Quiz Chapter 06.03: Linear Regression

MULTIPLE CHOICE TEST

(All Tests)

LINEAR REGRESSION

(More on Linear Regression)

REGRESSION

(More on Regression)


Pick the most appropriate answer


1. Given \left(x_{1},y_{1} \right), \, \left( x_{2}, y_{2} \right), \, ... \, , \, \left( x_{n}, y_{n} \right) best fitting data to y = f (x) by least squares requires minimization of

 
 
 
 

2. The following data

x 1 20 30 40
y 1 400 800 1300

is regressed with least square regression to y = a_{0} + a_{1}x. The value of a_{1} is most nearly

 
 
 
 

3. The following data

x 1 20 30 40
y 1 400 800 1300

is regressed with least square regression to y = a_{1}x. The value of a_{1} is most nearly

 
 
 
 

4. 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 = a_{0} + a_{1}x.

x 1 10 20 30 40
y 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

 
 
 
 

5. A torsion spring of a mousetrap is twisted through an angle of 180^{\circ}. The torque vs. angle data is given below

Torsion, T, N-m 0.110 0.189 0.230 0.250
Angle, \theta, rad 0.10 0.50 1.1 1.5

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

 
 
 
 

6. A scientist finds that regressing the y vs x data given below to y = a_{0} + a_{1}x results in the coefficient of determination for the straight-line model, r^{2}, being zero

x 1 3 11 17
y 2 6 22 ?

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