Holistic Numerical Methods

Transforming Numerical Methods Education for the STEM Undergraduate

 

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MULTIPLE CHOICE TEST

(All Tests)

LINEAR REGRESSION

(More on Regression Background)

REGRESSION

(More on Regression)

 

Pick the most appropriate answer.


Q1.  Given (x1,y1), (x2,y2), ..., (xn,yn) best fitting data to y=f(x) by least squares requires minimization of

 


Q2. The following data

1

20

30

40

1

400

800

1300

 

is regressed with least squares regression to y=a0+a1x.  The value of a1 most nearly is

27.480

28.956

32.625

40.000

 


Q3. The following data

 

1

20

30

40

1

400

800

1300

 

is regressed with least squares regression to y=a1x.  The value of y=a1x most nearly is

27.480

28.956

32.625

40.000

 


Q4.  An instructor gives the same y vs x data as given below to four students and asks them to regress the data with least squares regression to y=a0+a1x.

 

1

10

20

30

40

1

100

400

600

1200

 

Each student comes up with four different answers for the straight line regression model.  Only one is correct.  The correct model is

y=60x-1200

y=30x-200

y=-139.43+29.684x

y=1+22.782x

 


Q5A torsion spring of a mousetrap is twisted through an angle of 1800.  The torque vs angle data is given below.

 

Torsion, T, N-m

0.110

0.189

0.230

0.250

Angle, θ, rad

0.10

0.50

1.1

1.5

 

The amount of strain energy stored in the mousetrap spring in Joules is

0.29872

0.41740

0.84208

1561.8

 


Q6.  A scientist finds that regressing the y vs x data given below to y=a0+a1x results in the coefficient of determination for the straight-line model,r2 being zero.

 

x

1

3

11

17

2

6

22

?

 

The missing value for y at x=17 most nearly is

-2.4444

2.0000

6.8889

34.000

 

 

 

Complete Solution

 

 

Multiple choice questions on other topics

 


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Copyrights: University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620-5350. All Rights Reserved. Questions, suggestions or comments, contact kaw@eng.usf.edu  This material is based upon work supported by the National Science Foundation under Grant# Creative Commons License0126793, 0341468, 0717624,  0836981, 0836916, 0836805, 1322586.  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  Other sponsors include Maple, MathCAD, USF, FAMU and MSOE.  Based on a work at http://mathforcollege.com/nm.  Holistic Numerical Methods licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

 

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