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AP Stats > AP Stats > Flashcards

AP Stats Flashcards

1
Q

categorical variable

A

labels that place each individual into a particular group
ex: race, sex, age group

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1
Q

quantitative

A

takes number values that are quantities, counts, or measurements
ex: height, weight, cost

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2
Q

quantitative discrete

A

a fixed set of possible values
ex: how many green marbles you draw out of a bag

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3
Q

quantitative continuous

A

any value in an interval on the number line
ex: time

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4
Q

relative frequency table

A

shows the proportion or percent of individuals having each value.

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5
Q

two-way table

A

a table of counts that summarizes data on the relationship between two categorical variables for some group of individuals.

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6
Q

categorical graphs

A
  • Pie Charts
  • Pictographs
  • Dot-plots
  • Bar Graphs
  • Side-by-side Bar Graphs
  • Segmented Bar Graphs
  • Mosaic Plots
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7
Q

Association

A

if knowing the value of one variable
helps us predict the value of the other

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8
Q

Simpson’s Paradox

A

a contradiction between what we see when looking at individual
categories and the subtotals for our distributions when dealing with categorical
variables

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9
Q

Quantitative Graphs

A
  • Dot-plots
  • Stem-plots
  • Histogram
  • Boxplots
  • Ogives
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10
Q

how we describe distributions

A

CUSS + BS
-C: center
-U: unusual outliers
-S: spread
-S: shape
-BS: be specific

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11
Q

mean

A

average of all individual data values

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12
Q

Statistic

A

a number that describes some characteristic of a sample
-ex: asking 20 random people their height and averaging results

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13
Q

Parameter

A

a number that describes some characteristic of a population
-ex: asking everyone in the population
their height and averaging results

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14
Q

Range

A

difference between the maximum value
and the minimum value

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15
Q

Standard Deviation

A

the typical or average distance of the values in a distribution from the _mean

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16
Q

Interquartile Range (IQR)

A

IQR = Q3 – Q1

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17
Q

lower Outlier Test

A

Lower Outliers < Q1 – 1.5(IQR)

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18
Q

higher outlier test

A

Higher Outliers > Q3 + 1.5(IQR)

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19
Q

response variable

A

measures an outcome of a study

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20
Q

explanatory variable

A

may help predictor explain changes in a
response variable

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21
Q

population

A

the entire group we want to know about

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22
Q

census

A

collects data from every individual in a population

23
Q

sample

A

a subset of individuals in the population

24
Q

population parameter

A

ex: google says that 79% of people everywhere have a dog
*it’s from everyone

25
Q

sample statistic

A

student asks 100 people if they have a dog and 70% say yes
*it’s from a small sample

26
Q

convenience sampling

A

selects individuals who are easy to reach

27
Q

voluntary response sampling

A

allows people to respond if they want to

28
Q

SUDS

A

*Used for describing data
-S: strength (strong, moderate, weak)
-U: unusual values (outliers)
-D: direction (positive, negative)
-S: Shape (bell, Bimodal, skewed, uniform)

29
Q

correlation(r)

A

measures the direction and strength:
-strong: close to 1 or -1
-does not imply causation
-does not measure form
-only for linear relationships

30
Q

Regression Line(LSRL)

A

line that models how a response variable y changes as an an explanatory variable x changes
ŷ=a+bx

31
Q

ŷ=a+bx

A

ŷ: predicted y
a: y-intercept(a=ȳ -bx̄)
b: slope(b= r sy/sx)
x: x variable

32
Q

extrapolation

A

uses the regression to predict a value outside of the interval

33
Q

residual

A

actual y - predicted y OR (y-ŷ)

34
Q

least squared regression line(LSRL)

A

the sum of the squared residuals as small as possible

35
Q

coefficient of determination(r²)

A

n% of variability in y can be explained by the linear model

36
Q

stratified random sampling

A

selects a sample by choosing an SRS from each group and combining the SRSs into one big sample

37
Q

cluster sampling

A

selects a sample by randomly choosing clusters and including each member of selected clusters in the sample

38
Q

systemic random sampling

A

selects a sample from an ordered arrangement of the population by randomly selecting one of the first k individuals and choosing every kth after that

39
Q

under-coverage

A

when some members of the population are less likely to be chosen or can’t be chosen for a sample

40
Q

non-response

A

when an individual chosen for tyhe sample cannot be contacted or refuses to answer

41
Q

response bias

A

when there is a systematic pattern of inaccurate answers

42
Q

observational

A

observes results without trying to influence them

43
Q

experiment

A

deliberately and randomly imposes treatments to measure responses

44
Q

confounding

A

when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other

45
Q

principles of experimental design

A

1.) how many in each
2.) randomization
3.) repeats?
4.) rest/stop
5.) assign to treatment groups

45
Q

match pair design

A

a common experimental design for comparing two treatments that use blocks of 2

45
Q

block

A

a group of experimental units that are known before the experiment to be similar in some way in terms of response to treatment

46
Q

random selection

A

allows inference about the population from which the individuals were chosen

47
Q

random assignment

A

allows for inference about cause and effect

48
Q

law of large numbers

A

if we observe more and more trials of any random process, the proportion of times that a specific outcome occurs approaches its probability

49
Q

mutually exclusive

A

when you have one, you CANT have the other

50
Q

categorical conditions

A

☑ randomization
☑ independence OR 10% rule
☑ np rules

51
Q

quantitative conditions

A

☑ randomization
☑ independence OR 10 % rule
☑ central limits theorem: n>30

52
Q
A

AP Stats (1 decks)

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