Describing data

Question 1

Name 2 types of Qual data types

Answer 1

Nominal and Ordered categorical (ordinal)

Question 2

What is Ordered categorical (ordinal)

Answer 2

Qual type of data
data that can be put into more than 2 cat's, eg social class or grade of BC
Mutually exclusive and ordered

Question 3

what is Nominal

Answer 3

Qual type of data
displayed usually in a pie or bar chart
mutually exclusive and unordered
eg blood group or gender

Question 4

name 2 types of Quan data types

Answer 4

Measured (Continuous) and Discrete

Question 5

what is Discrete data type

Answer 5

Quan data type
can only take certain whole number values
eg number of children in a family

Question 6

What is Measured (Continuous) data

Answer 6

Quan data type
take values given within a range
limited only by accuracy of instrument
eg weight in Kng, age, height

Question 7

what does Binary data fall under

Answer 7

Nominal data

Question 8

what can numerical data be plotted as

Answer 8

dot plot, stem and leaf diagram, histogram, Box and whisker plot

Question 9

What is mean and how do you calculate it

Answer 9

the average of the sample

To get the mean you add all the sample values then divide over the size of the sample

Question 10

what is the medium and how do you calculate it

Answer 10

Middle value of the ordered sample

Order sample values small to large- basically the middle number

Question 11

What is the mode and how do you calculate it

Answer 11

A third measure of location is the mode which is simply the most common value observed
eg 2 appears most on sample of 10 with 2 appearing 6 times

Question 12

what are the pros and cons of mean/median/mode

Answer 12

Median robust to outliers.
Median/mode reflects what ‘most’ people experience.
Mean uses all the data (more ‘efficient’).
Mean is ‘expected’ value.
Mean more common with statistical tests.
Mode useful for grouped or categorical data

Question 13

Is the Median robust to outliers.

Answer 13

Yes

Question 14

what is the most common statistical tests

Answer 14

Mean

Question 15

which measure uses all the data

Answer 15

mean

Question 16

if the data is skewed what measure do you use

Answer 16

medium

Question 17

if the data is symmetrical what measure do you use

Answer 17

Mean

Question 18

what are the 3 approaches to quantifying the variability

Answer 18

1. Range
2. Inter quartile Range
3. Standard deviation

Question 19

what is the range

Answer 19

Simplest way to describe the spread of a data set is to quote the minimum (lowest) and maximum (highest) value
Difference between the smallest value and the largest
Effected by extreme values at each end of the data

Question 20

what is the Inter quartile range

Answer 20

Split the data set into four equal parts -  quartiles
Using three cut-points
Lower quartile 	(25th centile) 
Median	 (50th centile)	    
Upper quartile  	(75th centile)   

Inter quartile range (IQR) tells you where the middle 50% of your data lies
IQR = upper quartile - lower quartile

Graphical way of summarising data using percentiles is the box & whisker plot.

Basically tells you where medium is, useful for looking at deprivation eg upper Q and lower Q

Question 21

How do you calculate the IQR

Answer 21

When the quartile lies between 2 observations easiest option is to take the mean
Take mean away from medium- eg 2 away from 6- IQR-4 and 8- medium 6

Question 22

what is Variance

Answer 22

Based on the idea of averaging the distance each value is away from the mean
Basically, you work out each values difference from the mean, square them and add them up then divide that number by the sample size-1
The variance is not a suitable measure for describing variability because it is not in the same units as the raw data

Question 23

Is the Variance suitable measure for describing variability

Answer 23

No The variance is not a suitable measure for describing variability because it is not in the same units as the raw data

Question 24

what is a suitable measure for describing variability

Answer 24

Standard deviation

Question 25

How do you calculate SD

Answer 25

square root the variance

Question 26

sd vs IQR

Answer 26

S.D. vulnerable to ‘outliers’
Not useful for skewed data

IQR robust
Does not use all the data

Question 27

why use the mean and SD

Answer 27

For many variables in health sciences the mean ± 1 SD covers 68% of the distribution.
The mean ± 2 SDs covers 95% of the distribution.
The mean ±2 SDs is called the ‘normal reference range’.

Question 28

What does 95% of the distribution mean

Answer 28

the mean +- 2SD

68% IS 1SD+-

Question 29

in a normal distribution do the mean and medium coincide

Answer 29

Yes

Question 30

what summary measure is appropriate for symmetrical data

Answer 30

If symmetrical use the mean and standard deviation

Remember this data is bell shaped

Question 31

what summary measure is appropriate for Skewed data

Answer 31

If skewed the median and inter quartile range is more appropriate
Remember this data is presented with tail