Student Lab Handbook

8/17/2019 Student Lab Handbook

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Common Graph Forms in Physics

Working with graphs – interpreting, creating, and employing – is an essential skill in the sciences, andespecially in physics where relationships need to be derived. As an introductory physics student youshould be familiar with the typical forms of graphs that appear in physics. Below are a number oftypical physical relationships exhibited graphically using standard X-Y coordinates (e.g., nologarithmic, power, trigonometric, or inverse plots, etc.). Study the forms of the graphs carefully, and

be prepared to use the program Graphical Analysis to formulate relationships between variables byusing appropriate curve-fitting strategies. Note that all non-linear forms of graphs can be made toappear linear by “linearizing” the data. Linearization consists of such things as plotting X versus Y

2 or

X versus 1/Y or Y versus log (X), etc. Note: While a 5th

order polynomial might give you a better fit tothe data, it might not represent the simplest model.

X

0.800 1.20 1.60 2.00 2.40 2.80 3.20 3.60 4.00

Y

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.0

11.0

12.0

13.0

LINEAR RELATIONSHIP: What happens if you get a graphof data that looks like this? How does one relate the Xvariable to the Y variable? It’s simple, Y = A + BX where Bis the slope of the line and A is the Y-intercept. This ischaracteristic of Newton’s second law of motion and ofCharles’ law:

F = ma

P

T = const .

X

0 .0 0 0 .5 00 1 .0 0 1 .5 0 2 .0 0 2 .5 0 3 .0 0 3 .5 0 4 .0 0 4 .5 0 5 .0 0

0.00

0.400

0.800

1.20

1.60

2.00

2.40

2.80

3.20

3.60

4.00

4.40

4.80

5.20

Y

INVERSE RELATIONSHIP: This might be a graph of the pressure and temperature for a changing volume constanttemperature gas. How would you find this relationship shortof using a computer package? The answer is to simplify the plot by manipulating the data. Plot the Y variable versus theinverse of the X variable. The graph becomes a straight line.

The resulting formula will be Y = A/X or XY = A. This istypical of Boyle’s law:

PV = const .

X

0.400 0 .800 1.20 1.60 2.00 2.40 2.80 3.20 3.60 4. 00

Y

0.00

0.400

0.800

1.20

1.60

2.00

2.40

2.80

3.20

3.60

4.00

INVERSE-SQUARE RELATIONSHIP: Of the form Y= A/X2. Characteristic of Newton’s law of universalgravitation, and the electrostatic force law:

F =Gm

1m

2

r2

F =kq1q2

r2

In the latter two examples above there are only subtle differences in form. Many common graphforms in physics appear quite similar. Only be looking at the “RMSE” (root mean square error provided in Graphical Analysis) can one conclude whether one fit is better than another. The better fitis the one with the smaller RMSE. See page 2 for more examples of common graph forms in physics.


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DOUBLE-INVERSE RELATIONSHIP: Of the form 1/Y = 1/X+ 1/A. Most readily identified by the presence of an asymptotic boundary (y = A) within the graph. This form is characteristic ofthe thin lens and parallel resistance formulas.

1

f =

1

d i+

1

d o and

1

Rt

=

1

R1

+

1

R2

X

0 .0 0 0 .2 00 0 . 40 0 0 . 60 0 0 .8 00 1 .0 0 1 .2 0 1 .4 0 1 .6 0 1 .8 0 2 .0

Y

0.00

0.400

0.800

1.20

1.60

2.00

2.40

2.80

3.20

3.60

4.00

POWER RELATIONSHIP: Top opening parabola. Of the form Y= AX2. Typical of the distance-time relationship:

d =1

2at

2

POWER RELATIONSHIP: Side opening parabola. Of the formY2=A1X or Y=A2X

1/2. Typical of the simple pendulumrelationship:

P2= k

1l or P = k

2 l

X

0.00 0.400 0.800 1.20 1.60 2.00 2.40 2.80 3.20

Y

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.0

11.0

12.0

13.0

POLYNOMIAL OF THE SECOND DEGREE: Of the form Y =AX + BX2. Typical of the kinematics equation:

d = vot +

1

2at

2

X

0 .2 00 0 . 40 0 0 .6 00 0 .8 00 1 .0 0 1 .2 0 1 .4 0 1 .6 0 1 .8 0 2 .0 0

Y

1.20

1.60

2.00

2.40

2.80

3.20

3.60

4.00

4.40

4.80

5.20

5.60

6.00

6.40

6.80

7.20

7.60

EXPONENTIAL RELATIONSHIP: Of the form Y = A *exp(BX). Characteristic of exponential growth or decay. Graph toleft is exponential growth. The graph of exponential decay would

look not unlike that of the inverse relationship. Characteristic ofradioactive decay.

N = N oe"# t


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X

0 .5 00 1 .0 0 1 .5 0 2 .0 0 2 .5 0 3 .0 0 3 .5 0 4 .0 0 4 .5 0

Y

-0.800

-0.600

-0.400

-0.200

0.00

0.200

0.400

0.600

0.800

1.00

1.20

1.40

1.60

1.80

NATURAL LOG (LN) RELATIONSHIP: Of the formY = A ln(BX). Characteristic of entropy change during a freeexpansion:

S f ! S i = nR lnV f

V i


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Deriving Relationships from Graphs

Graphing is a powerful tool for representing and interpreting numerical data. In addition, graphs and

knowledge of algebra can be used to find the relationship between variables plotted on a scatter chart.Consider the following examples and procedures. Become familiar with these approaches, and attempt to

thoroughly understand the processes illustrated here. You will be using these approaches regularly inintroductory physics laboratory activities. Please note that this is merely an introduction to deriving

relationships from graphs. There ultimately will be much more to learn.

Linear Relationships:The graph to the left depicts a linear relationship. That is,

the data points all can be connected with a straight line. Note that the best-fit line does not pass through the origin;

rather, it passes through the y-axis at -1m. How would onedefine the relationship between the values of the x and y

coordinates in this relationship? It’s done with the equationof a straight line, y = m x + b where m is the slope and b is

the value of the y-intercept – the value of y when x = 0. Theslope, m, is found from ! y/! x = ( y2 - y1)/( x2 - x1) = 2m/s in

this graph. The value of b can be found from inspection asequal to -1m. The algebraic relationship is then represented

as y = m x + b where y is identified with the distance, x isidentified with time, m the slope, and b the y-intercept.

Thus, our relationship from the graph is:

distance = (2m/s)time – 1m.

Proportional Relationships:The graph to the left approximates the proportional

relationship between voltage and current in a circuit. Notethat a relationship is directly proportional only when the

relationship is linear and passes through the origin. This isnot quite the case in the current graph. The value of b is

0.0180 amps. The linear fit derived by the computer used togenerate the graph performs an algebraic best fit to the data.

The equation generated is the best mathematical fit, but thefit does not properly represent the physical reality of the

situation. If no voltage is applied, then no current flows. If this is the case, the best-fit line must pass through the origin

in what is known as a physical fit of the data. A physical fit

demands y = A x as the model for the fit. Under the newanalysis m = 2amps/volt. Then we find:

current = (2amps/volt )voltage

(continued)


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Non-linear Relationships:The graph to the left is a hyperbolic relationship. That is, the data

are not linear and both sets of values appear to approachasymptotes. There are two approaches that one might use to find

the relationship between the variables pressure and volume. Onecan perform a non-linear regression analysis to find the

relationship using a calculator or computer, or linearize the plot by

manipulating the data and re-plotting in an effort to create a linear relationship between the manipulated variables. Looking at thedata carefully suggests that we have here an inverse relationship.

That is, when pressure is doubled, the volume is halved. Therelationship between the variables would seem to be of the form

pressure is inversely proportional to volume. Assuming for amoment that this is the case, data for either pressure or volume can

be inverted (e.g., replace by 1/var) and the data re-graphed to see if a linear relationship results. Replacing volume by 1/volume results in the graph below.

Linearized Plot of Above Data:

Here we see a linearized plot of the pressure-volume data used tocreate the graph above. On the horizontal axis of the graph the

inverse of the volume has been plotted; pressure is plotted as before on the vertical axis. A linear fit of the type y = m x + b is

now possible and shows that a linear relationship exists between pressure and the inverse of the volume in this particular situation.

