why is it a problem?

Answer 23

Data collected in a haphazard fashion. May not be representative of the population.

Question 24

What is a non-response rate and why is it important?

Answer 24

Non-response rate is the rate at which people in the sample population do not respond. A high non-response rate can skew the results.

Question 25

What is a confounding variable

Answer 25

a variable that is correlated with both explanatory and response variables. E.g. sun exposure is related to both the use of sunscreen and skin cancer. Also called a lurking variable, confounding factor, or a confounder.

Question 26

What are the 2 kinds of observational study?

Answer 26

Prospective, which identifies individuals and collects info as events unfold
Retrospective, which collects data after events have taken place (e.g. studying medical records)

Question 27

What are the 4 sampling methods?

Answer 27

1. Simple random (SRS): like a lottery
2. Stratified: divide population into groups of similar individuals (strata), then take a random sample from each group.
3. Cluster: divide population into groups of dis-similar individuals, then use data from a sample of the clusters
4. Multistage: involves more than one stage of sampling, 1. cluster sample, then 2 SRS within the selected cluster.

Question 28

What is sampling variability?

Answer 28

The natural variation in samples. Unavoidable, doesn’t usually cause problems.

Question 29

What are 2 biased sampling methods? (not everyone in the population has an equal chance of being part of the sample)

Answer 29

Convenience sample (people who are easy to reach, like standing on a street and collecting data from people who walks by)
Voluntary response sample: people who have chosen to include themselves in the sample, people with a strong interest in the topic are most likely to respond.

Question 30

What is a population parameter?

Answer 30

a number that describes something about an entire group or population

Question 31

What makes a study an experiment?

Answer 31

researchers assign treatments to cases. When treatments are assigned randomly, it’s called a randomized experiment.

Question 32

What are the 4 principles randomized experiments are built on?

Answer 32

1. controlling for possible confounding variables such as how much water a person takes a pill with
2. randomization into treatment and control groups order to account for variables that can’t be controlled
3. replication: the more often a result is replicated (though a sufficiently large sample), the more accurately the effect of the explanatory variable on the response variable can be estimated
4. Blocking: using strata within the experimental population, i.e., grouping population into blocks that have certain characteristics, if they suspect those characteristics (variables) may influence the response.

Question 33

What are the 2 ways to employ randomization and what is the benefit of using them?

Answer 33

1. random sampling allows you to generalize results to the target population
2. random assignment to treatment or control group strengthens the suggestion of causality between explanatory and response variables.