# AP Statistics - Full

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Normal Distribution

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## Description and Tags

### 36 Terms

1

Normal Distribution

Data near the mean are more common than data far from the mean

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2

Positive Skew distribution

Tail is longer on the right

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3

Negative Skew Distribution

Tail is longer on the left

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4

SOCS

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5

z-score

Flashcard: "Z-score formula calculates how many standard deviations a data point is from the mean in a normal distribution. Formula: z = (X - μ) / σ."

Can also be found using invNorm on calculator

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6

Interquartile Range

Q3 - Q1

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7

Outlier

Data point if it is less than [Q1 - 1.5IQR] or more than [Q3 + 1.5IQR]

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8

Standard Deviation

Average distance between an individual data value and the mean

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9

Least-Squares Regression Line

Associations that are approximately linear on a scatter plot

Formula: ŷ = a + bx

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10

Residuals

The difference between the actual value on the scatter plot and the value on the line of the scatter plot

Formula: e= y - ŷ

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11

Line of Best Fit

Residuals on the LSRL being in a random pattern

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12

Lurking Variable

One variable causing two another variables to change together

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13

Confound Variable

The effect of multiple explanatory variables on a response variables cannot be changed

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14

Simple Random Sample (SRS)

Each set of individuals having an equal chance of being randomly selected

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15

Stratified Random Sample

Population divided into groups or strata and use SRS to select individuals from groups

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16

Cluster Sample

Population is divided into non-homogenous groups (ex: geographic location) and use SRS to select individuals in groups

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17

Systematic Sample

Selecting individuals in a certain interval order (ex: every 5th person)

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18

Convenience Sample

Biased sample that selects individuals in a ‘convenient’ manner.

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19

Voluntary Response Bias

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20

Non-response Bias

Researchers choose the participants, but refuse to participants

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21

Response Bias

Anything in the survey design that might induce a SPECIFIC answer

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22

Undercoverage Bias

Some portion in population not being included in right proportion

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23

Single-Blind

One party (researchers or participants) are blinded

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24

Double-Blinded

Both parties are blinded

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25

Placebo

Fake treatment

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26

Conditional Probability

The likelihood of an event happening given that another event has already occurred. It is calculated by dividing the probability of both events occurring by the probability of the given event.

P(A|B)

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27

Compound Events (AND)

Compound Events (AND): Events that occur simultaneously or in conjunction. The outcome is dependent on the occurrence of both events. Independent

P(A B)=P(A)⋅P(B)

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28

Compound Events (OR)

In probability, this refers to events that can occur separately or together, resulting in at least one of the events happening.

P(A U B) = P(A) +P(B) - P (A B)

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29

Independent Events

One event does not affect the probability of another event

If P(A and B)=P(A)⋅P(B)

If P(A|B) = P(A)

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30

Mutually-Exclusive Events

Both events cannot occur at the same time

If P(A B) = 0

Uses the (OR) formula

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31

Binomial Probability

A probability distribution that describes the number of successes in a fixed number of independent trials with the same probability of success.

binomPdf(n,p,x)

n = number of trials

p = probability of success

x = number of successes

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32

Geometric Distribution

A probability distribution that models the number of trials needed to achieve the first success in a series of Bernoulli (trials with two possible outcomes) trials.

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33

z-test

Only used if standard deviation is known

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34

one-sample t-test

Only used if experiment has only one independent variable and one group/condition

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t-test for independent means

Only used if there is one independent variable and two groups/conditions

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36

Pooled variance

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