understanding errors in measurements - esri€¦ · errors in measurements james a. coan, sr.,...
TRANSCRIPT
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Understanding Errors
In Measurements
James A. Coan, Sr., P.L.S.
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A Friendly Pre-Testn 1) Precision and Accuracy are the same thing T F n 2) All errors can be avoided T F n 3) All errors can be eliminated T F n 4) How many significant figures will an answern have if a measured distance is multiplied by Pi?n 5) How many significant figures is 0.032 ?n 6) Individual random errors are added together n to find the total random error of a measurement T F n 7) If a manufacturer states that your EDM can n measure to + (3mm+2ppm) what confidence n level are they referring to ?n 8) An instrument centering error refers to n how well a surveyor can set up over a point T F n 9) In Surveying, all angle measurements n are about the same precision T F
10) Weighting measurements allows the n surveyor to put the error where it belongs T F
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Understanding Errors In Measurements
(WHAT WE WILL COVER)
Errors and MistakesPrecision and AccuracySources of Errors in SurveyingSignificant FiguresRandom Error PropagationStatistics for Land SurveyorsRandom Errors in Angles and DistancePractical Weights of Observations
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Errors And Mistakes
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Errors And MistakesErrors
The difference between the true andmeasured value of a measurement
The error equals the measured distance minus the true distance
E = M-T
They are unavoidable
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Errors And Mistakes
Mistakes (Blunder)
A mistake is a blunder caused by carelessness
This type of problem has nothing to do with errors
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Systematic And
Random Errors
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Systematic And Random Errors
Systematic Errors
They are predictable
Their magnitude and direction can be determined
Systematic errors can be eliminated
This separates them from random errors
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Systematic And Random Errors
Random ErrorsAre unavoidable
Can be minimized but never eliminated
Have a tendency to cancel but never completely do so.
Can be dealt with by the science of propagation
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Precision And Accuracy
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Precision And AccuracyPrecision
The agreement of readings of the same quantity
The better the precision the smaller the random errorGood precision exposes random error
Crude precision hides random errors
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Precision And Accuracy
AccuracyThe agreement of readings with the true value
Accuracy is to systematic errors as precision is to random errors
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Precision And Accuracy
Precision
Accuracy
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Sources Of Errors In Surveying
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Sources Of Errors In Surveying
Natural ErrorsCaused by nature, wind, temperature, earth curvature, etc.
If the error is subject to known physical laws, it is systematic.
If the error does not follow known physical laws it is probably random
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Sources Of Errors In Surveying
Instrumental Errors
Caused by manufacture, wear and tear, or maladjustment of instruments.
This type of error can also be thought of as a blunder
Most instrumental errors are random in nature
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Sources Of Errors In SurveyingPersonal ErrorsCaused by the inability of a person perceive anything exactly
Can be controlled by good training, good motivation, and good technique
Personal errors are random and each person has their own
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Significant Figures
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Significant Figures
Significant figures is a part of mathematics that
deals with the relevancy of digits in a number,
and are mainly used in the surveying, scientific,
and engineering community.
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Significant figures can be separated into
two areas:
1) Significant figures in measurements
2) Significant figures in computations
Significant Figures
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Significant FiguresMeasurements
Relates to the way measurements are made and recorded
No measurement is exact
When measuring, record only the digits that have meaning
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Significant FiguresComputations
Concerns itself with round-off error
The surveyor must use established rules of
significant figures
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Significant FiguresRules
The following rules apply to both recording
data and interpreting recorded data
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Significant Figures
Zeroes used merely to indicate the position of
a decimal point are not significant
Example:
0.056 has two significant figures
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Significant FiguresRules
Zeroes recorded at the end of measurement
are significant
Example:
1.30 has three significant figures
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Significant FiguresExample:
Zeroes between non zero digits are
significant
Example:
1.04 has three significant figures
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Significant Figures
Rules
Numbers ending with one or more
zeroes to the left of the decimal
should have a special indication
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Significant Figures
Example:
175,000 has three significant figures
375,000 can have six significant figures
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Significant Figures
Rules
When adding or subtracting measured
distances the number with the fewest
decimal places will dictate the number of
significant figures
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Significant FiguresRules
Adding or Subtracting15.495
10.21
12.2
37.905
control, fewest number of decimals
The proper answer is 37.9, having three
significant figures
Example:
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Significant FiguresRules: Multiplication or Division
The product or quotient is
determined by the fewest number
of significant figures in the values
used, if both are measured values
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Significant Figures
Example:
5.29 x 0.052 = 0.28 two significant figures
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Significant Figures
RulesConversion factors do not
determine significant figures
Example:
1534.5 in / 12in per ft = 127.88 ft.