Note that the x-variable is identified with 1/volume, that the y-variable is identified with 1/volume, and that b = 0. That is,

pressure = (3atm*lit )/volume

or

pressure * volume = 3atm*lit

The “Simpler, More Natural” Fit:

Students are sometimes tempted to use a polynomial function toconduct a non-linear curve fit using calculators or computers (e.g.,

y = a + b x + c x2 + d x

3 + e x

4 + f x

5 +…). Such a function will fit

almost any data so long as the degree of the function is high

enough. What sometimes happens though (as did not happen in thegraph to the left) is that there will be wild migrations of the best-fit

curve between the data points that probably wouldn’t happen innature. Fortunately, Mother Nature is less complex than that, and

best fits involving trigonometric functions and the like can providesimpler forms of relationship. Note the 4

th order power function fit

of the graph to the left. A trigonometric function given by intensity= (2.5lux)cos(") is a “simpler” representation of the data and a

“more natural” fit with the data. As a rule, simpler interpretationsare valued over more complex in science.


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Graphs and Units of Slope and Y-intercept

After students linearize a graph, they know that they can find the relationship between the twovariables on the X- and Y-axes. They merely use the equation for a straight line y =mx + b where m is

slope and b the Y-intercept. But what are the units of the slope and Y-intercept? One answer is that theunits of the slope are the units of !y/!x and the units of the Y-intercept are the units of the Y-axis. The

next best way to answer this question is to use information provided by the graphing program Logger Pro.

Say that students are given the data to the left for time t (expressed in seconds, s) andvelocity v (expressed in meters per second, m/s). A graph is made plotting (t, v) and a right-opening parabola results. Logger Pro’s “Data: New Calculated Column…” tool is used to

square the velocity term. The data are then re-plotted (t, v2) and a linear relationship results

as is shown in the graph below.

Carefully examine the linear fit for the data set involving v-squared. (See the box withinthe graph.) Note carefully that the now linear relationship is given explicitly as v

2 = mt + b.

Note the fact that m (Slope) is given as 347.5.m2/s

2/s. The units can be simplified by using

the well-known relationship

a

b

c

d

=

ad

bc :

m2

s2

s

1

=

m2*1

s2*s

=

m2

s3

Now, b (the Y-intercept) is

given by Logger Pro as 76.00m2/s2.

The physical form of therelationship is then properly

written as follows, includingunits:

v2= 347.5

m2

s3 t +76.00

m2

s2

Note that when time is

inserted into the equation (say t= 4.500s) the units work out

properly for determining vwhich is expressed in m/s. That is,

v2

= 347.5m2

s3 (4.500s)+76.00

m2

s2

v2 =1564.m2

s2 +76.00

m2

s2

v2 =1640.m2

s2

v = v2 = 1640.m2

s2 = 40.50

m

s


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EXAMPLES OF COMMON GRAPH CONSTRUCTION ERRORS

Data collection errors. If you want straight-line data, collect only data for only two points; if reproducibility is a problem,

conduct only one test. A straight line can always be made to fit through two points. Clearly this is a wrong way to collect and

interpret data for, indeed, a line and, indeed, any curve can be made to pass through just two points. Observations should be

repeated for the sake of accuracy.

Confusing independent and dependent variables. The I ndependent variable is what I change.” The size of the Dependent

variable D

epends on the value of the independent variable.

Putting wrong variables on axes. For a matter of convenience, it is common practice to put the independent variable on the

horizontal x-axis (bottom) rather than the vertical y-axis (side) when seeking a relationship to define the dependent variable.

For instance, if one wants to arrive at a relationship describing F as a function of a, then F (the dependent variable) should be

plotted on the y-axis. The slope becomes the proportionality constant, F = ma.

Failure to understand the significance of “linearizing” data. When data are non-linear (not in a straight line when graphed),

it is best to “linearize” the data. This does not mean to fit the curved data points with a straight line. Rather, it means to modify

one of the variables in some manner such that when the data are graphed using this new data set, the resulting data points willappear to lie in a straight line. For instance, say the data appear to be an inverse function – as x is doubled, y is halved. To

linearize the data for such a function plot x versus 1/y. If this is indeed an inverse function, then the plot of x versus 1/y date

will be linear.

Failure to properly relate y = mx + b to the l inearized data. When plotting, say, distance (on the y-axis) versus time (on the

x-axis), the correct relationship between distance and time can be found by relating y to distance, x to time, m to the slope, and

b to the y-intercept. For instance, data have been linearized for the function resulting in a straight-line graph when distance is

plotted versus time-squared. The slope is 2m/s2 and the y-intercept is 1 m. The correct form of the relationship between all the

variables will be distance = (2m/s2)*time2 + 1 m.

Failure to apply the appropriate physical model to the data. When students have a scattering of data, they might at first betempted to fit the data with a linear model, y = mx + b. Using such a linear fit will result in an error if when y equals 0, x must

also be 0. For instance, if the force on a spring is 0, its extension from the equilibrium position will be zero. This is, no force,

no extension. Such a relationship expressed graphically MUST pass through the origin. Because of errors in the collected data,

a linear fit will almost never pass through the origin as it should. The way to solve this problem is to conduct a proportional fit,

y = mx. This forces b = 0 and assures that the best fit line will pass through the origin.

Using multiple (0,0) points to force best-fit line through origin. Some times students justifiably realize that a best-fit line fora straight-line relationship should pass through the origin (e.g., if force equals zero, then acceleration must also equal zero).

They then include multiple (0,0) points in the data set in an effort to force the best-fit line through the origin. This rarely works.

Better is to fit the linearized data with a proportional relationship (y = mx) in these cases rather than a linear relationship (y =

mx + b). The value of b (the y-intercept) is forced to be zero in the proportional fit.

Failure to apply appropriate labeling. Each graph should be appropriately labeled; each axis should be similarly labeled with

its variable and units (in parentheses). For instance, time (seconds) or distance (meters).

Confusing variables with units. Don’t confuse variables (time, resistance, distance) with their units (seconds, ohms, meters).

Connecting data points with straight lines. Never connect even linearized data point-to-point with straight lines. “Mother

nature is not jerky.” Linearized data sometimes appears not to fit the best-fit line precisely. This is frequently due to errors in

the data.

Scaling errors. In physics it is often best to show the origin (0,0) in each graph. While this is not an error, it frequently leads to

misinterpretations of relationships. Another problem to avoid is not sticking with a consistent scale on an axis. Values should be equally spaced on graphs. For instance, the distances between 1, 2, 3, and 4 should be the same. To be avoided are situations

where the spacing between such numbers is irregular or inconsistent.

Beware the high order polynomial f it. Mother Nature tends to be quite simple, but any 4 th or 5th order polynomial function

can be made to fit nearly any given set of data. Avoid fitting data with non-linear functions of above second order (e.g., y = a +

bx + cx2). Simple functions like x = constant / y or x = A*sin(!) are much more common.


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EXAMPLES OF COMMON GRAPH INTERPRETATION ERRORS

Failing to note that when the slope is zero (horizontal line), there is no relationship. Some times students think that a

relationship between two variables exists when, in fact, it does not. This often happens with auto-scaling computer graphing

programs where, for instance, the vertical axis runs from 0.05 to 0.06 and horizontal axis reads from 0 to 10. The 0.05-0.06

range of the vertical axis is automatically stretched out to take up the same linear space as the horizontal axis thus “filling the page.” In this case the actual relationship might be y = 0.0001x. Some falsely think that this constitutes a meaningful

relationship. It is an extremely weak relationship probably arising from measurement errors. By rescale each axis, the graph

clearly shows a line of essentially zero slope.

Failing to properly interpret horizontal and vertical slopes. When the slope of a line is zero degrees (horizontal), then there

is no relationship between the plotted variables. When the slope of a line is 90 degrees (vertical), there is no relationship

between the plotted variables. In both these cases, the so-called “dependent” variable does not actually depend upon the

independent variable.

Failing to properly interpret a negative slope. A negative slope does NOT imply an inverse relationship. What it does imply

is that when x gets larger, y becomes larger but in the negative direction. y = -mx is a negative slope. xy = m or x = m/y is an

inverse relationship. Additionally, a negative acceleration does not necessary imply a slowing down of an object. An objectmoving in the –x direction will, in fact, be increasing in speed.

Failing to properly interpret the y-intercept. The value of the y-intercept represents the value of the dependent variable

when the independent variable is zero. Sometimes when lines or curve representing relationships clearly should pass through

the origin (0, 0), they do not. The value of the y-intercept is then small but not zero. This is usually the result of data collection

errors. Linearized data can be “forced through the origin” by conducting a proportional fit of the data (y = mx) rather than a

linear fit (y = mx + b).

Finding slope using two data points NOT on best-fit line. Finding the slope of the best-fit line from two data points rather

than two points on the best-fit line is a common error. This can result in a substantial error in the slope. Use two points on the

best-fit line for this process, not data points to find the slope of a line.

