For the assumed cubic and log-log regression models, Demand = Be + B1Price + B2Price? + B3Price3 + ɛ and In (Demand) = Bo + B1 regression results are available. Variable Cubic Model Log-Log Model Intercept 85,345.5 26.2770 -587.4415 N/A x2 1.3074 N/A x3 In(x) -.0010 N/A N/A -3. 2694 R2 0.9810 0.9820 Adjusted R2 Standard Error se 0.9798 0.9818 1,515.4580 0.2094 3.2694 In(Price) + (0.2094)2/2) is 0.96: Assuming that the sample correlation coefficient between Demand and Demand = exp(26.2770 $250 found by the model with better fit?
For the assumed cubic and log-log regression models, Demand = Be + B1Price + B2Price? + B3Price3 + ɛ and In (Demand) = Bo + B1 regression results are available. Variable Cubic Model Log-Log Model Intercept 85,345.5 26.2770 -587.4415 N/A x2 1.3074 N/A x3 In(x) -.0010 N/A N/A -3. 2694 R2 0.9810 0.9820 Adjusted R2 Standard Error se 0.9798 0.9818 1,515.4580 0.2094 3.2694 In(Price) + (0.2094)2/2) is 0.96: Assuming that the sample correlation coefficient between Demand and Demand = exp(26.2770 $250 found by the model with better fit?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
Transcribed Image Text:The following data show the demand for an airline ticket dependent on the price of this ticket.
Price
100
110
120
130
140
150
160
170
180
190
200
210
220
230
Demand 42,308 39,508 32,058 28,558 25,058 22,758 20,538 17,528 15,408 13,248 11,238 8,088 7,428 6,058
Price
240
250
260
270
280
290
300
310
320
330
340
350
360
370
Demand
5,438 4,918 4,458 4, 028 3,648 3,298 2,988 2,708 2,448 2,368 2,008 1,8081,638 1,488
Price
380
390
400
410
420
430
440
450
460
470
480
490
500
510
Demand
1,348 1,218 1,108
998
908
818
748
668
608
548
498
458
408
368
Price
520
530
540
550
560
570
580
590
600
610
620
630
640
650
Demand
338
328
318
308
288
268
258
248
228
208
188
168
168
158
For the assumed cubic and log-log regression models, Demand = B9 + B1Price + B2Price? + B3Price + e and In (Demand) = Ba + 81ln(Price) + €, respectively, the following
regression results are available.
Variable
Cubic Model
Log-Log Model
85,345.5
-587.4415
Intercept
26.2770
N/A
x2
1.3074
N/A
x3
-.0010
N/A
In(x)
N/A
-3.2694
R2
0.9810
0.9820
Adjusted R2
0.9798
0.9818
Standard Error se
1,515.4580
0.2094
Assuming that the sample correlation coefficient between Demand and Demand = exp(26.2770 - 3.2694 In(Price) + (0.2094)2/2) is 0.963, what is the predicted demand for a price of
$250 found by the model with better fit?
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