Midterm Review Flashcards

0
Q

Area under density curve

A

Always = 1

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1
Q

How to tell if something is an outlier

A

Q1 - 1.5(IQR)
Q3 - 1.5(IQR)
Outside of this range is an outlier

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2
Q

Density Curve

A

A smooth curve which approximates the shape of a histogram and describes the overall pattern of a distribution

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3
Q

Residual

A

The difference between an expected value and the actual value (y-yhat)

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4
Q

Complementary events

A

When two events together make up the entire sample space (even and odd numbers)

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5
Q

Ogive graph

A

Graph of percentile, relative cumulative frequency distribution

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6
Q

Variance calculation

A

Σ(x-xbar) squared / (n-1)

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7
Q

Independent event

A

Choice of selecting one object does not affect ways of selecting other objects

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8
Q

Dependent events

A

Selecting an object does affect selecting other objects

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9
Q

Circular permutations

A

N objects in a circle, then (n-1)! permutations of the objects

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10
Q

Calculating correlation

A

Sum (standardized x)(standardized y) / (n-1)

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11
Q

Least squares regression

A

Method of predicting response given explanatory
Line of “response” on “explanatory”
LinReg(a+bx)
Yhat= a + bx

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12
Q

LSRL “b”

A

Slope:

r(sy/sx)

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13
Q

LSRL “a”

A

Intercept:

Ybar- b(xbar)

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14
Q

Coefficient of determination

A

R^2,

Percent variation that can be explained by the lsrl

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15
Q

Influential point

A

When removed, dramatically changes slope of lsrl, often x outlier

16
Q

Power law model

A

Y=ax^p
Log y = log a + p log x
Both variables are transformed

17
Q

Exponential growth model

A

Y=ab^x
Log y = log a + x log b
Only response is transformed

18
Q

Common response

A

A lurking variable- both x and y are acted on by another z force

19
Q

Confounding

A

A lurking variable which also affects the response, making it unclear how much effect the explanatory actually has

20
Q

Conditional distribution

A

Table cell/row or column total

21
Q

Marginal distribution

A

Sum or row total/table total

22
Q

Important in experiment design

A

Control (lurking variables)
Random (SRS)
Replication of experiment

23
Q

Observational study

A

No treatment/experiment

24
Q

Multiplication rule

A

If events a and b are independent, then p(a)p(b) = p(a and b)

25
Q

Random variable

A

Variable with numerical value

26
Q

Continuous random variable

A

Each individual outcome has p=0, use a normal distribution

27
Q

Discrete random variable

A

Has a finite # of values, each value has a probability

28
Q

Variance of discrete random variable

A

Sigma^2 sub x = Σ(x-μ)^2(p)

29
Q

Mean of discrete random variable

A

μx = Σ(xp)

30
Q

Means of random variables

A
μ(a+bx)= a + b μx
μ(x+y) = μx + μy
31
Q

Variance rules

A

Sigma^2sub x+y = sigma^x + sigma^2y + 2ρsigmaxsigmay

Sigma^2x-y= the same but - 2ρsigmaxsigmay
Where ρ is the correlation

32
Q

Rule of thumb for a binomial distribution

A

Use a normal approximation when np is greater than or equal to ten, and n(1-p) is greater than or equal to ten

33
Q

Mean and standard deviation of a binomial distribution

A

Mean=np

Strd Dv= root (np(1-p))

34
Q

Stratified random sample

A

Divide population into strata, take srs from each stratum and combine for whole sample

35
Q

Blocking

A

In an experiment, group together those known to be similar, and apply each treatment to each block so that there arent confounding variables