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Significant FiguresRules
With large or infinite number conversion
factors; use one extra digit
Example:incorrectcorrect
174.35 x 3.14 = 547.46174.35 X 3.14159 = 547.74
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Significant FiguresRules
With intermediate calculations use one
extra digit and round off your answer
Example: Intermediate Calculation
43.56 x 23.4321.02
= 1020.6 = 48.5521.02
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Random Error
Propagation
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Random Error Propagation
All measurements have random errors
Random errors have a tendency to cancel
but never completely do so
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Random Error Propagation
How random errors accumulate, cancel,
decrease or behave through the process
of computing the final value is termed
“Propagation of Random Errors”
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Random Error Propagation
Errors in a sum
Used when all errors are different
It is the square root of the sum of the squares
of the errors
E = + e12 + e2
2 + e32 +…+ en
2
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Random Error Propagation
Errors in a Series
Used when the same error happens several
times
Derived from the formula of errors in a sum
E = + e n
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Random Error Propagation
Errors in a Product
Used in determining the random error of area
calculations
Is also the square root of the sum of the
squares of the errors
E = + (Lew)2 + (WeL)2
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Statistics For
Surveyors
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Statistics For SurveyorsDefinitions
Direct Measurements
A measurement made directly
between two or more points
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Statistics For Surveyors
Definitions
Indirect Measurements
A computed measurement
between points
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Statistics For SurveyorsDefinitions
Sample Size
The number of observations or measurements in
a sample
n = Sample Size
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Mean
The sum of observations of a sample divided
by the sample size
=Mean Xi = The value of the sample
Statistics For Surveyors
X = S xi
n
X
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Sample Size = 25
Mean = 779.4 / 25 = 31.2
Sn = 779.4
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Statistics For Surveyors
Definitions
Median
The middle value of the sample when the data
is arranged in ascending or descending order
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Statistics For Surveyors
Definitions
Mode
The value which occurs most frequently in a
sample
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Median
Mode
Mode
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Statistics For SurveyorsDefinitions
Residual
The difference between an individual value
in a sample and the mean of the sample
Vi = Residual
Vi = Xi - X
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Statistics For SurveyorsDefinitions
Standard Deviation
68% probability of an occurrence
s = Standard Deviation
s = +S vi
2
n-1
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Statistics For SurveyorsLevels Of Certainty
NAME OF ERROR
SYMBOL VALUE % CERTAINTY
PROBABLE E50 0.6745s 50STANDARD DEVIATION s 1s 68.3
90% ERROR E90 1.6447s 90
TWO SIGMA E95 2s 9599%
ERROR E99 2.5s 99THREE SIGMA E99.7 3s 99.7
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Statistics For SurveyorsDefinitions
Standard Error of the Mean
The interval of uncertainty around the true value.
Sx = +s
n
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s sx n
= ± = ± = ±0 65
25013. .
Statistics For SurveyorsDefinitions
Standard Error of the Mean
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Pre-analysis
Formulas
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Pre-analysis FormulasReading Error
sar = The total reading errorsr = The individual reading errorn = The number of angles turned
s sa r
r
n= ±
2
Directional Theodolite
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Pre-analysis Formulas
Reading Error
sar = The total reading errorsr = The individual reading error
n = The number of angles turned
Repetition Theodolite
s sa r
r
n= ±
2
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Reading Error ExampleDirectional Theodolite
s sa r
r
n= ±
2
sr = individual reading error ( 0.65)
n = number of angles turned (4)
s a r= ± =
0 65 24
0 46. .