Improperly finding the slope using elements of a single coordinate point. When finding the slope of a tangent line, students

sometimes will mistakenly take the coordinate point nearest where the tangent line is drawn and from those coordinates attempt

to determine the slope. For instance, at (x, y) = (5s, 25m) the data point values are mistakenly used to find a slope of 25m/5s =

5m/s.

Failing to recognize trends. Graphs must be properly interpreted through the use of generalizations (e.g., double the mass, andthe period increases by 4)

Improperly interpreting the physical meaning of the slope. The units of a slope are a give away as far as interpreting the

physical meaning of a slope is concerned. The slope is defined as the change in the y value divided by a corresponding change

in the x value. Hence, the units are those of the y-axis divided by those of the x-axis. If distance (m) is on the y-axis, and time(s) on the x-axis, then the slope’s units will be m/s which is the unit for speed. Similarly, if velocity (m/s) is on the y-axis and

time (s) is on the x-axis, then (m/s)/s = m/s2, the unit of acceleration. Slopes can be either positive or negative.

Misinterpreting the area under a best-fit line or curve. To more easily determine what the area under the curve represents,

examine the product of the units on the x- and y-axes. For instance, if velocity or speed (m/s) is on the y-axis and time (s) is on

the x-axis, the product of the units will be meters that is the unit of displacement or distance. Hence, the area under the curve of

a v-t graph is displacement. Similarly, the area under the curve of an a-t graph is velocity.

Not recognizing errors in data. When one graphs data, large errors in measurement usually stand out. They become

noticeable when they deviate from the trend of the data. Such data points should not only be questioned, but the data collection

process for this datum repeated to see if there is a readily explainable error. Suspect data should be struck through rather thanerased in the written record. Update the data file appropriately.

Not understanding the meaning of the best-fit line. A best-fit line is a line which, when drawn, minimizes the sum of the

squares of the deviations from the line usually in the y direction. It is the line that most closely approximates the trend of the

data.


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How Many Data Points are Enough?

Lilienthal’s rule: If you want to fit a straight-line to your data, be certain to collect only two data points. A straight line can always be made to fit through two data points.

Corollary: If you are not concerned with random error in your data collection process, justcollect three data points.

Corollary: If you can make measurements completely without error, you need collect only one setof data and never need to repeat any measurements.

Clearly these are wrong ways to collect and interpret data – for a line, and indeed any curve, can

be made to pass through just two points. Random errors in data collection can also cause problems with the interpretation of graphical data if the number of data points is too few. More

data will allow for the averaging out of random error.

How much data, then, should be collected before generating a graph? That’s a good question,and one that is rarely addressed adequately in science courses that have laboratory components.Perhaps it is best to provide some examples of inadequate data collection to show the problemsthat arise from using an inadequate number – and sometimes more importantly an inadequate

range – of data points.

Inadequate number of data points: Consider a task in which a student is asked to determine the

relationship between Fahrenheit (F) and Celsius (C) temperature scales using a dual-scalethermometer as the source of data. If only three data points are collected, then a best-fit equation

will be strongly biased by errors in the data. For instance, the student generates a table of Cversus F temperatures from the inspection of the C/F thermometer. The student notes that 0oC

corresponds precisely to 32

o

F and that 212

o

F corresponds precisely to 100

o

C. The student thenuses the ambient air temperature to see that 73oF corresponds to 23

oC approximately (actually

22.777…oC). A best-fit regression equation will result in a non-linear relationship due to this

reading error. If multiple data points are collected (say 6), then the errors tend to averagethemselves out and a linear relationship would be clearly evident. Hence, as a general rule, the

more data points the better – up to a point. Too many data points constitute needless effort and awaste of time.

It is often difficult to decide just how much data to collect, but here are some simple guidelines:if the relationship is expected to be linear, then fewer data points are necessary to arrive at the proper regression formula; if the relationship is expected to be non-linear, then more data should

be collected in the area where the curve is expected to rapidly change, and less data may becollected where the curve is expected to be more linear. For instance, in the relationship betweenthe period of a pendulum and its length, more data should be collected at shorter pendulumlengths and less data should be collected at longer pendulum lengths. Consider the following

example.

Inadequate range of data: Say that someone is attempting to determine the period a simple

pendulum – a weight suspended at the end of a string. Length-versus-period data are collected


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for a variation of length of only 1 meter starting at 1 meter and going out to 2 meters. The

following data are collected and graph generated.

Length (m) Period (s)

1 2.01.2 2.2

1.4 2.4

1.6 2.5

1.8 2.7

2.0 2.8

Because of slight random errors in data collection (which are to be expected), it at first appearsthat the data best fit a linear relationship. Even if a physical model (derived from dimensional

analysis) is suggested (e.g., the best fit relationship must pass through the origin), it is difficult toreconcile the present data that clearly appear not to pass through the origin. Question: What isthe problem? Answer: Not a wide enough range of data has been collected. Data should be

collected in a concentrated fashion for shorter lengths of the pendulum, whereas fewer data pointsneed to be collected at greater lengths of the pendulum. As a general rule, the maximum value of

the independent variable should be at least 5 (preferable 10) times the minimum value of theindependent variable used in making a graph.

Dimensional analysis will show that the expected period-length relationship for the simple pendulum is parabolic. If data are collected too far out along the parabola and not enough close

in, then that limited set of data clearly suggests a straight-line relationship. These data are

reasonably correct for the interval of length studied however. The problem associated withgenerating a more general solution of the period-length relationship lies in not collecting a wideenough range of data to represent the phenomenon accurately over a wide range. For this reason,

extrapolation is not generally merited beyond the range of the data.

Inadequate separation of data points: Consider another variation of this problem – inadequateseparation of data points. A student attempts to determine the relationship between the period of

a torsion pendulum and its moment of inertia. The student varies the moment of inertia in smallincrements and measures the resulting period. This results in the following data set:

Moment of Inertia (kg m ) Period (s)

.060 .15

.061 .17

.062 .16

.063 .18

Note the lack of a trend in the data. While the moment of inertia appears to go up uniformly, the

period is more or less random. This apparent randomness results from small measurement errorsthat, while they may be small in absolute terms, are large in relative terms. (See relative error


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and absolute error.) The problem can be overcome by collecting data points that are separated

to a much greater degree. That is, the student should attempt to vary to moment of inertia of thetorsion pendulum by increasing the moment of inertial not in tiny steps, but in much larger steps.

Errors in data collection: Repeated measures will help to minimize the amount of error in

an observation and of relationships derived from graphs associated with those data. Allwell-done human measurements include some randomized error. Such error results from

the limited ability of humans to make measurements. Using a stopwatch, for instance,results in data that can easily be “off” 0.2 seconds or more due to the lack of instantaneous

hand-eye coordination. Besides, knowing just when to start and stop a stopwatch can bevariously interpreted. Even the use of precise scientific instruments will result in

measurements that contain errors, albeit smaller than those conducted with humanintervention.

Most data collection that students will do in a simple laboratory setting might be fruitfully

completed using a number of observations of the same phenomenon, and then taking the average.

An average will help to find the central tendency of a series of measurement around the centralvalue and will result in a more precise indication of the actual value of a repeatable phenomenonsuch as the period of a swinging pendulum.

To what extent students make repeated measures will depend on a number of factors such as the

use of precise scientific instruments, the degree of human interaction, the amounts of relative andabsolute errors, how low it takes to repeat an observation, the degree of precision needed, time

allocated for a particular laboratory activity, and so on. In a classroom situation the teacher is best suited to making this judgment.

A general rule for repeated measures: If the precision of the measurement is high, the number of

repeated measurements can be low; if the precision of the measurement is low, the number ofrepeated measurements should be high.

So, as long as no human blunders are made, an acoustical motion detected can be used once for

measuring the motion of an object. The use of a stopwatch, on the other hand, is subject tosubstantial error and repeated measures should be made if assessing motion. The average of the

repeated measures is then reported and used for the development of a graph if appropriate.

How many data points are enough?: As an answer to the initial question, a simple and fast rulefor introductory labs would be to collect 6 data points minimum. This is, admittedly, an

arbitrary number but one that seems to work well in the introductory setting. The selection ofthe data points must take into consideration the following:

• If the accuracy of the measurements is low, each data point used to make a graph shouldconsist of a number of observations that have been averaged.

• The maximum value of the independent variable should be at least 5 time (10 times is better) its minimum value.

• If the function is expected to change rapidly, then data collection should concentrate inthose regions of a graph where the data are expected to vary most quickly.


