Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.2 7.1 9.9 7.5 8.4 9.8 Weight (kg) 136 161 275 147 207 272 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r n α=0.05 α=0.01 NOTE: To test H0: ρ=0 against H1: ρ≠0, reject H0 if the absolute value of r is greater than the critical value in the table. 4 0.950 0.990 5 0.878 0.959 6 0.811 0.917 7 0.754 0.875 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 19 0.456 0.575 20 0.444 0.561 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 0.312 0.402 45 0.294 0.378 50 0.279 0.361 60 0.254 0.330 70 0.236 0.305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.256 n α=0.05 α=0.01 The regression equation is y=_____+_____x. (Round to one decimal place as needed.)
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.2 7.1 9.9 7.5 8.4 9.8 Weight (kg) 136 161 275 147 207 272 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r n α=0.05 α=0.01 NOTE: To test H0: ρ=0 against H1: ρ≠0, reject H0 if the absolute value of r is greater than the critical value in the table. 4 0.950 0.990 5 0.878 0.959 6 0.811 0.917 7 0.754 0.875 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 19 0.456 0.575 20 0.444 0.561 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 0.312 0.402 45 0.294 0.378 50 0.279 0.361 60 0.254 0.330 70 0.236 0.305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.256 n α=0.05 α=0.01 The regression equation is y=_____+_____x. (Round to one decimal place as needed.)
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
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.
|
Overhead Width (cm)
|
7.2
|
7.1
|
9.9
|
7.5
|
8.4
|
9.8
|
|
|---|---|---|---|---|---|---|---|
|
Weight (kg)
|
136
|
161
|
275
|
147
|
207
|
272
|
|
LOADING...
Critical Values of the Pearson Correlation Coefficient r
|
n
|
α=0.05
|
α=0.01
|
NOTE: To test
H0:
ρ=0
against
H1:
ρ≠0,
reject
H0
if the absolute value of r is greater than the critical value in the table. |
|---|---|---|---|
|
4
|
0.950
|
0.990
|
|
|
5
|
0.878
|
0.959
|
|
|
6
|
0.811
|
0.917
|
|
|
7
|
0.754
|
0.875
|
|
|
8
|
0.707
|
0.834
|
|
|
9
|
0.666
|
0.798
|
|
|
10
|
0.632
|
0.765
|
|
|
11
|
0.602
|
0.735
|
|
|
12
|
0.576
|
0.708
|
|
|
13
|
0.553
|
0.684
|
|
|
14
|
0.532
|
0.661
|
|
|
15
|
0.514
|
0.641
|
|
|
16
|
0.497
|
0.623
|
|
|
17
|
0.482
|
0.606
|
|
|
18
|
0.468
|
0.590
|
|
|
19
|
0.456
|
0.575
|
|
|
20
|
0.444
|
0.561
|
|
|
25
|
0.396
|
0.505
|
|
|
30
|
0.361
|
0.463
|
|
|
35
|
0.335
|
0.430
|
|
|
40
|
0.312
|
0.402
|
|
|
45
|
0.294
|
0.378
|
|
|
50
|
0.279
|
0.361
|
|
|
60
|
0.254
|
0.330
|
|
|
70
|
0.236
|
0.305
|
|
|
80
|
0.220
|
0.286
|
|
|
90
|
0.207
|
0.269
|
|
|
100
|
0.196
|
0.256
|
|
|
n
|
α=0.05
|
α=0.01
|
|
The regression equation is
y=_____+_____x.
(Round to one decimal place as needed.)
Expert Solution
Step 1
State the hypotheses.
Use EXCEL to obtain the value correlation coefficient r:
EXCEL procedure:
- Go to EXCEL
- Go to Data>Data Analysis.
- Choose Correlation.
- Enter the range as $A$1:$B$7.
- Check the option Labels in First row.
- Click OK.
EXCEL output:
From the EXCEL output, the value of the correlation coefficient is 0.9821.
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