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Reading Error ExampleRepetition Theodolite
s sa r
r
n= ±
2
sr = individual reading error ( 0.65)
n = number of angles turned (4)
s a r= ± =
0 65 24
0 23. .
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Reading Error Example Topcon IS
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Pre-analysis FormulasPointing Error
Epb = Error in pointing to the backsight
Epf = Error in pointing to the foresight
dc = Estimate of how closely the observer
can center the cross-hairs on the target
D = The distance to the backsight or the
foresight
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Pre-analysis FormulasPointing Error
= Angle in radiansdcD
dc
D(206,265) = Angle in Seconds
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E dDpb
b
b= ± =( , )206 265
EdDpf
f
f= ± =( , )206 265
Pre-analysis FormulasPointing Error
Pointing Error, Backsite
Pointing Error, Foresite
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Pointing Error Example
db = 0.01’ ; Db = 350.25
E pb= ± =
0 01350 25
206 265 589..
( , ) . "
E dDp
b
bb
= ± ( )206265
Pointing Error
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EdDp
f
ff
= ± ( , )206 265 df = 0.01’ ; Df = 425.36
E p f= ± =
0 0142536
206 265 4 85..
( , ) . "
Pointing Error ExamplePointing Error
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Pre-analysis FormulasPointing Error
E E Ep pb pf= ± +2 2
Error in one angle turned
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Pointing Error Example
E E Ep p pb f= ± +2 2
E p = ± + =589 4 85 7 632 2. . . "
Pointing Error
Error in one angle turned
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s appEn
= ±2
Total error in multiple sets turned
s a p= ± =
7 63 24
5 40. . "
Total Pointing Error
Pointing Error Example
Pointing Error
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Pre-analysis FormulasInstrument centering error
s aic
f b
d DD D
= ± =3
2206 265( , ) Angle In Seconds
dc= Estimate of how well you can set up over a pointDb= Distance to backsight
Df= Distance to foresight
D3= Distance from the backsight to the foresight
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Instrument Centering Error
s a i
d DD D
c
f b
= ± 3
2206 265( , )
dc = 0.005’ Df =425.36’
Db = 350.25’ D3 = 372.11’
s a i= ± =
( . ' )( . ' )( . ' )( . ' )
( , ) . "0 005 37211425 36 350 25 2
206 265 182
Example:
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Pre-analysis FormulasTarget Centering Error
db+ df = How well you can set up a target over a point
Db= Distance to the backsight
Df= Distance to the foresight
Etb & Etf = Angular error to the backsight and
foresight in seconds
sat= Total angular error
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Pre-analysis FormulasTarget Centering Error
E dDtb
b
b
= ± ( , )206 265
EdDtf
f
f
= ± ( , )206 265
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s at tb tfE E= ± +2 2
Pre-analysis Formulas
Target Centering Error
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Target Centering Error Example
E dDt
b
bb
= ( , )206 265
Etb= =
0 005350 25
206 265 2 94. '. '
( , ) . "
dD
b
b
==
0 005350 25. '
.