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Interpreting Slopes, Areas, and Intercepts of Graphs

Sometimes slopes, areas, and intercepts in graphs have physical interpretations. At other times they haveno real meaning at all. Consider how one can interpret meaningful terms in the following graph.

Here we have a linear relationship between velocity

and time. The slope of the graph can be found from

the relationship

m =" y

" x=

y2# y

1

x2# x

1

Because this is a linear relationship, arbitrarilychoosing two pairs of ( x, y) coordinates on the line

and performing the calculation results in a slope of1m/s

2. The slope represents the change in velocity

with time which is nothing other than acceleration. Note that the calculated slope units are those of

acceleration.

What does the area under the curve represent, say,

from time equals 1 s to time equals 5 s? Consideradding up a series of very narrow vertical columns to find total area. The height of each vertical column is

velocity, v, at a particular time and the width of each column is !t. The product of these terms is v!t

which equals a distance. Hence the sum total of these rectangles giving the area under the curve duringthe specified time interval is the distance that the object has traveled from time 1 s to 5 s. Can you see how

the distance the object travels between t = 1 s and t = 5 s is 32m? You can readily see this by adding thearea of the rectangle below and to the right of the point (1, 6) (area = height x width = (6m/s)(4 s) = 24m)

to the triangle to the upper right of this point (area = 0.5 height x base = 0.5(4m/s)(4 s) = 8m). Note the24m + 8m = 32m.

The y-intercept can also be readily interpreted in the above graph. It is merely the value of the y term at

time = 0 s. That is, the y-intercept is 5m/s, the velocity of the object at t = 0 s.

Not all graphs are so readily interpreted. Some graph’s slopes, areas, and intercepts might well bemeaningless. Consider the following examples, not all of which are meaningful. Can you tell which is

which?

What does the area under the curve of an acceleration-versus-time graph represent?

What does the slope of an acceleration-versus-time graph represent?

What does the y-intercept of an acceleration-versus-time graph represent?

What does the area under the curve of a displacement-versus-time graph represent?

What does the slope of a displacement-versus-time graph represent?

What does the y-intercept of a displacement-versus-time graph represent?


13/55

Finding the Displacement of a Uniformly Accelerated Object(a = constant, including 0)

Graph 1 Graph 2

An object moves with a uniformly accelerated motion and has a v(t) = (-6.25m / s)!t + 25m / s as shown in Graph 1. The

area under the line in this object’s velocity-time graph is equal to its displacement (!x). There are two simple methods (not

involving calculus) that we can use to arrive at the value of the displacement:

1)

Use kinematics:

From kinematics we know that !x = v!t which, for a uniformly accelerating body, is the same as !x =1

2(v

i + v

f )!t .

From Graph 1 we have !x =1

225m/s+0m/ s( )(4s"0s) =

1

2(25m / s)(4s) = 50m

2) Use geometry and the graph:

From the formula for area of a triangle we know that !x =1

2hb =

1

225m/s"0m/s( )(4s"0s) =

1

2(25m / s)(4s) = 50m

While the result is the same in each of the above approaches, how does one reconcile the obvious difference in the +

and – signs on the 0m/s terms? This is a special case due to the fact that the base of the triangle is the X-axis. Consider the

more general situation where the base of the displacement triangle is not the X-axis. This can be seen in Graph 2 where the Y-intercept is 30m/s and the velocity is 5m/s at 4s. The slope is the same; only the Y-intercept is greater by 5m/s.

Now, the area under the line in Graph 2 is the sum of two areas, the area of the triangle plus the area of the rectangle

below it. Consider again the geometric solution:

displacement = area under triangle + area of rectangle

!x =1

2h

t b+ h

r b

!x =1

2v

i " v

f ( )!t + vf !t (3)

!x =1

2v

i

!t " 1

2v

f

!t + vf

!t

!x =1

2v

i!t +

1

2v

f !t

!x =1

2(v

i + v

f )!t

When there is no rectangle under the triangle (e.g., vf = 0 ), the solution in line 3 above reduces to !x =

1

2(v

i " v

f )!t . So,

the solutions – those with the +0m/s and the –0m/s in methods 1 and 2 above – are both legitimate. The form of

!x =1

2(v

i + v

f )!t is the more general, and includes the rectangular area under the triangle if any.


14/55

This approach is fine of the “motion” is restricted to the first Cartesian quadrant, but what happens if the motion “enters”

the fourth Cartesian quadrant as shown in Graph 3 and we have displacement area below the X-axis?

Graph 3 Graph 4

The equation of motion is the same as in Graph 2, v(t) = !6.25m

s2

"

#$

%

&'(t +30

m

s, but we have extended the time interval to

6s as shown in Graph 3. The displacements above and below the X-axis are shown in Graph 4.

What is the displacement between, say, 0s and 6s? Notice that v = 0m/s when t = 4.8s. Note too that the displacement

following 4.8s is negative (the object is moving with a negative velocity) while the area of a triangle is always positive. Again,

there are two methods to calculate the displacement over the entire interval.

1) Use kinematics:

From kinematics we know that !x = vt which, for a uniformly accelerating body, is the same as !x =1

2(v

i + v

f )!t .

From Graph 3 we have !x =1

230m/s"7.5m / s( )(6s"0s) = 67.5m

2) Use geometry and the graph to sum the displacements represented by the two triangles shown in Graph 4:

From the formula for area of a triangle (whose sides are considered positive and whose displacements can be either

positive or negative) we know that displacement in this case is the area of triangle 1 – area of triangle 2 (because the object

is moving with a negative velocity after 4.8s).

!x =1

2h

1 b

1 " 1

2h

2 b

2

!x =1

2v

i " v

f ( )1 (!t) " 1

2v

i " v

f ( )2 (!t)

!x =1

230

m

s"0

m

s

#

$%

&

'(1

(4.8s"0s)" 1

20m

s""7.5

m

s

#

$%

&

'(2

(6s" 4.8s)

!x = 12

30ms

#$% &

'((4.8s)" 1

27.5m

s#$% &

'((1.2s)

!x = 72m " 4.5m

!x = 67.5m

The conclusion that we can draw from this analysis is that !x =1

2(v

i + v

f )!t is a less confusing, more convenient, and

less-error-prone way of calculating displacement for accelerated motion even if initial or final velocities are less than zero.


15/55

Physical Interpretations and Graphical Analysis

Creating realistic models from data requires more than a blind “best fit” strategy. A physical model needs to be

created from an algebraic model if the new model is to have any useful meaning. For instance, let’s say that an

experimenter measures the circumferences of a number of circular disks, as well as the diameters of these disks.

There will be measurement error associated with each determination of circumference and diameter. A graph of

circumference versus diameter produces the following result with the use of Graphical Analysis:

Note that an algebraic relationship results from a linear fit (y = mx + b) that is of the following form:

y = 3.15 x " 0.867mm

Identifying y with circumference, C , and x with the diameter, D, the equation translates to the following form:

C = 3.15" D# 0.867mm

This relationship suggests that if diameter equals zero, the circumference would have some negative value. This

clearly is incorrect in our physical world. If D = 0, then C must equal 0. The way to resolve is problem is to create a

physical model. Clearly, the relationship is linear, but the regression line must pass through the origin (0,0).

A physical model can be created by forcing the regression line to pass through the origin. This is NOT done by

including the data point 0,0; it IS done by using a different regression model, a proportionality (y = Ax), for the

curve fit. Such a curve fit produces a different equation that we would call a physical model.

C = 3.14 D

or more commonly

C = " D = 2" r

where r is the radius of the circle. This physical model is not weighed down by the non-zero y intercept. When

interpreting graphical results in lab, be certain to use an interpretation that corresponds to the physical world as we

know it.


16/55

Preparing Graphs: Requirements for Introductory Physics Lab Activities

How NOT to Prepare a Graph

1.

Point protectors missing.

2. Connecting data points with lines.

3.

Regression line shown with data point line.4.

Multiple (0, 0) data points in attempt to “force” regression line through origin.5. No column labels on data or graph.

6. No units on data or graph.

7.

No label on graph.

8. Label does not include student’s name.9. Provides algebraic interpretation of data, not physical (shown here in Notes box)

10.

Regression formula missing units on constants.

Other concerns:

11. Failure to linearize data to get a straight-line relationship unless otherwise called

for.12.

Printing graph without data table on same page.13. Failure to print use a landscape view.

14. Wasting paper and printer toner.


17/55

How to Prepare a Graph

1. Point protectors clearly evident.2. Not connecting data points with lines.

3.

Regression line shown alone.

4. No (0, 0) data points; uses proportionality for regression line.

5.

