Regression

1
Regression
2
Applications
3
Mousetrap Car
4
Torsional Stiffness of a Mousetrap Spring
5
Stress vs Strain in a Composite Material
6
A Bone Scan
7
Radiation intensity from Technitium-99m
8
Trunnion-Hub Assembly
9
Thermal Expansion Coefficient Changes with
Temperature?
10
THE END
11
Pre-Requisite Knowledge
12
This rappers name is

1. Da Brat
2. Shawntae Harris
3. Keha
4. Ashley Tisdale
5. Rebecca Black

13
Close to half of the scores in a test given to a
class are above the

1. average score
2. median score
3. standard deviation
4. mean score

14
The average of the following numbers is
2 4 10 14

1. 4.0
2. 7.0
3. 7.5
4. 10.0

15
The average of 7 numbers is given 12.6. If 6 of
the numbers are 5, 7, 9, 12, 17 and 10, the
remaining number is

1. -47.9
2. -47.4
3. 15.6
4. 28.2

16
Given y1, y2,.. yn, the standard deviation is
defined as

1. .
2. .
3. .
4. .

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THE END
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6.03Linear Regression
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Given (x1,y1), (x2,y2),.. (xn,yn), best
fitting data to yf (x) by least squares requires
minimization of

20
The following data
x 1 20 30 40
y 1 400 800 1300
is regressed with least squares regression to
ya1x. The value of a1 most nearly is

1. 27.480
2. 28.956
3. 32.625
4. 40.000

21
A scientist finds that regressing y vs x data
given below to straight-line ya0a1x results in
the coefficient of determination, r2 for the
straight-line model to be zero.
x 1 3 11 17
y 2 6 22 ?
The missing value for y at x17 most nearly is

1. -2.444
2. 2.000
3. 6.889
4. 34.00

22
A scientist finds that regressing y vs x data
given below to straight-line ya0a1x results in
the coefficient of determination, r2 for the
straight-line model to be one.
x 1 3 11 17
y 2 6 22 ?
The missing value for y at x17 most nearly is

1. -2.444
2. 2.000
3. 6.889
4. 34.00

23
The following data
x 1 20 30 40
y 1 400 800 1300
is regressed with least squares regression to a
straight line to give y-11632.6x. The
observed value of y at x20 is

1. -136
2. 400
3. 536

24
The following data
x 1 20 30 40
y 1 400 800 1300
is regressed with least squares regression to a
straight line to give y-11632.6x. The
predicted value of y at x20 is

1. -136
2. 400
3. 536

25
The following data
x 1 20 30 40
y 1 400 800 1300
is regressed with least squares regression to a
straight line to give y-11632.6x. The
residual of y at x20 is

1. -136
2. 400
3. 536

26
THE END
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6.04Nonlinear Regression
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When transforming the data to find the constants
of the regression model yaebx to best fit
(x1,y1), (x2,y2),.. (xn,yn), the sum of the
square of the residuals that is minimized is

29
When transforming the data for stress-strain curve
for concrete in compression, where
is the stress and
is the strain, the model is rewritten as

30
6.05Adequacy of Linear Regression Models
31
The case where the coefficient of determination
for regression of n data pairs to a straight line
is one if

1. none of data points fall exactly on the straight
   line
2. the slope of the straight line is zero
3. all the data points fall on the straight line

32
The case where the coefficient of determination
for regression of n data pairs to a general
straight line is zero if the straight line model

1. has zero intercept
2. has zero slope
3. has negative slope
4. has equal value for intercept and the slope

33
The coefficient of determination varies between

1. -1 and 1
2. 0 and 1
3. -2 and 2

34
The correlation coefficient varies between

1. -1 and 1
2. 0 and 1
3. -2 and 2

35
If the coefficient of determination is 0.25, and
the straight line regression model is y2-0.81x,
the correlation coefficient is

1. -0.25
2. -0.50
3. 0.00
4. 0.25
5. 0.50

36
If the coefficient of determination is 0.25, and
the straight line regression model is y2-0.81x,
the strength of the correlation is

1. Very strong
2. Strong
3. Moderate
4. Weak
5. Very Weak

37
If the coefficient of determination for a
regression line is 0.81, then the percentage
amount of the original uncertainty in the data
explained by the regression model is

1. 9
2. 19
3. 81

38
The percentage of scaled residuals expected to be
in the domain -2,2 for an adequate regression
model is

1. 85
2. 90
3. 95
4. 100

39
THE END