Years20022003200420052006200720082009201020112012201320142015201620172018201920202021Ethiopia (GDP) Germany (GDP)7850809498.17 2078484517474.518623691300.04 2501640388482.3510131187261.44 2814353869359.0812401139453.97 2846864211175.1015280861834.60 2994703642023.5319707616772.80 3425578382921.5827066912635.22 3745264093617.193411261212652.3432437389116.0429933790334.34 3399667820000.0131952763089.33 3749314991050.593527143188785.1643310721414.0847648211133.22 3733804649549.0355612228233.52 3889093051023.5264589334978.80 3357585719351.5674296618481.09 3469853463945.5381770791970.98 3690849152517.6584269348327.35 3974443355019.6095912590628.14 3888226035921.56107657734392.45 3889668895299.62111271112329.98 4259934911821.64By letting the GDP (gross domestic product) be the dependent variable (y), and the year (2002-2021) be the independent variable (x), answerthe following question:Determine the correlation between the GDP and year and develop the estimated equation for Ethiopia and Germany. Refer to the summaryoutput of the two countries in the image attached below and interpret the coefficient for the independent variable. Determine whether theregression model is significant at 1% significance level. SUMMARY OUTPUT (Germany)Multiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotalInterceptRegression StatisticsSUMMARY OUTPUT (Ethiopia)ANOVAMultiple RR SquareAdjusted R SquareStandard ErrorObservationsRegressionResidualTotalInterceptRegression Statistics0.8451432440.7142671040.6974592862.48156E+112002df19dft StatP-value Lower 95%Upper 95%Lower 95.0% Upper 95.0%-6.351346854 7.23515E-06 -1.76948E+14 -8.87032E+13 -1.76948E+14 -8.87032E+132002 67758187612 10394105434 6.518905166 5.25188E-06 45828542050 89687833173 45828542050 89687833173Coefficients Standard Error-1.32826E+142.0913E+130.9807726590.9619150080.95967471468025162891718 3.66385E+24119SSMSF2.61697E+24 2.61697E+24 42.496124571.04688E+24 6.15813E+221Significance F5.25188E-06SSMSFSignificance F1.98688E+22 1.98688E+22 429.370057 1.67599E-137.86662E+20 4.62742E+191718 2.06554E+22Upper 95% Lower 95.0% Upper 95.0%Coefficients Standard Error t StatP-value Lower 95%-1.18287E+13 5.73273E+11 -20.63359563 1.79653E-13 -1.30382E+13 -1.06192E+13 -1.30382E+13 -1.06192E+135904022990 284926053.4 20.72124651 1.67599E-13 5302881564 6505164416 5302881564 6505164416

Given is a regression output for relationship b/w GDP and year for Germany and Ethiopia.

Answered: By letting the GDP (gross domestic…

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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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 39 43 46 61 64 Bone Density 352 346 321 314 312 Table Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 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.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table Step 3 of 6 : Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.
One of the biggest changes in higher education in recent years has been the growth of online universities. The Online Education Database is an independent organization whose mission is to build a comprehensive list of the top accredited online colleges. The following table shows the retention rate (%) and the graduation rate (%) for 29 online colleges.a). Use Excel Data Analysis Tool – Regression to get the relationship between the two variables;b). Create a scatter diagram for the two variables and display regression equation and R square on chart, then explain the relationship between the variables;c). Did the estimated regression equation provide a good fit?d). Suppose you were the president of South University. After reviewing the results, would you be able to use the regression result for forecasting. College RR(%) GR(%) Western International University 7 25 South University 51 25 University of Phoenix 4 28 American InterContinental University 29 32 Franklin…
The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression eqation, using the first variable (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140. Use a significance level of x Systolic Diastolic 116 82 124 88 112 72 144 95 138 96 112 76 145 88 124 76 130 90 129 94 1)Find the correlation coefficient. 2)State the appropriate critical r value to use to test the claim that the correlation between systolic blood pressure and diastolic blood pressure is significantly different from 0 3)The correlation between diastolic blood pressure and systolic blood pressure is: significant, not significant, weak, none 4)The best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140 is closest to 1pts
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1.5 2.5 3 4 4.5 6 Overall Grades 89 86 81 79 72 67 62 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table Step 1 of 6 : Find the estimated slope, y instercept and correlation coefficient. Round your answers to three decimal places.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1 1.5 2 3 4.5 5.5 Overall Grades 100 97 96 85 83 72 67 Table Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places

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