Column labels clearly evident on data and graph.6.

Units clearly evident on data or graph.

7. Label on graph.

8.

Label includes student’s name.

9. Provides physical interpretation of data, not algebraic (show here in Notessection).

10.

Regression formula includes units on constants.

Other pointers:11.

Linearize data to get a straight-line relationship unless otherwise called for.

12. Print graph with data table on same page.

13.

Print using a landscape view, avoid using portrait view.14. Wasting paper and printer toner.


18/55

Absolute and Relative Error

Absolute and relative error are two types of error with which every experimental scientist should

be familiar. The differences are important.

Absolute Error: Absolute error is the amount of physical error in a measurement, period. Let’s

say a meter stick is used to measure a given distance. The error is rather hastily made, but it is

good to ±1mm . This is the absolute error of the measurement. That is,

absolute error = ±1mm (0.001m).

In terms common to Error Propagation

absolute error =" x

where x is any variable.

Relative Error: Relative error gives an indication of how good a measurement is relative to the

size of the thing being measured. Let’s say that two students measure two objects with a meter

stick. One student measures the height of a room and gets a value of 3.215 meters ±1mm

(0.001m). Another student measures the height of a small cylinder and measures 0.075 meters

±1mm (0.001m). Clearly, the overall accuracy of the ceiling height is much better than that of

the 7.5 cm cylinder. The comparative accuracy of these measurements can be determined by

looking at their relative errors.

relative error =absolute error

value of thing measured

or in terms common to Error Propagation

relative error =" x

x

where x is any variable. Now, in our example,

relative errorceiling height =0.001m

3.125m•100 = 0.0003%

relativeerrorcylinder height =0.001m

0.075m•100 = 0.01%

Clearly, the relative error in the ceiling height is considerably smaller than the relative error in the

cylinder height even though the amount of absolute error is the same in each case.


19/55

Chi-Square Test for Goodness of Fit(after Applied Statistics by Hinkle/Wiersma/Jurs)

Scientists will often use the Chi-square (

" 2) test to determine the goodness of fit between theoretical

and experimental data. In this test, we compare observed values with theoretical or expected values.Observed values are those that the researcher obtains empirically through direct observation; theoretical

or expected values are developed on the basis of some hypothesis. For example, in 200 flips of a coin, onewould expect 100 heads and 100 tails. But what if 92 heads and 108 tails are observed? Would we reject

the hypothesis that the coin is fair? Or would we attribute the difference between observed and expectedfrequencies to random fluctuation?

Consider another example. Suppose we hypothesize that we have an unbiased six-sided die. To testthis hypothesis, we roll the die 300 times and observe the frequency of occurrence of each of the faces.

Because we hypothesized that the die is unbiased, we expect that the number on each face will occur 50times. However, suppose we observe frequencies of occurrence as follows:

Face value Occurrence

1 422 55

3 384 57

5 646 44

Again, what would we conclude? Is the die biased, or do we attribute the difference to random

fluctuation?Consider a third example. The president of a major university hypothesizes that at least 90 percent of

the teaching and research faculty will favor a new university policy on consulting with private and publicagencies within the state. Thus, for a random sample of 200 faculty members, the president would expect

0.90 x 200 = 180 to favor the new policy and 0.10 x 200 = 20 to oppose it. Suppose, however, for thissample, 168 faculty members favor the new policy and 32 oppose it. Is the difference between observed

and expected frequencies sufficient to reject the president's hypothesis that 90 percent would favor the policy? Or would the differences be attributed to chance fluctuation?

Lastly, consider an experimental result where, for given independent values of “X,” the followingtheoretical (expected) and experimental (observed) dependent values of Y were found:

X Ytheoretical Yexperimental 3.45 11.90 11.37

4.12 16.97 17.024.73 22.37 23.78

5.23 27.35 26.136.01 36.12 35.96

6.82 46.51 45.227.26 52.71 53.10

In each of these examples, the test statistic for comparing observed and expected frequencies is " 2,

defined as follows:

" 2=

(O# E )2

E i=1

k

$

where


20/55

O = observed value

E = expected valuek = number of categories, groupings, or possible outcomes

The calculations of " 2for each of the three examples, using the above formula, are found in Tables 1-4.

TABLE 1. Calculation of " 2 for the Coin-Toss Example

Face O E O-E (O-E)2 (O-E)

2/E

Heads 92 100 -8 64 .64

Tails 108 100 +8 64 .64

Totals 200 200 0 -- 1.28 = " 2

TABLE 2. Calculation of " 2 for the Die Example

Face Value O E O-E (O-E)2 (O-E)

2/E

1 42 50 -8 64 1.28

2 55 50 5 25 .50

3 38 50 -12 144 2.88

4 57 50 7 49 .98

5 64 50 14 196 3.92

6 44 50 -6 36 .72

Totals 300 300 0 -- 10.28=

" 2

TABLE 3. Calculation of " 2 for the Consulting-Policy Example

Disposition O E O-E (O-E) (O-E) /E

Favor 168 180 -12 144 .80

Oppose 32 20 +12 144 7.20

Totals 200 200 0 -- 8.00=

" 2

TABLE 4. Calculation of " 2 for the Experiment Example

X O E O-E (O-E)2 (O-E)

2/E

3.45 11.37 11.90 -.53 .28 .02

4.12 17.02 16.97 .05 .02 .00

4.73 23.78 22.37 1.41 1.99 .09

5.23 26.13 27.35 -1.22 1.49 .05

6.01 35.96 36.12 -.16 .03 .00

6.82 45.22 46.51 -1.29 1.66 .04

7.26 53.10 52.71 .39 .15 .00

Totals 212.58 213.93 -1.35 5.62 0.20 = " 2

There is a family of " 2 distributions, each a function of the degrees of freedom associated with the

number of categories in the sample data. Only a single degree of freedom (df) value is required to identify

the specific " 2distribution. Notice that all values of "

2are positive, ranging from zero to infinity.

Consider the degrees of freedom for each of the above examples. In the coin example, note that the

expected frequencies in each of the two categories (heads or tails) are not independent. To obtain theexpected frequency of tails (100), we need only to subtract the expected frequency of heads (100) from

the total frequency (200), or 200 - 100 = 100. Similarly, for the example of the new consulting policy, theexpected number of faculty members who oppose it (20) can be found by subtracting the expected

number who support it (180) from the total number in the sample (200), or 200 – 180 = 20. Thus, given


21/55

the expected frequency in one of the categories, the expected frequency in the other is readily determined.In other words, only the expected frequency in one of the two categories is free to vary; that is, there is

only 1 degree of freedom associated with these examples. For the die example, there are six possible categories of outcomes: the occurrence of the six faces.

Under the assumption that the die is fair, we would expect that the frequency of occurrence of each of thesix faces of the die would be 50. Note again that the expected frequencies in each of these categories are

not independent. Once the expected frequency for five of the categories is known, the expected frequency

of the sixth category is uniquely determined, since the total frequency equals 300. Thus, only the expectedfrequencies in five of the six categories are free to vary; there are only 5 degrees of freedom associatedwith this example.

In the last example dealing with the data from the experiment, the cross-tabulation table has 2columns and 7 rows. The degrees of freedom for such tables is (# rows – 1)(# columns – 1) = (6)(1) = 6

The Critical Values for the " 2 Distribution

The use of the " 2 distribution in hypothesis testing is analogous to the use of the t and F distributions.

A null hypothesis is stated, a test statistic is computed, the observed value of the test statistic is compared

to the critical value, and a decision is made whether or not to reject the null hypothesis. For the coinexample, the null hypothesis is that the frequency of heads is equal to the frequency of tails. For the die

example, the null hypothesis is that the frequency of occurrence of each of the six faces is the same. Ingeneral, it is not a requirement for the categories to have equal expected frequencies. For instance, in the

example of the new consulting policy, the null hypothesis is that 90 percent of the faculty will support thenew policy and 10 percent will not.

The critical values of " 2 for 1 through 30 degrees of freedom are found in Table 5. Three different

percentile points in each distribution are given – ! = .10, ! = .05, and ! = .01. (e.g., chances of 10%, 5%,and 1% respectively of rejecting the null hypothesis when it should be retained). For the coin and

consulting-policy examples, the critical values of " 2 for 1 degree of freedom, with ! = .05 and ! = .01,

are 3.841 and 6.635, respectively. For the die example, the corresponding critical values of " 2 for 5

degrees of freedom are 11.070 and 15.086. Although Table 5 is sufficient for many research settings in

the sciences, there are some situations in which the degrees of freedom associated with a " 2

test aregreater than 30. These situations are not addressed here.

