Physics 2.1 AS 91168
4 Credits
Carry out a practical physics
investigation that leads to a nonlinear mathematical relationship.
Success Criteria: Can derive more Units and standard form.
• Anything that can be measured (velocity,
energy, volume) is called a quantity.
• In Physics almost any quantity is based on the
following three fundamental quantities: mass,
length and time.
Fundamental
Quantity
Symbol
Unit
Mass
m
kg
Length
L, l, d, x, h
m
Time
T, t
s
The quantity velocity (v) is derived by dividing the
fundamental quantity length (d) by the fundamental quantity
time (t).
The units for velocity can be derived from the units of length
and time.
v
d
t

m
 ms
1
s
volum e  l  l  l  m  m  m  m
3
Success Criteria: Can describe how random uncertainties can
occur and how repeated readings can minimise these.
Repeated measurements
Small variations in the equipment and the techniques used
in measuring cause random errors, an individual reading
may be quite different to the actual value.
By taking repeated measurements random errors that come
about because of human error and the imperfections in
apparatus used can be reduced and the average answer is
more likely to be closer to the actual value.
Repeating and averaging reduces the random error caused
by the difficulty in judging how high the ball bounces.
Success Criteria: Can describe how random uncertainties can
occur and how multiple measurements can minimise these.
Multiple measurements
The scale used to measure the object is too large, there will
be a large percentage error in the measurement.
By taking multiple measurements the measurement can be
taken to a higher number of significant figures.
It is difficult to accurately measure when exactly a complete
oscillation starts or ends as the mass moves quite quickly,
there will be a large percentage error in the measurement.
Success Criteria: Can describe how random uncertainties can
occur and how selecting a correct scale can minimise these.
Choosing the correct scale
When choosing the correct scale the reading can be
recorded to a higher level of accuracy (more significant
figures).
By choosing the smaller scale I am able to take
a measure to more significant figures.
Success Criteria: Can describe how systematic uncertainties can
occur and how a zero adjustment can minimise these.
Zero reading
A zero adjustment eliminates the systematic error produces
when a scale being used to measure a quantity does not
start at zero. As this is a systematic error every reading
taken will be out by the amount of this error.
The scale of the ammeter reads 0.3 A when there is no
current flowing, every measurement will be 0.3 A higher
than the actual value.
Success Criteria: Can describe how systematic uncertainties can
occur and how a parallax adjustment can minimise these.
Parallax error
When an object a distance from the scale it is being read
and the observer is not perpendicular to the scale a value
larger or smaller tan the true value is observed.
By viewing an object perpendicular to the scale
the error from parallax can be reduced.
Lining the needle up with it’s reflection
reduces the parallax error.
By lining up the eye horizontally in line with
the bottom of the meniscus the parallax error
is reduced.
Success Criteria: Can identify the correct number of Significant
figures.
Success Criteria: Can plot an accurate graph.
50
45
40
35
d (m)
30
25
20
15
10
5
0
0
1
2
3
4
5
6
7
8
9
10
When you plot your data
from an investigation the
points may form a straight
line. This means that the y
axis quantity is directly
proportional to the x axis
quantity or y  x .
t (s)
In the graph on above the x value, for every point, is 5 times
larger than the y value. This proportion is the same for all points,
so we say d (the y value) is directly proportional to t (the x value).
This constant proportion is equal to the gradient (m) of a graph
and is often called a constant.
Success Criteria: Can read trend lines to identify relationships
between the dependent and independent variable.
200
180
160
140
d (m)
120
100
80
60
40
20
0
0
1
2
3
4
5
6
7
8
9
10
More often that not the initial data
will produce a line that is a curve.
This data is not directly proportional.
However the data on the x axis can
be manipulated and a new graph
plotted that is a straight line.
In this case, the data on the x axis is
squared. A new graph is drawn where
y is plotted against x2 (t2). The data
produces a straight line showing that y
2
is proportional to x2.
y  x
d (m)
t (s)
200
180
160
140
120
100
80
60
40
20
0
0
10
20
30
40
50
t2 (s2)
60
70
80
90 100
Success Criteria: Use relationship to recalculate x axis and re-plot.
To achieve AS 2.1 there are four graph types you need to be able
to recognise and re-plot to obtain a straight line graph.
y x
Data is proportional.
y
1
x
Re-plot the graph using the
y data and x data inverted.
y x
2
Re-plot using the y data and x data
squared to get a straight line.
y
1
x
2
Re-plot using the y data and x
data squared then inverted.
Success Criteria: Can draw a line of best fit for a linear
relationship.
A line of best fit (LOBF) is drawn with a straight ruler so that the
data points are evenly weighted above and below the line. The
LOBF will not always go through the point 0,0.
Success Criteria: Work out the gradient of the graph.
When drawing a LOBF it is important to identify data points that
are outliers. This erroneous data is usually caused by mistakes
during the investigation
The gradient of a graph is
calculated by first selecting
two non-data points on the
LOBF at least ⅔ of the line
apart. From the points draw
vertical and horizontal lines
to intersect with the axis.
Find the change in y (∆y) between the two points
on the y axis and divide this value by the change in
x (∆x) from the x axis. This is the gradient (m)
m 
y
x
Success Criteria: Can write a graph equation from a straight line
graph.
y axis
quantity
y  mx  c
gradient
Intercept on
the y axis.
x axis quantity