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# A S S O C I A T I O N O F C A N A D A L A N D S S U R V E Y O R S - B O A R D O F E X A M I N E R S W E S T E R N C A N A D I A N B O A R D O F E X A M I N E R S F O R L A N D S U R V E Y O R S A T L A N T I C P R O V I N C E S B O A R D O F E X A M I N E

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ASSOCIATION OF CANADA LANDS SURVEYORS - BOARD OF EXAMINERS WESTERN CANADIAN BOARD OF EXAMINERS FOR LAND SURVEYORS ATLANTIC PROVINCES BOARD OF EXAMINERS FOR LAND SURVEYORS ---

SCHEDULE I / ITEM 2

LEAST SQUARES ESTIMATION AND DATA ANALYSIS

February 2001 (1990 Regulations) This examination consists of __5__ questions on __2__ pages (Closed Book) Q. No. Time: 3 hours Marks

1. Perform a least squares adjustment of the following leveling network in which three height differences ∆hi, i = 1, 2, 3 were observed.

w = 5cm h1

h3

h2

The misclosure w is 5cm. Each ∆hi was measured with a variance of 2 cm2. 25

2. Given the following direct model for x and y as a function of l1, l2 and l3:







 

= −



 

3 2 1

1 3 5

1 2 4 y x

l l l

where the covariance matrix of the l’s is





=

2 1 1

1 2 2

1 2 4 Cl

Compute the covariance matrix for x and y. 15

3. Define and explain briefly the following terms:

a) Statistical hypothesis

b) Degree of freedom of a linear system c) Accuracy

d) Design matrix

e) Precision 10

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4. Given the following mathematical models

2 2

1 1

0 ) ,

(

0 ) , (

2 1 2 2

1 1 1

x x

C C x

, x f

C C x

f

l l

l l

==

==

where f1andf2 are vectors of mathematical models, x1and x2 are vectors of unknown parameter, l1andl2 are vectors of observations,

2 1

2

1,C ,C and C

Cl l x x are covariance matrices.

a) Formulate the variation function.

b) Derive the most expanded form of the least squares normal equation

system. 25

5. The following residual vector rˆ and estimated covariance matrix C were computed from a least squares adjustment using five independent observations with a standard deviation of σ= 2mm and a degree of freedom υ =2:

4 2 3 9 5

### ]

rˆ= − − (mm)

16 1 3 2 5

1 4 2 3 3

3 2 4 1 2

2 3 1 1 1

5 3 2 1 9 C













= (mm2)

Given α = 0.01,

a) Conduct a global test to decide if there exists outlier or not.

b) If the test in a) fails, conduct local tests to locate the outlier.

The critical values that might be required in the testing are provided in the following tables:

α 0.001 0.01 0.02 0.05 0.10

2 2 ,υ=

χα 13.82 9.21 7.82 5.99 4.61

α 0.001 0.002 0.003 0.004 0.005 0.01 0.05 Kα 3.09 2.88 2.75 2.65 2.58 2.33 1.64 where χα2,υ=2 is determined by the equation 2 (x)dx

2 ,

### ∫

= 2

=

υ

χα χ

α and Kα is

determined by the equation α= K e x /2dx

2

2 1 −−

∫∫α

π . 25

Total Marks: 100

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