Now that we have seen the table of critical values for the " 2 distribution, we can complete the

examples. For the coin example, the null hypothesis is that the frequency of heads equals the frequency oftails. As we mentioned, because there are only two categories, once the expected value of the first

category is determined, the second is uniquely determined. Thus, there is only 1 degree of freedom

associated with this example. Assuming that the ! = .05 level of significance is used in testing this null

hypothesis, the critical value of " 2 (

" CV

2 ) is 3.841 (see Table 5). Notice that, in Table 1, the calculated

value of " 2 is 1.28. Because the calculated value does not exceed the critical value, the null hypothesis

(the coin is fair) is not rejected; the differences between observed and expected frequencies are

attributable to chance fluctuation. That is, when the calculated value of " 2

exceeds the critical value, thedata support the belief that a significant difference exists between expected and actual values.For the example of the new consulting policy, the null hypothesis is that 90 percent of the faculty

would support it and 10 percent would not. Again, because there are only two categories, there is 1 degree

of freedom associated with the test of this hypothesis. Thus, assuming ! = .05, the " CV

2 is 3.841. From

Table 3, we see that the calculated value of " 2 is 8.00; therefore, the null hypothesis (that there is no

difference) is rejected. The conclusion is that the percentage of faculty supporting the new consulting

policy is not 90.


22/55

For the die example, the null hypothesis is that the frequency of occurrence of each of the six faces isthe same. With six categories, there are 5 degrees of freedom associated with the test of this hypothesis;

the " CV

2 for ! = .05 is 11.070. Using the data from Table 2, " 2 = 10.28. Because this calculated value is

less than the critical value, the null hypothesis is retained. The conclusion is that the differences betweenthe observed and expected frequencies in each of the six categories are attributable to chance fluctuation.

For the example with the experiment, the null hypothesis is that there is no difference betweentheoretical and experimental results. With seven data pairs there are 6 degrees of freedom associated with

the test of this hypothesis; the " CV

2 for ! = .05 is 12.592. Because this calculated value of " 2 (0.20) is

less than the critical value (12.592), the null hypothesis is retained. The conclusion is that the differences between the observed and expected values in each of the seven data pairs are attributable to chance

fluctuation. The experimental results are therefore consistent with the theoretical results to with a 95%chance of probability.

TABLE 5. CRITICAL

" 2 VALUES FOR UP TO 30 DEGREES OF FREEDOM

df

12

345

678

9

10

111213

1415

161718

1920

21

2223

2425

2627

2829

30

= .10

2.7064.605

6.2517.7799.236

10.64512.017

13.36214.68415.987

17.275

18.549

19.81221.06422.307

23.54224.769

25.98927.204

28.412

29.61530.813

32.00733.19634.382

35.56336.741

37.91639.08740.256

= .05

3.8415.991

7.8159.48811.070

12.59214.067

15.50716.91918.307

19.675

21.026

22.36223.68524.996

26.29627.587

28.86930.144

31.410

32.67133.924

35.17236.41537.652

38.88540.113

41.33742.55743.773

= .01

6.6359.210

11.34513.27715.086

16.81218.475

20.09021.66623.209

24.725

26.217

27.68829.14130.578

32.00033.409

34.80536.191

37.566

38.93240.289

41.63842.98044.314

45.64246.963

43.27849.55850.892


23/55

Conversion Factors

The use of conversion factors will not be confusing so long as certain rules are adhered to rigidly.These rules can best be shown with the use of examples:

Problem: Convert 3 (assumed an integer) inches (in) to millimeters (mm).

The conversion factor is 1in = 25.4mm precisely. This can be rewritten in two ways:

1in

25.4mm=1 or

25.4mm

1in=1

Now, we can multiply any quantity by 1 without changing its essential value. We can multiply 3inches by 1 and still have 3 inches. If we choose to rewrite 1 in one of the above forms, we canconvert a distance in one system of measure (British) to that of another (metric). The numericalvalue will not be the same because the unit of measurement will change. However, the actual distancewill be the same in either system of measurement. The question is, which one of the above two waysshould we choose to write 1 so as to successfully convert from one system of measurement toanother?

If we choose to write 1 in the first way, we have:

3in = 3in "1= 3in " 1in

25.4mm=

0.118in2

mm

The result is in mixed units that have no direct physical meaning and is a combination of both metricand British systems. If we choose to write 1 in the second way, we have:

3in = 3in "1= 3in " 25.4mm

1in= 76.2mm

Note that the inches unit has canceled; the result is purely metric.

What happens if one needs to convert, say, cubic centimeters (cm3) into cubic meters (m3)? Consider the following problem.

Problem: Convert 300cm3 into cubic meters.

The conversion factor is 100cm = 1 meter. Now, one cannot merely multiply 300cm3 by 1m/100cm

to arrive at the answer. If this were done, the answer would come out in cm2 x m and, again, mixed

units. To handle this sort of complication, we must rewrite 1 as 13

. One cubed is the same as one. For example:

300cm3= 300cm

3"1

3= 300cm

3" (

1m

100cm)3

= 300cm3"

1m3

1"106cm

3=

300m3

106

= 3 "10#4m

3

When using conversion factors, always pay meticulous attention to the units and their associated powers. In doing so, you need never make an error in converting from one system to another.


24/55

Dimensional Analysis

Scientists sometimes will use a process called dimensional analysis to predict the form of the relationship between

variables in a system. While this does NOT constitute a theory base, it does give scientists a better understanding of

what to expect experimentally, and helps them to design scientific experiments. Carefully observe the following

process of dimensional analysis for a cart accelerating down a fixed inclined plane.

Let’s say that a scientist observes the motion of the cart on the fixed incline, and after conducting a few qualitativeexperiments concludes that the distance of the cart is a function of its acceleration and the time. Assume that the

cart accelerates from rest when the stopwatch starts. What is the expected form of the relationship between

distance, acceleration, and time?

The form of this relationship can be predicted using an approach known as dimensional analysis. This process is

based on the knowledge that the distance (d ) is a function of the acceleration (a) and the time (t ). That is,

d = f (a,t )

Now, if d (expressed in meters, m) is related to both a (expressed in meters per second squared, m/s2) and t

(expressed in seconds, s) in some form, then the only thing missing is a proportionality constant and the powers of

the variable terms. Write a proportionality applying power terms x and y to a and t respectively Replace variables by units and solve for the power terms as, in this case, two simultaneous equations with two unknowns. Working

backward, then find the form of the equation and insert proportionality constant. (Note that if the unit of time, s, is

present on the right side of the equation, it must also be present on the left side of the equation. Place s0 on the left

side of the equation in step two below as shown; s0 is equal to 1 and done not affect the equality.)

d "a x t y

replacing variables with units

m1s0 " m

s2

#

$ %

&

' (

x

sy

simplifying

m1s

0 "m xs y)2 x

equating exponents on m and s

1= x

0 = y )2 x

and solving simultaneous equations

y =2 x

hence, y =2

thus,d "a1t 2

or, d = kat 2

Experiments can be conducted or theoretical work performed to find the value of the constant, k . In reality, k equals

1/2 giving the familiar kinematic equation

d =1

2at

2


25/55

Errors, Accuracy, and Precision

In making physical measurements, one needs to keep in mind that measurements are not

completely accurate. Each measurement will have some number of significant figures and should

also have some indication as to how much we can “trust” it (i.e. error bars). Thus in order to

reliably interpret experimental data, we need to have some idea as to the nature of the “errors”

associated with the measurements. There are whole text books devoted to error analysis. We will

not even attempt to be complete in our discussion of the topic, rather we will present some basic

ideas to assist you in analyzing your data.

When dealing with error analysis, it is a good idea to know what we really mean by error. To

begin with, let’s talk about what error is not. Error is not a blunder such as forgetting to put the

decimal point in right place, using the wrong units, transposing numbers, or any other silly

mistake. Error is not your lab partner breaking your equipment. Error isn’t even the difference

between your own measurement and some generally accepted value. (That is a discrepancy.)

Accepted values also have errors associated with them; they are just better measurements than

you will be able to make in a three-hour undergraduate physics lab. What we really mean by

error has to do with uncertainty in measurements. Not everyone in lab will come up with the

same measurements that you do and yet (with some obvious exceptions due to blunders) we may

not give preference to one person’s results over another’s. Thus we need to classify types of

errors.

Generally speaking, there are two types of errors: 1) systematic errors and 2) random errors.

