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All Statistics Flashcards

1
Q

Center

A

Mean, Median or Mode

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2
Q

Measures of Spread

A

How far apart the numbers are in relation to each other

Range, IQR, Variance and Standard Deviation

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3
Q

Shape

A

Symmetric, normal, skewed left, skewed right, uniform, bimodal

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4
Q

Variability

A

How spread out numbers in a set are in relation to each other. Measured by spread.

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5
Q

Box Plot

A

A graph of the 5 number summary

A modified box plot shows if the data set has outliers.

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6
Q

Stemplot

A

A graph for quantitative data. Each value of the data set is represented by a stem and a leaf. Each leaf may only be 1 digit. Stem plots may have rounded values in place of the actual data.

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7
Q

Histogram

A

Common distribution for one variable

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8
Q

Dot Plot

A

A simple graph for small data sets

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9
Q

Mean

A

Average

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10
Q

Median

A

The middle number in a data set when the numbers are in order.

Sometimes called Q2 or MED

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11
Q

Mode

A

Most common value within the data

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12
Q

Outliers

A

A value that doesnt follow the general trend of the data.

Upper limit = Q3 + 1.5(IQR)

Lower limit = Q1 - 1.5(IQR)

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13
Q

Standard Deviation

A

A measure of spread. The average distance from the mean.

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14
Q

Range

A

A measure of spread

Maximum-Minimum

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15
Q

5 Number Summary

A

Used in box plots

Min-Q1-Median-Q3-Max

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16
Q

Individuals

A

Person/object that is a member of the studied population

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17
Q

Quantitative

A

Numerical measures (order)

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18
Q

Qualitative

A

Classification of individuals based on attributes/characteristics (categorical, grouping)

