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 1.8 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) Weight (kg) 7.7 8.1 8.9 9.7 7.2 8.2 150 196 224 232 148 196 = Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =+x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.8 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. O C. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
Percentage
A percentage is a number indicated as a fraction of 100. It is a dimensionless number often expressed using the symbol %.
Algebraic Expressions
In mathematics, an algebraic expression consists of constant(s), variable(s), and mathematical operators. It is made up of terms.
Numbers
Numbers are some measures used for counting. They can be compared one with another to know its position in the number line and determine which one is greater or lesser than the other.
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Addition
Before we begin to understand the addition of algebraic expressions, we need to list out a few things that form the basis of algebra.
Please help solve.
![prediction be correct? What is v
Find the regression equation, letting overhead width be the predictor (x) var
with predicting the weight in this case? Use a significance level of 0.05.
Critical Values of the Pearson Correlation Coefficientr
Overhead Width (cm)
7.7
8.1
8.9
9.7
7.2
Weight (kg)
150
196
224
232
148
Click the icon to view the critical values of the Pearson correlation coe
Critical Values of the Pearson Correlation Coefficient r
a = 0.01
0.990
0.959
0.917
0.875
0.834
0.798
0.765
0.735
NOTE: To test H,: p= 0
against H,: p 0, reject H,
lif the absolute value of r is
greater than the critical
value in the table.
a = 0.05
0.950
T0.878
0.811
0.754
0.707
0.666
10.632
0.602
0.576
0.553
0.532
0.514
0.497
0.482
0.468
0.456
0.444
0.396
In
The regression equation is y =+x.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 1.8 cm is kg.
(Round to one decimal place as needed.)
9
10
11
12
Can the prediction be correct? What is wrong with predicting the weight in t
.708
O A. The prediction cannot be correct because a negative weight does n
0.684
0.661
0.641
0.623
0.606
0.590
0.575
0.561
0.505
13
O B. The prediction cannot be correct because there is not sufficient evid
14
15
16
17
18
19
20
25
OC. The prediction cannot be correct because a negative weight does n
O D. The prediction can be correct. There is nothing wrong with predictin](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F51400f28-65ef-43af-a9e0-34c1c7929969%2F1f376a7b-6960-4aa5-bb89-9ad19bcf3d41%2Fgansh9p_processed.jpeg&w=3840&q=75)
![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 1.8 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)
7.7
8.1
8.9
9.7
7.2
8.2
Weight (kg)
150
196
224
232
148
196
E Click the icon to view the critical values of the Pearson correlation coefficient r.
....
The regression equation is y =+ x.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 1.8 cm is kg.
(Round to one decimal place as needed.)
Can the prediction be correct? What is wrong with predicting the weight in this case?
O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data.
O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data.
O C. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation.
O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F51400f28-65ef-43af-a9e0-34c1c7929969%2F1f376a7b-6960-4aa5-bb89-9ad19bcf3d41%2Fig3soqh_processed.jpeg&w=3840&q=75)
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