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

Section: Chapter Questions

Problem 4SGR

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Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the 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, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.1 7.3 9.9 9.3 8.8 8.3 Weight (kg) 137 176 282 230 230 214 The regression equation is y=+x. (Round the constant to the nearest integers needed. Round the coefficient to one decimal place as needed.) The best-predicted weight for an overhead width of 1.8 cm, based on the regression equation, is: ____ kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct because a weight of zero does not…
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 1.6 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) 7.8 7.4 9.7 7.3 8.2 9.4 Weight (kg) 177 190 286 153 216 275 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 1.6 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 there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. B. The prediction cannot be correct because a negative weight does not make sense and…
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 2.4 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) 7.4 8.2 9.9 8.5 8.6 8.4 Weight (kg) 139 203 269 184 210 207 A Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =-220.7 + 49.7 x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.4 cm is - 101.4 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…
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 2.3 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 7.5 8.8 8.2 7.6 9.4 Weight (kg) 146 167 215 165 161 242 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = + x X. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.3 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. B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation.…
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 2.5 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) 7.6 7.4 9.8 8.8 9.3 7.3 Weight (kg) 142 163 256 188 231 156 The regression equation is y= + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.5 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? 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. B. The prediction cannot be correct because a negative weight does not make sense and because there is…
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 1.6 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) 7.9 8.1 9.2 9.4 7.1 7.6 Weight (kg) 145 183 222 203 133 159 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=nothing+nothingx. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.6 cm is nothing kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? A. 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…
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 2.3 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 8.3 8.8 8.1 9.9 9.8 Weight (kg) 121 195 210 156 250 253
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 119 7.4 157 9.9 9.3 8.8 8.4 253 202 204 194 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope 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 2.3 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) 7.8 175 7.3 9.5 274 7.4 156 9.9 294 9.2 183 256 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.)
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Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the 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, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. 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…