Lab 02
Experimental verification of ohm’s law
Objective
To verify Ohm’s law experimentally and to find the relationship between voltage, current, and resistance in
a circuit.
Plotting a graph and verify the resistance or conductance as a slope
Pre-Lab:
1. In Figure 1: if the voltage across the resistor is 10V and ammeter reading is 1mA.what is the value of R
2. What are jumper wires?
3. Why practical results are different from theoretical results?
Overview:
In this lab, we will continue to explore the characteristics of the resistors. In this lab, we measure several
combinations of voltage and current for a resistor and plot the resulting voltage-current characteristic curve measured
for the resistor. The resistance of the resistor will be estimated from the slope of the voltage-current characteristic.
General Discussion:
We have previously noted that the resistance of a component is the slope of the current vs. voltage curve for the
component. In this part of the lab assignment, we will measure a current-voltage characteristic curve for a resistor
and estimate a resistance from this data. We will compare this resistance from the resistance measured by an
ohmmeter. No scientific-practical result can perfectly match the theory, the actual results depend on environmental
conditions, the accuracy of equipment under test. The quality of resistor also impacts the measurements
In order to experimentally determine the current-voltage characteristic for our resistor, we will use the circuit
shown schematically in Figure 1. The resistor and variable power supply voltage vs. We will measure the voltage
across the resistor vR, and the current through the resistor, iR, using our DMM. By varying vs, we can measure a set
of values for vR and iR and plot vR vs iR, as shown in Figure 2(a). The slope of the line that ‘‘best fits’’ the measured
data can then be used to estimate the component’s resistance, as shown in Figure 2(b).
Note:
Do not use the values displayed by the power supply as the resistor voltage and current, vR and iR. The values
displayed by the power supply may differ from the resistor’s voltage and current due to non-ideal power supply
effects, such as the power supply internal resistance
Figure 2. Measured data with ‘‘best fit’’ straight-line approximation
Lab Procedures:
1. Connect the circuit shown in Figure 1. Using a 1K, 2.2K and 10K respectively with a variable supply voltage. Use an
ohmmeter to measure the actual resistance of your resistor and record the value in the table below
2. Vary the supply voltage Vs from 0V to approximately 5V with an increment of 0.5V as shown in the table below.
Measure Vr and Ir for at least 10 different values of Vs. Note: since we only have one DMM, the voltage and
current measurements will have to be performed separately. If you have access to two DMMs, the two
measurements can be made simultaneously.
3. Draw three different graphs of ohms law for the following three resistors, implementing one by one in figure1.
For the calculations in the following table use actual values of the resistors
R1nom= 1K Ω R1actual= K Ω R2nom= 2.2K Ω R2actual= KΩ R3nom= 10K Ω R3actual= K Ω
V (volts) Voltage I (mA) I (mA)Calculated Voltag I (mA) I (mA)Calculated Voltage I (mA) I (mA) Calculated
measure Measure I=V/ R1 e Measure I=V/ R2 measure Measu I=V/ R3
d Vr d measu d d Vr red
(using red Vr (using (using
DMM) DMM) DMM)
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Post Lab Questions:
1. Is the resistor a linear circuit element? Why
2. What does the slope tells us in (V vs. I curve) usually called VI curve
3. Draw the all three curve together on one graph .how you will know which graph is of highest resistance
Appendix A – Basic Statistics
In this appendix, we briefly present some common statistical values used to characterize data which have some random
component. We will also present MATLAB commands which can be used to determine these values.
Mean value:
If we have N sample values, {y1, y2, …yN}, we can determine the mean of the values by the following expression
Thus, the mean can be determined by summing all the sample values and dividing by the total number values. It is
essentially what we do when we determine an average of a series of values.
Median value:
The median value of a set of N sample values {y1, y2, …yN} is the value which has the same number of samples
above the value as there are below the value. This definition, however, does not necessarily uniquely determine
the median value of a set of numbers. We will, therefore, refine our definition as follows:
If N is an odd number, sort the data so that they are ordered from smallest to largest. The median value is then
the middle data value.
If N is an even number, again sort the data so that they are ordered from smallest to largest. There is no
value exactly at the middle of the resulting sequence, so we choose the median value as the mean of the middle
two numbers.
Standard deviation:The standard deviation of a set of values {y1, y2, …yN} can be thought of as the amount of
‘‘spread’’ that the numbers have from their mean value. Thus, the standard deviation provides an indication as to how
closely group the values are around their mean. The definition of standard deviation that we will use is:
where s is the standard deviation and y is the mean of the values, as defined above. The positive square root is
used in the calculation, so that s is always represented as a positive number. Some textbooks use an alternate
version of the standard deviation; we will exclusively use equation (2). When using a software package to perform
statistical analyses, always check the definition of standard deviation that is use Thus, the mean can be determined
by summing all the sample values and dividing by the total number values. It is essentially what we do when we
determine an average of a series of values.
Median value:The median value of a set of N sample values {y1, y2, …yN} is the value which has the same number
of samples above the value as there are below the value. This definition, however, does not necessarily uniquely
determine the median value of a set of numbers. We will, therefore, refine our definition as follows:
If N is an odd number, sort the data so that they are ordered from smallest to largest. The median value is
then the middle data value.
If N is an even number, again sort the data so that they are ordered from smallest to largest. There is no
value exactly at the middle of the resulting sequence, so we choose the median value as the mean of the middle
two number
Standard deviation:
The standard deviation of a set of values {y1, y2, …yN} can be thought of as the amount of
‘‘spread’’ that the numbers have from their mean value. Thus, the standard deviation provides an
indication as to how closely group the values are around their mean. The definition of standard
deviation that we will use is:
where s is the standard deviation and y is the mean of the values, as defined above. The positive square
root is used in the calculation, so that s is always represented as a positive number. Some textbooks use
an alternate version of the standard deviation; we will exclusively use equation (2). When using a
software package to perform statistical analyses, always check the definition of standard deviation that is
use
