10.2.23 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 1.9 cm. Can the prediction be comect? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 8.2 7.3 9.7 9.4 83 7.5 195 184 287 246 220 190 Critical Values of the Pearson Correlation Coefficient r Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r -0.05 0.950 0.878 0.811 0.754 0.707 The regression equation is y= x. (Round to one decimal place as needed.) E-0.01 0.990 0 959 0.917 0.875 0.834 in NOTE: To test H p=0 against H p0, reject H if the absolute value of r is greater than the critical value in the table

10.2.23 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 1.9 cm. Can the prediction be comect? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 8.2 7.3 9.7 9.4 83 7.5 195 184 287 246 220 190 Critical Values of the Pearson Correlation Coefficient r Click the icon to view the critical values of the Pearson correlation coefficient r. Critical Values of the Pearson Correlation Coefficient r -0.05 0.950 0.878 0.811 0.754 0.707 The regression equation is y= x. (Round to one decimal place as needed.) E-0.01 0.990 0 959 0.917 0.875 0.834 in NOTE: To test H p=0 against H p0, reject H if the absolute value of r is greater than the critical value in the table

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

10.2.23
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 1.9 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)
9.4
8.3
7.5
- X
8.2
7.3
9.7
i Critical Values of the Pearson Correlation Coefficient r
195
184
287
246
220
190
Click the icon to view the critical values of the Pearson correlation coefficient r
Critical Values of the Pearson Correlation Coefficient r
k-0.05
0.950
0.878
0.811
0.754
0.707
0.666
0.632
0.602
0.576
0.553
0.532
0.514
0 497
0.482
0.468
0.456
10.444
The regression equation is y =+x.
(Round to one decimal place as needed.)
a-0.01
NOTE: To test H: p=0
against H,: p+0, reject
H, if the absolute value of
in
0.990
0.959
0.917
0.875
0.834
0.798
0.765
0.735
0.708
0.684
0.661
0.641
0.623
0.606
0.590
0.575
0.561
4
16
r is greater than the
critical value in the table.
17
19
10
11
12
13
14
15
16
17
18
19
20
Enter your answer in the edit fields and then click Check Answer.
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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) Weight_(kg)8.4 1957.6 1919.7 2769.5 2418.7 2288.3 216 The regression equation is y=---+----x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm is = ___ kg. Can the prediction be correct? What is wrong with predicting the weight in this case? A. The prediction cannot be correct because a negative weight does not make sense. The regression does not appear to be useful for making predictions. B. The…
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