Week 1 Probabilities and Interpretations Flashcards

You may prefer our related Brainscape-certified flashcards:
1
Q

define the two types of data

A

qualitative - non numeric

quantitative - numeric

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

define the two types of quantitative data

A

discrete - data can only take certain values

continuous - data can take any value within a range

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what is the first step for data visualisation

A

Create the simplest graph that conveys the information you want to convey

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what is the 2nd step for data visualisation

A

consider the type of encoding object and attribute used to create a plot

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what is the 3rd step for data visualisation

A

focus on visualising patterns or on visualising details, depending on the purpose of the plot

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what is the 4th step for data visualisation

A

select a meaningful axis value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

what is the 5th step for data visualisation

A

data transformation and graph aspect ratios can be used to emphasize ratios of change

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what is the 6th step for data visualisation

A

plot overlapping points that allows density to become apparent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is the 7th step for data visualisation

A

use lines when connecting sequential data in time-series plots

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what is the 8th step for data visualisation

A

aggregate larger datasets

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what is the 9th step for data visualisation

A

keep axis ranges as similar as possible

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what is the 10th step for data visualisation

A

select an appropriate colour scheme

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

when are central tendency values useful

A

describing data with single values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

define the arithmetic mean

A

it is the central measure that is the result of the sum of all terms divided by the number of terms

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

what is the geometric mean

A

calculated as the N-th root of the product of the N elements in the datasets

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what is the harmonic mean

A

reciprocal of the arithmetic mean of the reciprocals of the data values

17
Q

what is the root mean square and its equation

A

the square root of the mean of sum of the squares of the data values
sqrt[ Σx^2 / n ]

18
Q

what is the median

A

the data value that separates the data into an upper and lower half

19
Q

what is the mode

A

the value that appears most frequently in a dataset

20
Q

what are the 7 measures of dispersion that can be used to characterise a dataset

A
variance/standard deviation
mean absolute deviation
skewness
kurtosis
covariance
correlation
covariance matrix
21
Q

what is variance and its equation

A

it is the spread of distribution
V(x) = 1 / N Σ(xi - μ)
μ is the true mean

22
Q

what is standard deviation in terms of variance

A

the square root of the variance

23
Q

what is the mean absolute deviation equation

A

MAD = 1/N ΣIxi - I

24
Q

what is skewness and its equation

A

measure of asymmetry of a distribution

γ = 1/σ^3 )^3> = 1/Nσ^3 Σ(xi -)^3

25
Q

what is kurtosis and its equation

A

measure of tailedness of a distribution

κ = 1/σ^4 )^4 - 3 = 1/Nσ^4 Σ(xi - )^4 - 3

26
Q

when are covariance, correlation and covariance matrix more useful as measures of dispersion

A

when dealing with multiple variables

27
Q

what is covariance and its equation

A

measure of the joint variability of two random variables

cov(x,y) = 1/N Σ(xi - )(yi - )

28
Q

what is correlation and its equation

A

it is the normalisation of covariance via standard deviation

p(x,y) = cov(x,y) / σxσy

29
Q

what is the covariance matrix

A

the combination of covariance in a matrix