Q1) Consider actual values Y and predict values F given in table below.
|
Time Period
|
Y
|
F
|
|
1
|
19.5
|
19.3
|
|
2
|
21.5
|
20.9
|
|
3
|
22.6
|
22.5
|
Compute root mean squared error (RMSE) of forecasts.
a) 0.30
b) 0.37
c) 1.42
d) 0.90
Q2) A first-order model doesn't contain any higher-order terms.
True
False
Q3) Method of fitting first-order models is same as that of fitting simple straight-line model, i.e. method of least squares.
True
False
Q4) Retail price data for n = 60 hard disk drives were recently reported in computer magazine. 3 variables were recorded for each hard disk drive:
|
y =
|
Retail PRICE (measured in dollars)
|
|
x1 =
|
Microprocessor SPEED (measured in megahertz) (Values in sample range from 10 to 40)
|
|
x2 =
|
CHIP size (measured in computer processing units) (Values in sample range from 286 to 486)
|
First-order regression model was fit to data. Part of printout follows:
|
VARIABLE
|
DF
|
PARAMETER ESTIMATE
|
STANDARD ERROR
|
T FOR 0: PARAMETER=0
|
PROB > |T|
|
|
INTERCEPT
|
1
|
-373.526392
|
1258.1243396
|
-0.297
|
0.7676
|
|
SPEED
|
1
|
104.838940
|
22.36298195
|
4.688
|
0.0001
|
|
CHIP
|
1
|
3.571850
|
3.89422935
|
0.917
|
0.3629
|
Recognize and interpret estimate for speed β -coefficient, β ^1.
a) β ^1 = 105; For every $1 increase in price, we estimate speed to increase 105 megahertz, holding chip fixed.
b) β ^1 = 3.57; For every 1-megahertz increase in speed, we estimate price to increase $3,57, holding chip fixed.
c) β ^1 = 3.57; For every $1 increase in PRICE, we estimate speed to increase by about 4 megahertz, holding chip fixed.
d) β ^1 = 105; For every 1-megahertz increase in speed, we evaluate price (y) to increase $105, holding chip fixed.