Chapter 1

Question 1

WHAT IS STATISTICS

Answer 1

Statistics IS the science of gathering, describing, and
analyzing data

Question 2

Variable

Answer 2

-value that changes among a group
-numerical or categorical
-of one usually

Question 3

random variable

Answer 3

values determined by chance

Question 4

numerical variable

Answer 4

-values with equal units eg. weight in pounds, time in hours

Question 5

categorical variable

Answer 5

-places person or thing into a category

Question 6

data

Answer 6

counts, measurements, observations about variables in a group

Question 7

datum

Answer 7

single value

Question 8

population

Answer 8

-whole, consists of all things being studied
-particular group of interest
-group i want to know about

Question 9

sample

Answer 9

-within the whole
-group within the population being studied
-group i do know about

Question 10

parameter

Answer 10

P for population
-numerical population
-fixed
-parameters unknown
-“the population mean”

Question 11

statistic

Answer 11

S for sample
-actual numerical sample
-must contain population characteristics (representative sample)
-change with sample
-“the sample mean”

Question 12

Descriptive Statistics

Answer 12

Definition: Methods for organizing, summarizing, and presenting data.

Purpose: To describe what the data show.

Examples: Mean, median, mode, Standard deviation, range, Graphs

Key Point: They do not go beyond the data at hand

Question 13

Inferential Statistics

Answer 13

Definition: Methods for making predictions, decisions, or generalizations about a population based on a sample of data.

Purpose: To draw conclusions or test hypotheses.

Examples: Hypothesis testing

Key Point: beyond the observed data.

Question 14

Probability

Answer 14

-studies randomness
-deals with chance

Question 15

Qualitative data

Answer 15

-aka categorical

Definition: Describes qualities, characteristics, or categories.

Nature: Non-numerical (though sometimes represented with numbers as labels).

Examples: Hair color, Types of cars, Yes/No responses, gender

Key Point: Answers “what kind?” or “which category?”

Question 16

Quantitative Data

Answer 16

-aka counted

Definition: Describes quantities or amounts that can be measured or counted.

Nature: Numerical

Examples: Height in cm, Age in years
,Number of pets

Key Point: Answers “how much?” or “how many?”

Question 17

will qualitive have discrete or continuous

Answer 17

no

Question 18

discrete variable

Answer 18

Discrete → countable numbers (e.g., number of fries, number of siblings, 0,1,2,3)
-“number of”

Question 19

Continuous Variable

Answer 19

Continuous → measurable numbers that can take on any value in a range (e.g., height, weight, temperature)

-fractions and decimals

Question 20

Qualitative data charts

Answer 20

pie and bar:
-side by side pie and bar
-no missing data : other category

-pareto: bars sorted by size

Question 21

How to select a sample

Answer 21

-must be a representative sample
-has the same relevant characteristics as the defined population and does not favor
-allows people to study a population without studying every single individual in that population.
-is a valuable research tool.

Question 22

Random sampling

Answer 22

Selected by using chance or
random numbers
- Each individual subject
(human or otherwise) has
an equal chance of being
selected
-Examples: Drawing names from a hat

Question 23

Systematic Sampling

Answer 23

Select a random starting point and then select every nth subject in the population

Question 24

Convenience Sampling

Answer 24

Definition: Choosing a sample based on convenience, not random selection.
Downside: Often biased
-examples: A researcher surveys their classmates because they’re nearby.

Question 25

Stratified Sampling

Answer 25

Divide the population into at least two different groups (strata) with common characteristic(s), then draw SOME subjects from each group -example: divide men and women then pick some men and pick some women

Question 26

Cluster Sampling

Answer 26

Divide the population into groups (clusters), randomly select some of the groups, and then collect data from ALL members of the selected groups -example: exit polls

Question 27

replacements

Answer 27

with replacement: put back into population and can be chosen again without replacement: put back into population and cannot be chosen again

Question 28

Sample errors

Answer 28

Sampling Errors Definition: Errors caused by the sampling process itself. Nonsampling Errors Definition: Errors not related to sampling. Sampling Bias Definition: Occurs when some members of the population are less likely to be chosen. -large samples are bias

Question 29

variation

Answer 29

The degree to which data points in a set differ from each other or from the mean -present in any data set

Question 30

Frequency Distribution

Answer 30

Definition: Organizing raw data into a table with classes (groups) and frequencies. Class/Group: A category (qualitative or quantitative). Frequency: How many times a value appears. Relative Frequency: Frequency ÷ total number of values. Cumulative Relative Frequency: Sum of relative frequencies up to that class.

Question 31

go over freq table

Answer 31

slide 57 -note it will always equal 1 -organize the amount of numbers into categories -divide by total freq for relative freq -add rel freq up for cum to equal 1

Question 32

Experiments and Variables

Answer 32

Purpose: Investigate cause-and-effect between variables. Explanatory Variable: Causes change (independent). Response Variable: Affected by explanatory variable (dependent). Treatments: Different values of the explanatory variable. Experimental Unit: Single subject or object measured. Lurking Variables: Other factors that may affect results. Random Assignment: Isolates explanatory variable, spreads lurking variables evenly. Control Group & Placebo: Helps measure true effect, counteracts suggestion. Blinding: Subjects unaware of treatment to reduce bias. Double-Blind: Both subjects and researchers are unaware of treatment assignment