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EdDt
f
ff
= ( , )206 265dD
f
f
=
=
0 00542536. '
. '
Et f= =
0 00542536
206 265 2 42. '. '
( , ) . "
Target Centering Error Example
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Target Centering Error Example
s a t b fE Et t= ± +2 2EE
t
t
b
f
=
=
2 94
2 42
. "
. "
sat = + 2.942 + 2.422 = 3.81”
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Pre-analysis FormulasBubble Centering Error
sm g m g
abd b d ff Tan f Tan
n= ±
+( ) ( )2 2
g= Vertical angle to backsight and foresight (not
zenith angles)
m= Bubble sensitivity of the spirit level in seconds
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Pre-analysis FormulaBubble Centering Error
sm g m g
abd b d ff Tan f Tan
n= ±
+( ) ( )2 2
fd= Estimate of how closely the instrument can be
leveled during a set of angles in
fractions of divisions of the spirit level
n = Number of angles turned
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Bubble Centering Error Example
sm g m g
abd b d ff Tan f Tan
n= ±
+( ) ( )2 2
fd = 0.5 m = 10” g b= 25°30’45”
g f = 32°15’09” n = 4
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s a b
Tan Tan= ±
· · + · ·=
( . ' ") ( . ' ") . "05 10 25 30 45 05 10 32 15094
0 992 2o o
Bubble Centering Error Example
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Total Random Error
sa = + sr2 + sp
2 + si2 + st
2 + sb2
sr = Reading Error
sp = Pointing Error
si = Instrument Centering Error
st = Target Centering Error
sb = Bubble Centering Error
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Total Random Error
sa = + 0.462 + 5.402 + 1.822 + 3.812 + 0.992 = 6.93”
Reading Error directional = 0.46”
Pointing Error = 5.40”
Instrument Centering Error = 1.82”
Target Centering Error = 3.81”
Bubble Centering Error = 0.99”
Total Angular Error = 6.93”
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Pre-analysis FormulasEDM Error
Em= (Standard manufacturer error) (distance)
Et= Target centering error in feet
Ei= Instrument centering error in feet
sEDM = + Em2 + Et
2 + Ei2
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Pre-analysis FormulasEDM Error
sEDM = + 0.012 + 0.0052 + 0.0052 = 0.01’
Em = 0.01 + [ 3mm + (3ppm)(425.36’)]
Et = 0.005’ Target Centering
Ei = 0.005’ Instrument Centering
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Pre-analysis Formulas
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In Summary
Random errors are part of every measurement
taken by a surveyor.
In order to control random errors a surveyor
must first understand them.
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Once the surveyor understands random errors
they can place them where they belong.
This is critical because some measurements are
better than others.
In Summary
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Weights Of Observations
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General
Some measurements are better than others. This
is common knowledge in the surveying
profession.
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General
Measurements made in good conditions,
using good equipment, with proper survey
procedures will produce good results.
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General
Measurements made in adverse conditions,
using poor equipment, and bad surveying
procedures will produce bad results.
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General
As a rule, surveyors use good equipment, with
good procedures, but can work in poor conditions
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General
Because of this, the quality of measurements
can vary from setup to setup in the same job.
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General
Weighting measurements allows the surveyor
to distribute errors of the measurements
where the error should go.
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General
If one angle in a survey is turned under good
conditions and another angle, in the same survey,
is turned under bad conditions, weighting
measurements allows for putting more of the
error in the bad angle than in the good angle.
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General
According to statistical theory, the weight of a
measurement is inversely proportional to the
variance.
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General
The higher the precision of the measurement,
the smaller the variance.
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General
The smaller the variance, the larger the weight.
OR
The better the measurement, the larger the
weight.
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The Variance
What is the variance?
Simply put, the variance is the square of the
standard deviation.
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The Variance
The standard deviation of a set of measurements
can be determined by statistical analysis.
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The Variance
This can get complicated in a hurry, and
many practicing surveyors tend not to use
weights
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The Variance
If weights are not applied to the surveyors
measurements, all the measurements are
treated the same. This is false !
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Weighting measurements does not need to be
this complicated
Surveyor can assign weights to their
measurements
Weights Of Observations
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Weights Of Observations
The worse the measurement, the smaller the
weight.
The more precise the measurement, the
larger the weight.
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Example One
A distance between two points were measured
four times.
The first distance was found to be 396.57 feet
and was measured with a cloth tape
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Example One
The second and third distances were found to
be 396.61 and 396.62 feet, and were
measured with a steel calibrated tape
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Example One
The fourth distance was found to be 396.64
feet and was measured with an EDM.
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Example OneThe party chief assigned a weight of one (1) to the first measurement, a weight of two (2) the second and third measurement, and a weight of four (4) to the final measurement.
These values were not based on statistical theory, but based on field judgment.
