Statistics AS Flashcards

1
Q

Sum of the data notation

A

Σx

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2
Q

Mean of the data notation

A

x bar

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3
Q

X bar =

A

Σx / n

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4
Q

Q1 =

A

n/4

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5
Q

Q2 =

A

n/2

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6
Q

n3 =

A

3n /4

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7
Q

IQR =

A

Q3 - Q1

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8
Q

X bar = (from frequency table)

A

Σx2 / Σf

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9
Q

Median from UNGROUPED data set

A

n + 1 / 2

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10
Q

Median from GROUPED data

A

n / 2

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11
Q

Linear interpolation =

A

x - lower bound / group width = percentile - lower bound / group width

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12
Q

σ^2 = (variance)

A

Σx^2/n - (Σx/n)^2

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13
Q

σ = (standard deviation)

A

Sqrt(Σx^2/n - (Σx/n)^2)

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14
Q

σ = (from coding)

A

Sqrt (Sxx summary stats / n)

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15
Q

σ^2 = (from frequency table)

A

Σxf ^2/Σf - (Σfx/Σf)^2

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16
Q

σ =(from frequency table)

A

Sqrt(Σxf ^2/Σf - (Σfx/Σf)^2)

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17
Q

Mean of y from code y = ax + b

A

a (x bar) + b

a times mean of x. Add b

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18
Q

Standard deviation of y from code y = ax + b

A

σy = a(σx)

a times standard deviation of x

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19
Q

Outlier definition

A

out of 1.5x IQR from Q1 or Q3
Or 2 standard deviations from mean

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20
Q

Frequency =

A

frequency density x class width x k

Where k is constant

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21
Q

Frequency = (from histogram)

A

k x area

Where k is a constant

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22
Q

How to draw a frequency polygon

A

Join up midpoints

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23
Q

Define cleaning the data

A

Removing incorrect data values (anomalies)

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24
Q

Define consistent

A

Smaller range/ standard deviation/ IQR

25
Q

Define experiment

A

Repeatable activity that has a result that can be observed and recorded

26
Q

Define outcome

A

A result from an experiment

27
Q

Define sample space

A

A way to show all possible outcomes

28
Q

Define event

A

An outcome/ outcomes

29
Q

And

A

n
intersection

30
Q

Or

A

u
Union

31
Q

Not

A

A’
The complement of A

32
Q

Define independent

A

Outcomes don’t affect each other

33
Q

for independent events, P(A n B) =

A

P(A) x P(B)

34
Q

For mutually exclusive, P(A n B) =

A

0

35
Q

Define mutually exclusive

A

Events can’t occur together

36
Q

For mutually exclusive, P(A u B) =

A

P(A) + P(B)

37
Q

Universe

A

S, U, ξ

38
Q

empty set

A

Φ

39
Q

conditional probability

A

Probability of A given B has already occurred P(A|B)

40
Q

P(A|B) =

A

P(A n B) / P(B)

41
Q

for independent events, P(A|B) =

A

P(A)

because we know P(A n B) = P(A) x P(B) and P(B) / P(B) = 1

42
Q

Two way table

A

Lists the frequencies for the outcomes of both events happening together (column and row)

43
Q

find conditional probability from tree diagram

A

Second tree is P(B|A) , P(B’|A) and P(B|A’) and P(B’|A’)

So P(B) = P(B|A) + P(B|A’)

44
Q

P(A u B) =

A

P(A) + P(B) x P(A n B)

45
Q

discrete random variable

A

CAPITAL X or Y

46
Q

P(X = x) meaning

A

Probability that random variable X takes value of x

47
Q

Σ P(X = x) =

A

1

48
Q

X is at most k

A

X =< k

49
Q

X is no greater than k

A

X =< k

50
Q

X is at least k

A

X => k

51
Q

When can binomial distribution be used

A

Fixed number of TRIALS, n
fixed probability of success, p
OUTCOMES of each trial are independent
2 OUTCOMES only

52
Q

mean of successful trials in binomial distribution

A

np

53
Q

Variance of number of successful trials

A

np(1 - p)

54
Q

let X =

A

NUMBER OF … (success outcome)

55
Q

nCr =

A

n! / r! (n-r)!

56
Q

P(X>a) = (for calculator)

A

1 - P(X<=a)

57
Q

P(X>=a) = (for calculator)

A

1 - P(X<=a-1)

58
Q

P(a < X < b) = (for calculator)

A

P(X <= b-1) - P(X <= a)

59
Q

P(a =< X =< b) = (for calculator)

A

P(X <= b) - P(X <= a-1)