Copper Rod Error
Measurements and Error Analysis, #1, Chris Baca
Discussion of differences
The purpose of this experiment is to understand why we have variances in measurements and how to reduce the variances. When taking a measurement there are multiple factors that affect its value. The more the measurement is taken the measurements average is closer to the actual value. Other factors include the instruments calibration, cleanliness of the inside of the measuring arms and human error in reading the measurements off of the measuring devices. For this experiment, we followed the procedures as indicated in the lab manual.
Data
Copper Rod Measurements
Trial
1
2
3
4
Length (m)
0.601
0.6
0.601
0.6
Diameter (caliper-m)
0.0006
0.00065
0.0006 …show more content…
Copper rod measurements continued
Equations
, ,
Calculations
Average and Standard Deviation of Copper Rod Measurements
Average
Standard Deviation
Length (m)
0.6004
0.022509257
Diameter (caliper-m)
0.00608
0.002392697
Diameter (Vernier Caliper-m)
0.0063485
.051639778
Mass (kg)
0.168743 …show more content…
Average values and the standard deviation of the 10 trials of measurements.
Density of Copper Rod
Caliper
Vernier Caliper
Average (kg/m³)
9.680282744
8.878773009
+ standard deviation
8.995958262
8.806850666
-standard deviation
10.44528428
9.54935451
Figure 4. Average Density of copper rod with both calipers with standard deviations.
Results/Comparison to Theory / Answers
The caliper and the micrometer were checked for the calibration. The results showed zero offset before and after experiment. Since there are differences in the measured data; the calculation of density varies. There are 3 variables that we have to take into account for calculating the density of the copper rod. The length plays the largest role in the uncertainty of the copper rod’s density since it has the largest deviation. The Vernier calipers were the most accurate because it showed the smallest deviation.
Our experiment carried out exactly how the theory explained it would. We measured the same part a multitude of times and received varying data. Calculating the density from the averaged data made the value more accurate, and when combined with the standard deviation; our values for density became that much more
Discussion of differences
The purpose of this experiment is to understand why we have variances in measurements and how to reduce the variances. When taking a measurement there are multiple factors that affect its value. The more the measurement is taken the measurements average is closer to the actual value. Other factors include the instruments calibration, cleanliness of the inside of the measuring arms and human error in reading the measurements off of the measuring devices. For this experiment, we followed the procedures as indicated in the lab manual.
Data
Copper Rod Measurements
Trial
1
2
3
4
Length (m)
0.601
0.6
0.601
0.6
Diameter (caliper-m)
0.0006
0.00065
0.0006 …show more content…
Copper rod measurements continued
Equations
, ,
Calculations
Average and Standard Deviation of Copper Rod Measurements
Average
Standard Deviation
Length (m)
0.6004
0.022509257
Diameter (caliper-m)
0.00608
0.002392697
Diameter (Vernier Caliper-m)
0.0063485
.051639778
Mass (kg)
0.168743 …show more content…
Average values and the standard deviation of the 10 trials of measurements.
Density of Copper Rod
Caliper
Vernier Caliper
Average (kg/m³)
9.680282744
8.878773009
+ standard deviation
8.995958262
8.806850666
-standard deviation
10.44528428
9.54935451
Figure 4. Average Density of copper rod with both calipers with standard deviations.
Results/Comparison to Theory / Answers
The caliper and the micrometer were checked for the calibration. The results showed zero offset before and after experiment. Since there are differences in the measured data; the calculation of density varies. There are 3 variables that we have to take into account for calculating the density of the copper rod. The length plays the largest role in the uncertainty of the copper rod’s density since it has the largest deviation. The Vernier calipers were the most accurate because it showed the smallest deviation.
Our experiment carried out exactly how the theory explained it would. We measured the same part a multitude of times and received varying data. Calculating the density from the averaged data made the value more accurate, and when combined with the standard deviation; our values for density became that much more