MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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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 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 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 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 2 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…arrow_forwardUse the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r= -0.438 please dont copy content from other sourcesarrow_forwardI need help finding the answers for A,B,and Carrow_forward
- The null hypothesis: H, :0 H :0 The alternative hypothesis: Н The type of test statistic: (Choose one) v The value of the test statistic: (Round to at least three decimal places.) The critical value at the 0.05 level of significance: (Round to at least three decimal places.) Can we conclude that houses in Sun Beach have higher appreciation over the past three years than houses in North Arden? Yes Noarrow_forwardonly HANDWRITTEN answer needed ( NOT TYPED)arrow_forwardThe table shows a part of an output of a linear regression model predicting the average fare on different flight routes. Data Table Regression Table Coefficient Constant 95.80976147 COUPON −9.61654124 DISTANCE 0.080733811 PAX −0.000167343 What is the difference in prediction of the following two routes? Route A that is 3,000 miles, with COUPON=1.5 and PAX=6,000 Route B that is 3,000 miles, with COUPON=1.2 and PAX=6,000.arrow_forward
- Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r=0.926arrow_forwardThe best predicted circumference for a diameter 1.2 cm is?arrow_forwardThe regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]arrow_forward
- A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.271 b=39.101 ²-0.407044 r=-0.638 Assume the correlation is significant, and use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place) Aftarrow_forwardFind 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) 8.1 7.6 8.9 7.7 8.6 9.8 Weight (kg) 156 166 213 138 197 251 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.)arrow_forwardA sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept −12.201 6.560 −1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 What is the regression equation? Multiple Choice Ŷ = 2.195 − 12.201X Ŷ = −12.201 + 2.195X Ŷ = 12.201 + 2.195X Ŷ = 2.195 + 12.201Xarrow_forward
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