1. 1. Data Processing
Sci Info Skills

2. Statistics – making sense of numbers
Data Collection
Data Organisation
Data Interpretation
Data Presentation

3. Types of statistics Descriptive
the processed data provides a summary of the observations/measurements, such as averages, variations and graphs
Inferential
the processed data is used to make judgements or predictions, such as trends, indications of variations between different samples

4. Class Exercise 1.1
the average December temperature in Sydney has increased by 1C in the last 50 years
it is expected that the average December temperature in Sydney will increase by another 1C within 25 years
25% of people surveyed at a shopping centre indicated that they were aware of increasing temperatures in Sydney
A survey has a shown that 75% of Sydneysiders are ignorant of the changing climatic conditions in their city
Descriptive
Inferential
Descriptive
Inferential

5. Class Exercise 1.2
Identify the sample and the population in the following.
(a) a bottle of water is taken from a dam to be tested
Sample – the water in the bottle
Population – all the water in the dam
(b) the frog population of a large wetland is checked by looking at two separate hectares
Sample – the two hectares
Population – the whole wetlands

6. Class Exercise 1.2
(c) the levels of lead in fallout around a smelter are assessed by testing a selection of properties
Sample – the selected properties
Population - the whole area
(d) people in shopping centre are asked their opinions … to determine the level of awareness in the community
Sample – the people asked
Population – the community

7. Variables characteristic being measured
category - result of measurement is a “word”, e.g. yes (or no), truck, bird, sparrow, first (or second) etc
numerical - measurement produces number
could be limited to certain values (e.g. whole numbers)
any value (e.g. mass of an object)
Exercise 1.3
lead levels in fallout
types of birds observed
numbers of birds observed in different locations
numerical – any value
category
numerical –set values

8. Presenting & organising data
large quantities of raw data are not useful for presenting the results of the tests
they need to organised to show the results in a smaller scale
tables
graphs
averages
comparisons

9. Tabulating data organising it so that it can be evaluated more easily
generally some sort of table
category data is most usually grouped (tallied)
the number of times each different category occurs is the recorded result
can also be used where the data is numerical
only with fixed and pre-known values
a large number of data points
numerical (all values) data presents problem
must be grouped into ranges
information is lost, e.g. 0.1 and 4.9 both fit into 0-5 range

10. Grouping numerical data
identify the minimum and maximum values
decide how many groups are appropriate for the size of the dataset
determine the groups (which should be equivalent ranges – for example, 0-5,6-10 etc, but not 0-5, 6-20)
Class Exercise 1.4
You have a data set of 100 pH measurements of river water, ranging from 5 to 9. What would be an appropriate way of grouping them?
8 ranges of 0.5
e.g , etc

11. Frequencies
number of times a particular value or range occurs is the frequency
spread of data across the range of values is the distribution
Is it evenly spread across the groups?
Do certain groups have higher frequencies?
Is there any pattern?
frequency should considered in relation to total number of data values
relative frequency – the proportion (often as a percentage) of the frequency of the total dataset

12. Excel & tally charts
manually tallying - how many occurrences of each value – of large data sets is boring, tiring and potentially inaccurate
Excel has some functions which help:
COUNT(cell range)
COUNTIF(range , criterion)
FREQUENCY (range , group) – probably more trouble than it’s worth

13. COUNT ( ) returns the total number of cells with numerical data
ignores blank cells and non-numerical values A B 1 10 2 3 7 4 5
n/a 6
*** 8 9
=count(A1:A9)

14. COUNTIF ( ) returns the number of cells meeting a given criteria
criteria include =, > or < A B 1 10 2 3 7 4 5
n/a 6
*** 8 9
=countif(A1:A9,”>5”)

15. FREQUENCY(,) tally data into user-chosen groups
entered as an array formula
highlight a group of cells where you want the frequencies to appear
type in the formula and then hit the key combination CTRL+SHIFT+ENTER A B 1 10 2 3 4 5
n/a 6
*** 7 8 9
values for groups 0-5, 6-10
=frequency(A1:A9,B6:B7)

16. Two-way frequency tables
One sample set – two variables
Sex of koala
General state of health
Male
Female
Healthy 45 28
Ill 21 9
Two sample sets – one variable
Type of parkland
Origin of plant
Urban
Undeveloped
Native
37% 65
Introduced 51 20
Not identified 12 15

17. The typical value represents all the data values with one or two
average – some way of representing the “most common” value
variation – how much spread there is in data set
category variables – class with highest frequency (mode)
variation cannot be measured
numerical variables
mean – what we normally refer to as average
mode – most common value (used in grouped data)
median – the value in the middle when arranged in order
range – highest – lowest
standard deviation – calculation of difference of all points from mean
mean & std dev normally used in scientific data

18. Assignment 1 large amount of data simple formulas required
all questions and directions contained in Excel spreadsheet