Intro/Chapter 1 Flashcards

1
Q

Descriptive Statistics

A

Reducing a closed set of data into key features

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2
Q

Inferential Statistics

A

Drawing conclusions about a larger group than you have data for; generalizations

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3
Q

3 steps of Inferential Statistics

A

1) Estimating/ Creating generalization
2) Testing the hypothesis
3) Fitting to a statistical model

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4
Q

2 Types of Numerical Data

A

continuous and discrete numerical data

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5
Q

Continuous Numerical Data

A

Data in a range, e.g: time, length

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6
Q

Discrete Numerical Data

A

Counted integers

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7
Q

2 Types of Categorical Data

A

Nominal and ordinal categorical data

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8
Q

Nominal Categorical Data

A

No natural order to the categories, e.g: gender, colors

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9
Q

Ordinal Categorical Data

A

A natural order to the categories, e.g: weekdays, scales on a test

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10
Q

Confounding Variables

A

variables that varies both the independent dependent variable, making an apparent, but not valid correlation.

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11
Q

Population

A

all subjects/specimens of interest

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12
Q

Sample

A

subset of population of size ‘n’

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13
Q

Histogram

A

basically a bar chart that plots y as frequency

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14
Q

Which side does the histogram favor?

A

Right. [ )

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15
Q

IQR

A

Inter quartile range Q3 - Q1

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16
Q

Standard deviation

A

s = sqrt((1/(n-1))(sum(yi-y)^2))

17
Q

Sample variance

A

s^2

18
Q

Sum of differences

A

sum(yi-ybar)

19
Q

RV - Definition and common variable

A

Represents a random process (X)

20
Q

Realization - Definition and common variable

A

observed outcome of RV (x)

21
Q

Probability Distribution of X

A

describes values that X can take and the probability of each. ( Normally a table for discreet RV)

22
Q

Discrete RV - Definition and function

A

only certain values can be listed p(x)

23
Q

Two properties of discrete RV and variables

A

Mean(mu) and variance(sigma^2)

24
Q

Continuous RV- Definition and function

A

X takes values in a range, cannot list all of them. f(x)

25
Q

p.d.f - relates to… and symbol

A

probability density function f(x)

26
Q

Mean (RV)

A

mu(x) = E(x) = sum(xp(x))

27
Q

Variance (RV)

A

sigma^2 = VAR(X) = sum(((x-mu(x))^2)p(x))

28
Q

Binomial RV

A

n identical trials with 2 outcomes. Probability of each trial is (pi)

29
Q

Binomial RV - how to symbolize

A

X~Bin(n,(pi))

30
Q

Binomial RV - p.m.f

A

p(x) = ((n!)/(x!(n-x)!))((pi^x)((1-pi)^(n-x)))

31
Q

Binomial RV - mean

A

mu(x) = n(pi)

32
Q

Binomial RV - variance

A

sigma^2 = n(pi)(1-(pi))

33
Q

Normal RV - how to symbolize

A

X~N(mu,sigma^2)

34
Q

Normal RV - inflection points occur…

A

at mu +- sigma

35
Q

Normal RV - Area under f(x) between inflection points

A

0.68