Systematic errors are errors that are constant and always of the same sign and thus may not be

reduced by averaging over a lot of data. Examples of systematic errors would be time

measurements by a clock that runs too fast or slow, distance measurements by an inaccurately

marked meter stick, current measurements by inaccurately calibrated ammeters, etc. Generally

speaking, systematic errors are hard to identify with a single experiment. In cases where it is

important, systematic errors may be isolated by performing experiments using different

procedures and comparing results. If the procedures are truly different, the systematic errors

should also be different and hopefully easily identified. An experiment that has very smallsystematic errors is said to have a high degree of accuracy.

Random errors are a whole different bag. These errors are produced by any one of a number of

unpredictable and unknown variations in the experiment. Examples might include fluctuations in

room temperature, fluctuations in line voltage, mechanical vibrations, cosmic rays, etc.

Experiments with very small random errors are said to have a high degree of precision.

Since random errors produce variations both above and below some average value, we may

generally quantify their significance using statistical techniques.


26/55

Propagation of Errors

Let’s say that you have experimentally measured two quantities that will be used to determine a

third quantity. There are errors in each of the measured quantities. How does the error in each of

these two measured quantities affect the value of the calculated quantity?

Assume for the sake of the discussion that we use a pendulum to indirectly measure theacceleration due to gravity according to the hypothetical relationship

t = 2" l

g

The length of the pendulum, l , and the period of oscillation, t , are measured. In the process of

determining the local value of g , there is an error in the measurement of the length of the

pendulum !l. Similarly, assume an error in the measured period equal to !t. How do these errors

in length and period affect the resulting value of g ? Consider the following process of analyzing

error propagation. Given the above relationship that has reformulated to eliminate the square root

and terms in the denominator, start by substituting g + ! g for g , t + !t for t , and l + !l for l .

gt 2= 4"

2l

(g+"g)(t +"t )2 = 4# 2(l +"l)

(g+"g)(t 2+2t "t +"t

2) = 4#

2(l+"l)

gt 2 +2gt "t + g"t 2 +"gt 2 +2t "g"t +"g"t 2 = 4# 2l + 4# 2"l

Eliminating terms in double and triple ! (assumed


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1) Most of the time it is best to simplify a relationship before making the error term

substitutions. Eliminating square roots and terms in the denominator will make the task of

finding the error term much simpler. For instance, consider the following range equation

with error terms in s, v, h, and g . Convert the equation before beginning the required

substitutions:

s = v " 2h

g becomes s

2g = 2v

2h then

(s+"s)2(g+"g) = 2(v +"v)2(h +"h)

2) Pay close attention to substitutions that can be made by manipulating the simplified

original equation obtained from following the previous hint (e.g. 2s = gt 2). The reason

for these substitutions is to eliminate “like” terms from both sides of the equation. This

allows one to find terms that commonly appear in error propagation equations. In the

example immediately above, this would mean the following:

"v

v

,"s

s

,"g

g, and

"h

h

It should be noted that with knowledge of partial differential equations, equations for absolute

and relative error can be much more easily derived (especially with complex equations).


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Percent Difference – Percent Error

Sometimes scientists will want to compare their results with those of others, or with a

theoretically derived prediction. Each of these types of comparisons call for a different type of

analysis, percent difference and percent error respectively.

Percent Difference: Applied when comparing two experimental quantities, E1 and E2, neither

of which can be considered the “correct” value. The percent difference is the absolute value of the

difference over the mean times 100.

% Difference = E

1" E

2

1

2 E

1+ E

2( )•100

Percent Error: Applied when comparing an experimental quantity, E , with a theoretical

quantity, T , which is considered the “correct” value. The percent error is the absolute value of the

difference divided by the “correct” value times 100.

% Error =T " E

T •100


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Scientific Notation

Scientific notation is a form of “short hand” for writing long numbers. In various fields ofscience, very large and very small numbers are the norm. For instance, the mass of the sun is

1,988,920,000,000,000,000,000,000,000,000 kilograms

and the mass of an electron is

0.000000000000000000000000000000910938 kilograms

There are shorthand ways of writing these numbers. Using scientific notation, the mass of the

sun is 1.98892 * 1030 kilograms and the mass of an electron is 9.10938 * 10-31 kilograms.

Not that 1 = 100; 10 = 10

1; 100 = 10

2; 1,000 = 10

3; 10,000 = 10

4 and so on.

3,208 = 3.208 thousand = 3.208 * 103

Likewise 0.1 = 1/10 = 10-1

; 0.01 = 1/100 = 10-2

; 0.001 = 1/1,000 = 10-3

; 0.0001 = 1/10,000 = 10-4

and so on.

0.00045 = 4.5 * .00001 = 4.5 * 1/10,000 = 4.5 * 1/10-4

= 4.5 * 10-4

Even scientific calculators cannot deal with long strings of zeros such as those shown above.Still, they can easily deal with scientific numbers (and follow all the next rules accurately).

There are rules for working with exponents that are imbedded in scientific calculators:

a * 10 b multiplied by c * 10d = ac * 10 b+c

For example, 4 * 103 multiplied by 6 * 10

4 = 24 * 10

7 = 2.4 x 10

8

a * 10 b

divided by c * 10d = a/c * 10

b-c

For example, 4 * 103 divided by 8 * 10

4 = 0.5 * 10

-1 = 0.05 * 10

0 = 0.05

Note how moving the decimal point in a number to the left in a leading number requires one to

increase the exponent on 10 by 1; moving a decimal point in a leading number to the rightrequires one to decrease the exponent on 10 by 1. For example (and noting 10

-1 * 10

1 = 10

0 = 1)

23 * 106 = 23 * 10

-1 * 10

1* 10

6 = 2.3 * 10

7 and

0.5 * 10

3 = 0.5 * 10

1 * 10

-1 * 10

3 = 5 * 10

2

The rule for addition and subtraction is the same for both. That is, in order to add or subtract two

numbers written with scientific notation, the exponents must first be the same. For instance,

250 + 1,400 = 1,650

2.5 * 102 + 1.4 *10

3 = 2.5 * 10

2 + 14 * 10

2 = 16.5 * 10

2 = 1.65 * 10

3


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Significant Figures in Measurements and Computations

Uncertainty in Measurements: The word accuracy has various connotations depending on thecircumstances under which it is used. In the study of physics it is commonly used to denote thereliability of a number or the limiting accuracy of a measurement.

As applied to a measurement, the accuracy is expressed by stating the uncertainty of the numericalresult, that is, the estimated maximum amount by which the result may differ from the "true" or accepted value.

For instance, if we have a keg advertised to contain 15,000 nails, we can assume that there are between 14,000 and 16,000 nails. The uncertainty amounts to +/- 1 in the last significant figure.

Significant Figures: The accuracy of a physical measurement is properly indicated by the number of figures used to express the numerical measure. Conventionally, only those figures that arereasonably trustworthy are retained. These are called significant figures.

In recording certain numbers, the location of the decimal point requires zeros to be added to thesignificant figures. These zeros serve only to locate the decimal point and are never consideredsignificant.

Examples: Length of a page 22.7 cm (3 significant figures)Thickness of a page = 0.011 cm (2 significant figures)Distance to the sun = 149,600,000 km (4 significant fig)Speed of light = 299,790 km/sec (5 significant figures)

If each of these numbers is expressed in terms of scientific notation, there is no doubt as to thenumber of significant figures for only the significant figures are then retained. Thus:

Length of page = 2.27 x 101 cm

Thickness of a page = 1.1 x 10-2 cm

Distance to the sun = 1.496 x 108 km

Speed of light = 2.9979 x 105 km/sec

Do not be confused by the placement of a zero in a string of numbers. For instance, say the landspeed record for a bicycle is 80.13 miles per hour. In this case the zero does more than position the8, 1, and 3. It indicates that the number is 80, not 79 or 81 miles per hour. A zero imbedded in astring of significant figures can never itself be considered non-significant. The number 80.13 isaccurate to 4 significant figures.

Further, do not be confused by a zero that is said to be of significance. Something may be measured toan accuracy of 2.000 units. In this case we know that the measurement is accurate to three decimal places. That is, the measurement lay somewhere between 2.001 and 1.999.

There are some numbers that, by their definition, may be taken to have an unlimited number of significant figures. For example, the integer 2 and the irrational number ! in the following

relationship for a circle: circumference = 2!r where r is the radius of a circle.

