ul Sprint11:43 AM86%DoneAttachment11Construct the indicated prediction interval for an individual y.12) The paired data below consists of heights and weights of 6 randomly selected adults. The equation12)of the regression line is y = -181.342 + 144.46x and the standard error of estimate is se = 5.0015. Findthe 95% prediction interval for the weight of a person whose height is 1.75 m.x Height (meters) | 1.61 1.72 1.78 1.80 1.67 1.88y Weight (kg)A) 58.5 < y < 84.5х546270846192B) 65.4 < y < 77.6C) 52.1 < y < 90.9D) 56.5 < y < 86.5

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Answered: ul Sprint 11:43 AM 86% Done Attachment…

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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Use the given data to find the best predicted value. If the prediction is unreliable, state so. 5) Eight pairs of data yield r = 0.742 and the regression equation y = 55.8 + 2.79x. Also, y = 71.125. What is the best predicted value of y for x = 8? Round to one decimal place.
Pick one or more and look at the image below A) estimated beta (slope) for this relationship is unlikely to be zero B) r-squared value for this relationship must be negative C) 20 is a plausible value for the population mean mpg for a vehicle with 50 hp
2
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 1.7 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) Click the icon to view the critical values of the Pearson correlation coefficient r. F n example Get more help. The regression equation is y=-159.5+ 43.2 x (Round to one decimal place as needed.) 3 # The best predicted weight for an overhead width of 1.7 cm is kg. (Round to one decimal place as needed.) I 80 E 26 D 7.8 161 C $ 4 Q F4 8.3 212 R F Stv 9.9 276 07 20 V F5 T 9.1 215 G 6 B 9.7 262 746 MacBook Air FO 7.3 166 Y H & 7 N F7 U X +00 8 J DII FB - M ( 9 K O < I ) O |qqqqqqqqi L F10 Clear all P . V : ; F11 { + [ command option Check answer = ? 11 I F12 1
e) Perform the F Test making sure to state the null and alternative hypothesis.
please answer the question and show work!! thank you also please make sure if writing on paper handwriting can be read thank you again :)
The data show The chest size and weight of several bears find regression equation, letting chest size be the independent variable then find the best predicted weight of a bear with a chest size of 51 inches is the result close to the actual weight of 422 pounds use a significance level of 0.05 Chest size : 45, 50, 43, 43, 52, 52. Weight : 352, 374, 275, 314, 440, 367.
Please answer this questions I need help from expert. I will give likes to your answer answr in 10 minutes. Thanks. Don't copy from any websites
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…
Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing. Call: lm(formula = score ~ list, data = hearing) Residuals: Min 1Q Median 3Q Max -14.7500 -5.5833 -0.2083 6.3333 16.4167 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.750 1.612 20.315 < 2e-16 *** listList2 -3.083 2.280 -1.352 0.17955 listList3 -7.500 2.280 -3.290 0.00142 ** listList4 -7.167 2.280 -3.144 0.00225 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared: 0.1382, Adjusted R-squared: 0.1101 F-statistic: 4.919 on 3 and 92 DF, p-value: 0.00325
Create two lists and make a scatterplot of the data and find the line-of-best-fit (regression line). For part a below, round all number values in your answer to the nearest tenth. a) What is the equation of the line-of-best-fit? ________________________ b) To the nearest thousandth, what is the value of the correlation coefficient, r ? _________ c) According to the regression equation from part a, what is the predicted salary for year 8? Round your answer to the nearest dollar. __________________
Using the data in the Excel spreadsheet "auto.xlsx", estimate a regression to answer the following question. How much does a 1 inch increase in LENGTH corresponds to a _________________ dollars increase in PRICE. Use negative values if it results in a decreased price. Round your answers to two decimals.

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