Low precision, large variance, low weight
High precision, small variance, large weight
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Example OneThe formula to calculate the weighted mean of the distance is. S
SWMW
MW=
SWM = The sum of the measurements times their weights
SW = The sum of the weights
MW = The weighted mean distance
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Example OneUsing the weighted mean formula in our example we have.
39657 1 396 61 2 396 62 2 396 64 41 2 2 4
396 62. ( ) . ( ) . ( ) . ( ) .+ + ++ + +
=
While the above example shows how to weight measurements, it is not very practical as most distance measurements are made with EDM’s, and the need to weight measurements are rare.
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Example TwoA five sided traverse was run with the following results
Station Angle WeightA 76°46’35” W = 1B 87°15’20” W = 2C 122°10’45” W = 2D 165°58’25” W = 3E 87°48’50” W = 4
Total 359°59’55” 12Error 00°00’05”
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Example Two
Historically, one second would be put into each
angle and the error would be eliminated.
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Example Two
If this is done, than all of the angles are
treated the same, that is, all of the angles
are given the same weight.
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Example Two
In this case it is the party chief that
determines the weights of the angles from
the conditions in the field.
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Example Two
The party chief assigns the weights of the angles as follows.Angle “A” is the worst angle
Angles “B” and “C” are better than “a” but still not real good.
Angle “D” was better than the others but not the best.
Angle “E” was the best angle in the traverse
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Example TwoThe weight of angle “A” = 1,
The weights of angles“B” and “C” = 2
The weight of angle “D” = 3
The weight of angle “E” = 4
.
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Example Two
Angle adjustments are made inversely
proportional to their weights. The larger the
weight of the angle, the smaller the
adjustment
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Example TwoThe table illustrates how weights are used to distribute errors
Station Measured Weight Correction Numerical Correction
Rounded Correction
Adjusted Angles
A 76°46’35” 1 12X 1.94” 2” 76°46’37”
B 87°15’20” 2 6X 0.97” 1” 87°15’21”
C 122°10’45” 2 6X 0.97” 1” 122°10’46”
D 165°58’25” 3 4X 0.65” 1” 165°58’26”
E 87°48’50” 4 3X 0.47” 0” 87°48’50”
Sum 539°59’55” 12 31X 5.00” 5” 540°00’00”
31X=5” X=0.16”
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Example Two
To find the correction factor , divide the individual weights into the sum of the weights.
To find the value of “x”, place the sum of the correction factors equal to the error and solve for “x”
To find the numerical correction, multiply the individual correction factor times the value of “x”
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Example Two
As seen in the chart, the worst angle received the largest correction (2”), and the best angle had no correction at all.
This is more realistic with what the party chief observed in the field when the angles were turned
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How Weights Can Be Determined
A surveyor can make their own system. One
example will be to divide the angles into four
categories, as follows.
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How Weights Can Be Determined
Category 1 might be one where the setup is on
soft ground and the surveyor is having a hard
time keeping the instrument level, and their
backsite or foresite (or both) is short.
This could receive a weight of one (1)
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How Weights Can Be Determined
Category 2: the second example might be where
the surveyor is on firm ground but still has a
shore backsite and is having a hard time seeing
his foresite. This type of angle could receive a
weight of two (2)
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How Weights Can Be Determined
Category 3: the third example might be where the
surveyor is on firm ground, with good sites both
front and back, but the conditions are not very
good. It could be foggy or there might be a lot of
heat waves. This type of angle could receive a
weight of three (3)
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How Weights Can Be Determined
Category 4: the last example is the best angle.
The surveyor is on firm ground with good sites,
turning an angle with strong strength of figure.
The weather is high overcast skies and about
65°f This type of angle could receive a weight of
four (4)
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Conclusion
Many programs, such as star*net, allow you to
weight your measurements very easily and with
very little practice the surveyor can use weights
and put the error where it belongs.
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Conclusion
Using weights helps take control of
measurements and helps distribute errors in
a more realistic manner that can result in a
better, more accurate survey.