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19
Q

Bar Graph

A

Used for categorical data

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20
Q

Ogive

A

A relative cumulative frequency histogram

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21
Q

Pie Chart

A

Categorical data separated into percentages

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22
Q

Symmetric

A

Equal on both sides

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23
Q

Minimum

A

Smallest value within the data

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24
Q

Q3

A

The median between the median and the maximum

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25
Time Plot
Used to follow trends based on time (connecting)
26
Q1
Median between the minimum and median
27
Normal
Perfectly symmetric distribution
28
Uniform
Histogram with bars of all the same height
29
Bi-Modal
A graph with 2-peaks
30
Maximum
Largest value in the data set
31
Density Curve
On/above horizontal (x) axis; the area underneath is exactly 1
32
Inflection Points
The point where the graph changes from concave upward to concave downward Approximately where one standard deviation lies from the mean
33
68-95-99.7 Rule
Empirical Rule
34
N(μ, σ)
Short-hand notation for normal distribution N=Normal Center=μ (mean) Spread=σ (standard deviation)
35
Standard Normal Distribution
Mean=0 Standard Deviation=1
36
z = (x - μ) / σ
Normal Distribution Equation
37
Normal Probability Plot
Shows linearity
38
Explanatory Variable
x, input, independent
39
Response Variable
y, output, dependent
40
Scatterplot
41
Regression Outlier
f
42
Independent Variable
One event has no effect on the other
43
Dependent Variable
Two events have effects on eachother
44
Influential Observation
A point in a scatter plot that changes the regression line
45
LSRL
46
Positive Association
As x increases, y increases As x decreases, y decreases Positive correlation
47
Negative Association
As x increases, y decreases As x decreases, y increases Negative correlation
48
Correlation
"r" As r-value becomes closer to 1, the correlation becomes stronger
49
Coefficient of Determination
r² written as a decimal/percentage (% of the change in y is explained by the change in x)
50
Regression Line
ŷ = a + bx "line of best fit"
51
Residual
observed - expected (y-ŷ)
52
Slope
"b" value in ŷ=a+bx
53
y-intercept
"a" value in ŷ=a+bx
54
Residual Plot
A plot representing the x values and residual values (y-ŷ)
55
Causation
Changes in x cause changes in y
56
Extrapolation
When you predict for a value outside of the domain
57
Confounding
effect of y on x, mixed up with effects on y with another variable, z
58
Common Response
x and y respond to changes in unobserved variables
59
Lurking Variable
variable that has an important effect on the relationship among variables in the study, but not included
60
Conditional Distribution
61
Marginal Distribution
Totals of rows and columns
62
Observational Study
Does not attempt to influence responses; just observing
63
Experiment
Deliberately imposes some treatment on individuals in order to observe their response
64
Population
Entire group of individuals to be studied
65
Sample
Subset of studied population
66
Census
Attempting to contact every individual in population
67
Bias
Systematically favoring certain outcomes
68
Voluntary Response Sample
A sample from volunteers who are choosing to participate
69
Convenience Sample
Choosing random participants for a sample convienently instead of stratigically
70
SRS
Simple Random Sample
71
Stratified Random Sample
Group(strata) by a common variable, then take SRS of each group (strata)
72
Table of Random Digits
Used to choose subjects within a sample
73
Undercoverage
Some group of the population left out in choosing process
74
Nonresponse
Individual cannot be contacted/does not cooperate
75
Response Bias
Interviewer may have an influence on respondant's answers
76
Experimental Units
Individuals on which an experiment is done
77
Subjects
Units are people
78
Placebo Effect
Dummy treatment with no physical effect
79
Treatment
Specific experimental condition applied to units
80
Control Group
Group of subjects with no treatment/given a placebo
81
Statistically Significant
Observed effect is too large to attribute plausibly to chance
82
Double Blind
Neither subjects/people who have contact with them know which treatment a subject recieves
83
Block Design
Random assignment of units to treatments is carried out within each block
84
Matched Pairs
2 treatments: - match subjects (pairs) - each subject gets both treatments in random order (blocking)
85
Sample Space
Set of all possible outcomes
86
Probability
Outcome of a random phenomenon is the proportion of times the outcome would occur in a very long series of repetition
87
Venn Diagram
Probability Represents probability using area (2+ events) P(S)=1
88
Tree Diagram
Probability
89
Independent
One event does not change the probability of another event
90
P(S)=1
Area within a venn diagram
91
P(A&B)=P(A)P(B)
Testing for Independence (Probability)
92
P(AorB)=P(A)+P(B)-P(A&B)
Disjoint/Mutually Exclusive
93
Conditional Probability
The probability of A, given B
94
Complement of an event
(1-p)
95
Mutually Exclusive/Disjoint
Events cannot occur at the same time
96
Continuous Random Varibale
Graphed by a density curve (ex. normal curve)
97
Discrete Random Variable
x has a amount that is countable for possible values
98
Law of Large Numbers
As sample number increases, the sample results become more accurate
99
Binomial Distribution
f
100
Independence
f
101
PDF
binompdf(n,p,k) n= trials p= probability of success k= number of successes
102
CDF
binomcdf(n, p, k) n= trials p= probability success k= number of successes
103
What is the shape of the graph?
Skewed Left
104
What is the shape of the graph?
Skewed Right
105
Back to Back Stem Plot
Used for comparing two distributions. Leaves are increasing in values away from the stem.
106
σ2x+y=σ2x+σ2y
Combining variances (population)
107
μa+bx=a+bμx
property of means
108
σ2a+bx=bσ2x
property of variances
109
μx+y=μx+μy
Combining means (population)
110
σ2x=Σ(Xi - μx)2 Pi
Variance for discrete distribution
111
μx=ΣXi(Pi)
Mean for discrete distribution
112
μx=np
Mean of binomial distribution
113
σ=√np(1-p)
Standard deviation of binomial distribution
114
Z-Score
The standardized score z = (x - μ) / σ
115
B(n,p)
B= binomial distribution n= sample size (trials) p= prob of success
116
Designing Experiments
1. Control (Lurking variables) 2. Randomization (Treatments) 3. Replication

AP Statistics (12 decks)

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