Data analysis

Question 1

Nominal

Answer 1

data categorical, independent and coded as numbers

Question 2

example of nominal

Answer 2

diagnosis eg 
1 = stable angina 
2 = unstable angina 
3 = acute coronary syndrome STEMI
4 = acute coronary artery syndrome NSTEMI

Question 3

ordinal

Answer 3

data are categorical and have a relative direction

Question 4

ordinal eg

Answer 4

BMI: 
1 = underweight
2 = normal weight 
3 = overweight 
4 = obese

Question 5

interval

Answer 5

data relative to each other

no true zero (absence) exists

Question 6

interval eg

Answer 6

temp:
degrees
kelvin
F

Question 7

ratio

Answer 7

data relative to each other
true 0 (absence) exists
eg currency

Question 8

what type of variable is colour

Answer 8

nominal - different primary colours
ordinal and interval - shades
ratio - wave lengths

Question 9

what is a statistic

Answer 9

describes a characteristic of a sample

Question 10

what is a parameter

Answer 10

describes a characteristic of a population

Question 11

samples

Answer 11

study small gp (sample) to infer what would happen in the pop
want to be representative

Question 12

intention to treat

Answer 12

all data from randomised trials

Question 13

per protocol analysis

Answer 13

based on treatment received/completed

risk of adding bias

Question 14

quantitive data

Answer 14

interval and ratio

Question 15

qualitative

Answer 15

nominal and ordinal

Question 16

axis labels for frequency distribution

Answer 16

y - frequency

x - variable

Question 17

mean

Answer 17

add all values and divide by number of values

Question 18

median

Answer 18

order all values in ascending order and choose the middle value

Question 19

mode

Answer 19

identify the most frequent value measured

Question 20

effect of mean=mode=median

Answer 20

normally distributed

symmetrical

Question 21

effect of mean»median>mode

Answer 21

shift to lower end

Question 22

averages

Answer 22

different indications of true averages
depend on data interpretation
non-parametric need different statistical tests

Question 23

normal distribution

Answer 23

bell shaped curve

or Gaussian distribution

Question 24

mean =

Answer 24

Ex/n

Question 25

variance =

Answer 25

E(x - mean)squared/(n-1)

Question 26

sd =

Answer 26

𝑠= √𝑉𝑎𝑟

Question 27

SEM =

Answer 27

𝑆𝐸𝑀= 𝑠/√𝑛

Question 28

confidence interval =

Answer 28

mean +- z xSEM

Question 29

z score for 95% ci

Answer 29

1.96

Question 30

excel mean

Answer 30

=average(XX:XX)

Question 31

excel variance

Answer 31

=var.s(XX:XX)

Question 32

excel sd

Answer 32

=stdev(XX:XX)

Question 33

excel SEM

Answer 33

=stdev(XX:XX)/sqrt(count(XX:XX))

Question 34

comparision between SEM and sd

Answer 34

SEM

Question 35

legend

Answer 35

remember n numbers

Question 36

excel formulae for T test

Answer 36

ttest(array1,array2,tails,type)

Question 37

t test tails

Answer 37

1 - see difference in 1 dirn | 2 - not assuming anything, T test in both directions

Question 38

type of T test

Answer 38

``` paired - same sample unpaired - completely independent 2 - unpaired, equal variance 3 - unpaired, unequal variance better to do 3 if unsure ```

Question 39

what does the appropriate index of average and variability depend on

Answer 39

distribution

Question 40

normality

Answer 40

key assumption | informs how data is managed, analysed and reported

Question 41

when to use mean, median or mode

Answer 41

not mean when outliers median - when data skewed - mean loses ability to show central value as data is dragging it away, median less stringly affected mode - not with continuous, unlikely to get more than 1 people with exactly same value mean - when normally distributed

Question 42

when to use mean, median and mode with data typesn

Answer 42

nominal - mode ordinal - median interval/ratio not skewed - mean interval/ratio data skewed - median

Question 43

when to use different types of variation

Answer 43

SD - shows general variability, descriptive, assess overall variation, estimate percentiles of normally distributed data SE - variability between samples, technical and inferential, assess precision of estimate CI - indicates likely range of values - assess the certainty of an estimate and compare to bench marks