First-order regression model fit to data

Assignment Help Basic Statistics

Reference no: EM1321600

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.

Reference no: EM1321600

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