In calculations there is frequent need to use data that have been recorded without a clear indication of the number of significant figures. For example, a textbook problem may refer to a "two-kilogrammass", or in an experiment a student may announce that she has measured a certain distance as "3meters". In such cases the values with the approximate number of significant figures should be writtenfrom what is know or assumed about the way in which the measurement were made. If the distancereferred to were measured with an ordinary tape measure, the distance might appropriately be writtenas 3.0 meters. If the distance were carefully measured with a steel scale accurate to the nearestmillimeter, the distance might be recorded as 3.000 meters. In academic problem work a good rule tofollow is to retain three figures unless there is good reason to decide otherwise.


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Recapping: The significant figures in a quantity are the meaningful digits in it. There are severalconventions to keep in mind when dealing with significant figures:

1)

Any nonzero digit is a significant digit. Examples: 3.14 has three significant digits and 239.67has five significant digits.

2)

Zeros in between nonzero values are significant digits. Example 20.4 has three significant digits.3)

Zeros to the left of the first nonzero digit are NOT significant. Example: 0.0012 has twosignificant digits.

4)

For numbers with decimal points, zeros to the right of nonzero digits are significant. Example:

Both 2.0 and 0.0020 have two significant digits.5)

For numbers without decimal points, zeros to the right of nonzero digits may or may not besignificant. Example: 350 may have two or three significant digits. To avoid confusion, use adecimal point: 350. has three significant digits, 350 has two.

6)

The last significant digit in a number should have the same order of magnitude as theuncertainty. Examples: 3.1415±0.0001; 0.8±0.2

7)

The uncertainty should be rounded to one or two significant digits.

Rules for Computation: There is always a pronounced and persistent tendency on the part of beginners to retain too many figures in a computation. This not only involves too much work but,worse still, leads to a fictitiously precise result.

The following rules are recommended and will save much time that would otherwise be spent incalculation. Furthermore, their careful use will result in properly indicated accuracies.

1)

In recording the result of a measurement or calculation, one, and only one, doubtful digit isretained. For instance, a caliper whose finest division is 100-thousandths can produce ameasurement that is accurate to five decimal places at best. i.e. 3.02364.

2)

In addition or subtraction, do not carry the result beyond the first column that contains adoubtful figure. Add all figures and then round (see #4 below) the result. For example:

23.6543 3.410.01 -2.554

33.6643 0.846

which becomes 33.66 which becomes 0.8

3)

In multiplication and division, carry the result to the same number of significant figures thatthere are in that term which has the least number of significant figures. For example:

3.95 x 5.1 = 20.145, rounded to 2 significant figures = 20.

34.2/6.29934 = 5.429140196... becomes upon rounding 5.43

4)

When “doing” mathematical manipulations with numbers that have varying numbers of significant digits, the final result cannot have more significant digits than the least well knowndigit. In practice, we will keep all the numbers to their known accuracy through out thecalculation and then round to the number of significant figures at the end. Examples:

2.0*3.14159 = 6.3; 25.001+32.0 = 55.0

5)

In dropping figures that are not significant (rounding), the last figure retained should beunchanged if the following figures dropped are less than half the previous digit. It should beincreased by 1 if the following figures dropped are greater than half the previous digit. If the lastdigit dropped is 5, the preceding digit should be left unchanged if it is an even number butincreased by 1 if it is an odd number. For example:

24.5627 rounded to four significant figures becomes 24.5634.2523 rounded to three significant figures becomes 34.3

3.485 rounded to three significant figures becomes 3.487.550 rounded to two significant figures becomes 7.6


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Uncertainty in Measurement

When scientists collect data, they rarely make just one measurement and leave it at that. Random error

and unanticipated events can cause a single data point to be somewhat non-representative. Thus,

multiple measurements are usually taken, and the “best value” is represented using a variety of

approaches. Mean, median, and mode are measurement of central tendency. The degree to which data

tend to be distributed around the mean value is known as variation or dispersion. A variety of measuresof dispersion or variation around a mean are available, each with its own peculiar idiosyncrasy. Themost common measures are the following: range, mean deviation, standard deviation, semi-interquartile

range, and 10-90 percentile range. In this brief article we will examine only those measures most

suitable for the purposes of an introductory lab experience.

As a scientist or an engineer you need to know how to deal effectively with uncertainty in measurement.

Uncertainty in measurement is not to be confused with significant digits that have their own rules of

uncertainty (see Significant Figures in Measurement and Computation elsewhere in this Handbook ).

When one reads that some measurement has the value and absolute error of, say, d = 3.215m± .003m, how is

this to be interpreted? The value 3.215m is known as the arithmetic mean (as opposed to weighted mean,

geometric mean, harmonic mean, etc.) It is sometimes loosely called the average (but there are different

types of averages as well). The arithmetic mean of N measurements is defined as follows:

x =1

N x

i

i=1

N

"

Unless the person who provides the information indicates what the absolute error represents, there is

usually no way of knowing. Does the indicated error represent the range of the values measured? That is,

does this merely indicate that the “true” value of d is somewhere between 3.212m and 3.218m? It could

imply this, but other interpretations are also possible.

In another case, ±0.003m could represent the mean deviation. The mean deviation is the mean of the

absolute deviations of a set of data about the data’s mean. The mean deviation is defined mathematically as

MD =1

N xi" x

i=1

N

#

where x is the arithmetic mean of the distribution.

In yet another case ±0.003m could represent one standard deviation. This would then represent a

probability that there is a 68% chance (±1! or ±1standard deviation) that the “best” value is within the

range of 3.212m and 3.218m. Such an interpretation is not strictly justified unless more than 30 data points

have been used to calculate the mean value and standard deviation. In this interpretation data must also be

“normally distributed” around the mean. That is, the mean, median, and mode of the data must be

essentially the same. There must be a “bell shaped” distribution of data that are symmetrically distributed

around the mean. Most data collected in introductory labs fail to meet these criteria.

Whatever form of error representation you use in lab, you must be consistent in your expressions of

uncertainty in measurement. You must also declare what your error represents. For the purposes of these

introductory labs, the following forms of uncertainty in measurement will be used if called for.

Example 1: Range – You use a meter stick with divisions marked off in 1mm units. The measurements

of length appear to be somewhere between 93mm and 94mm. You would best represent the value of the

length with uncertainty as follows stating without any ambiguity that the true value of the length

measured lay somewhere between 93mm and 94mm.


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93.5mm ± 0.5mm

Example 2: Mean Deviation – You make six observations using an electronic photogate timer to

measure nearly identical falls of a ball with the following results:

Observation 1: 6.556 s






The value of the mean deviation is calculated thus:

x = 6.588 s

| x1-

x |=0.022 s

| x2-

x |=0.012 s

| x3-

x |=0.042 s

| x4-

x |=0.001 s

| x5-

x |=0.013 s

| x6 -

x |=0.014 s

Sum of deviations = 0.104 s

Mean deviation = 0.017 s

The mean of the data along with mean deviation are accurately stated as 6.588 s ± 0.017 s

Example 3: Standard Deviation – Let’s say you have made 30 measurements using your electronic timer.

For the purposes of these introductory labs we will use the mean ±1standard deviation (or ±1!) to represent

the error. This process makes three major assumptions – that the variation in the data comes only from

random error, that 30 or so data points are sufficient to calculate a good standard deviation, and that data

constitute a “normal” (bell-shaped distribution) around the mean. None of these assumptions might becorrect in a particular situation, so it might be a good idea to examine the distribution of data to make

certain that data have the standard symmetrical “bell shaped” curve. Thus, interpretation of the standard

deviation must be done with great caution.

Standard deviation of N data points can be readily calculated with the use of the following formula:

" =

1

N ( x

i# x)2

i=1

N

$

Most scientific calculators will, given the data points, readily calculate the standard deviation with ease.Lab students should use this form to express uncertainty in repeated measures throughout these

introductory lab activities when called for. Due caution should be observed in the use of significant digits.


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The Importance of Units

Units are incredibly important to physics. Two of the most important reasons are the following: (1) they help us

to avoid making mistakes in computation, and (2) they serve as a check on computations once they are completed.

In the first case, you can avoid adding 3m and 25cm and coming up with the wrong answer. If one merely adds

3 and 25, one gets 28. But answer this question, “28 what?” One cannot add apples and oranges – so to speak – and

get a meaningful answer. Before adding, units should be in common to the two terms. To add 3m to 25cm, convert

both to either meters or centimeter and then add:

3m + 25cm = 300cm + 25cm = 325cm OR 3m + 25cm = 3m + 0.25m = 3.25cm

Note that 325cm and 3.25m (both the same distance) don’t look anything like 28 “something”.

In the second case, one might be looking for a speed expressed in m/s. Speed can be found using the equation,

!x = vt . What if someone solves the equation incorrectly and gets the following where t = 3.5 s and !x is 62.5m?

v =t

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Student Lab Handbook