Physics 2.1 AS Credits Carry out a practical physics investigation that leads to a non- linear mathematical relationship.

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. Success Criteria: Can derive more Units and standard form. Fundamental Quantity SymbolUnit Massmkg LengthL, l, d, x, hm TimeT, ts

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.

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. 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. 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. 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 x 2 (t 2 ). The data produces a straight line showing that y is proportional to x 2.

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. Data is proportional. Re-plot using the y data and x data squared to get a straight line. Re-plot the graph using the y data and x data inverted. Re-plot using the y data and x data squared then inverted. Success Criteria: Use relationship to recalculate x axis and re-plot.

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)

Success Criteria: Can write a graph equation from a straight line graph. Intercept on the y axis. y axis quantity gradientx axis quantity