The table below shows the height, h(t), in meters, of an object that is thrown off the top of a building, tseconds after it is thrown.t0.511.5h(t) 90.175 100.9 109.17522.5115 118.3753119.3A) Using your calculator's quadratic regression feature to find an equation that best fits the data andexpress the height of the object as a function of the number of seconds that have passed since the objectwas thrown. Make sure to use function notation and use the correct variables. Round all numbers in youranswer to 1 decimal place.B) Using your regression equation, how high will the object be 2.8 seconds after it is thrown? Round to 3decimal places.metersC) Using your regression equation, how long will it take the object to reach 18 meters? Round to the 3decimal places.seconds

0.511.522.5390.175100.9109.175115118.375129.3

Answered: The table below shows the height, h(t),…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Using the regression to model annual income, a variable for years of education has a slope = 2000. This means that the model predicts each additional year of education will yield an extra 2,000 for the average person's salary, regardless of how many total years of education a person has. A. True B. False
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 1.5.
Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.
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=-0.794 b=31.311 r²=0.9801 r=-0.99 Use this to predict the number of situps a person who watches 8 hours of TV can do (to one decimal place)
A teacher wants to form a linear regression equation to predict a student's Score on the final based on the number of hours they spent studying for it. Final scores are in percents. y=2x + 60 R-Squared = 0.65 %3D What does the slope mean in terms of the situation? O For each additional hour a student studies, their grade on the final increases by 2%. O For each additional 2 hours a student student studies, their score on the final increases by 1%. O Every hour a student studies, increases their score on the final. O Always choose C. O The more a student studies, the better they will do on the final.
Statistics professor wants to use the number of hours (x) a student studies for a statistics final exam to predict the final exam score (y). A regression model was fit based on data collected from a class during the previous semester, with the following results: (y hat): y = 35.0 + 3X What is the slope? a. Yb. 35.0c. 3d. X
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (x) and number of sit ups a person can do (y ). The results of the regression were: y=ax+b a=-1.33 b=25.138 r2=0.712336 r=-0.844 Use this to predict the number of sit ups a person who watches 10 hours of TV can do, and please round your answer to a whole number.

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