Hi I really need some help with this thank you! a. Is I(t), the amount of iodine as a function of time in days, better modeled with a linear function or an exponential function? Explain youranswer.EditFormat TableBIU2 でン12pt vParagraph v!! 5. On March 11, 2011, Japan had an earthquake and tsunami that caused a disastrous accident at the Fukishima nuclear power plant.Among other issues, the disaster resulted in high iodine levels in spinach as far as 60 miles from the power plant. The following tableshows the amount I, in thousands of becquerels per kilogram, of iodine that remained t days after the disaster.t= time in Days41= amount of iodine54.0049.5245.4141.6438.18

Name: Hi I really need some help with this thank you! a. Is I(t), the amount
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Description: Hi I really need some help with this thank you! a. Is I(t), the amount of iodine as a function of time in days, better modeled with a linear function or an exponential function? Explain youranswer.EditFormat TableBIU2 でン12pt vParagraph v!! 5. On Marc

Here we have the amount of I, in thousands of becquerels per kilogram of iodine that remained t days…

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Math

Algebra

help wit

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter4: Polynomial And Rational Functions

Section: Chapter Questions

Problem 5T

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To study the effect of temperature on yield in a chemical process, five batches were produced at each of three temperature levels. The results follow.
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A chemical reaction is run 12 times, and the temperature xi (in °C) and the yield yi (in percent of a theoretical maximum) is recorded each time. The following summary statistics are recorded: x⎯⎯=65.0, y⎯⎯=29.03,∑ni=1(xi−x⎯⎯)2=6032.0,∑ni=1(yi−y⎯⎯)2=835.42,∑ni=1(xi−x⎯⎯)(yi−y⎯⎯)=1988.6x¯=65.0, y¯=29.03,∑i=1n(xi−x¯)2=6032.0,∑i=1n(yi−y¯)2=835.42,∑i=1n(xi−x¯)(yi−y¯)=1988.6 Let β0 represent the hypothetical yield at a temperature of 0°C, and let β1 represent the increase in yield caused by an increase in temperature of 1°C. Assume that assumptions 1 through 4 for errors in linear models hold. Compute the error variance estimate s2. Round the answer to three decimal places.