-r Question 2 Retaken O f 1 point This linl-t has an excel data set for you to evaluate. Use this data set to find a regression equation using a log-linear solution. Use the mathematical method as demonstrated in class. Enter the values for the non-linear regression equation parameters below. Use 6 digits after the decimal place. Values can be rounded or truncated, either answer is considered correct. Answer for blank # 1: Answer for blank # 2: 4 Question 3 Retaken O f 1 point This linl-t has a data set for you to evaluate. Find the standard error of a non-linear regression line that can be used to model this data. Use the method taught in lecture for using the residuals to calculate this value. Enter the answer below with 6 digits after the decimal place. The answer can be rounded or truncated, both are accepted as correct. Answe r: A X y -2.36 4470 -2.8 3770 A 0.45 2170 5 3.98 1260 6 5.02 770 6 600 6.97 410 LO 8 360 10 8.84 320 11 9.81 290 12 10.84 200 13 11.6 140 14 12.66 50 15 14 150 16 15.77 120 17 17.44 60 18Exponential Model: Reminder y = qebx Iny = Ina + bx do = y - a1x K a1 K = do + a1x This transformed equation is now a linear relationship between K and x. This particular transformation is referred to as log-linear, or log-level, to differentiate it from other common log transformations. b = a Important step : the original constants of the model are found as since a,=In(a)Exponential Simple Regression Model example In(y) = In(a) + bx y = aex Reminder K = ap tax Y Transform In(y) 4000 10 2000 Co = y - ax 0.5 1.5 2 2 5 0.5 1.5 2 2.5 X V In(vi xInvi a1 = 0.841406 (In(y) - In(b)): 0.4 750 6.62 0. 16 2.648 0.8 a0 = 6.252722 6.589551 0.03052 0.00083163 1000 6.01 0.84 5.528 6.02624 -0.01849 0.00034170 1.2 1400 7.24 1.44 8.688 7.28293 -0.01870 0.00034878 1.6 2000 7.6 2.56 12 16 In(y) 7.59062 0.00128 0.00000165 2 2700 7.9 4 15.8 = 6.2527 7.936309 -0.03530 0.00124626 2.3 3750 8.23 5.20 18.929 + 0.84140 x 8. 188827 0.04068 0.00165522 sum 8.3 11600 44.5 14.09 63.753 0.00452623 mean 1.383333 1933.333 7.415867Exponential Simple Regression Model example Now switch back to the original equation Reminder Our original data Since do = In(a) : a = edo = (6.252722 = 519 1.2 1400 b = a, = 0.841406, therefore the original exponential equation is y = 519e0.841406x Verify the equation with x = 1.2 y = 5190.841406*1.2 = 1424.8 SSE=0.00452623, with standard error=0.03363864