The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,231 +4.417x, - 4.677x,, where x, is the number of acres planted and x, is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 36,100, x, = 36,500 (b) x, = 37,800, x2 = 38,100 (c) x, = 38,800, x2 = 39,000 (d) x, = 42,100, X2 = 42,200

The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,231 +4.417x, - 4.677x,, where x, is the number of acres planted and x, is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 36,100, x, = 36,500 (b) x, = 37,800, x2 = 38,100 (c) x, = 38,800, x2 = 39,000 (d) x, = 42,100, X2 = 42,200

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 1TI: Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use...

See similar textbooks

Similar questions

Does Table 1 represent a linear function? If so, finda linear equation that models the data.
Find the slope ofthe line in Figure l.
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________
Can the average rate of change of a function be constant?
The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,121 +4.505x₁ -4.772x₂, where x₁ is the number of acres planted and x2 is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x₁ =35,100, x₂ =35,500 (b) x₁ = 36,800, X2=37,100 (c) x₁ = 37,900, x₂ = 38,000 (d) x₁ =41,100, x₂ = 41,200 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (b) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (c) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (d) The predicted yield is pounds per acre (Round to one decimal place as needed.)
The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,677 + 4.556x, - 4.745x2, where x, is the number of acres planted and x, is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 36,600, x2 = 37,000 (b) x, = 38,400, xX2 = 38,600 (c) x4 = 39,400, x2 = 39,500 (d) x, = 42,600, x2 = 42,700 %3D %3D %3D %3D 2 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) th (b) The predicted yield is pounds per acre. 12 (Round to one decimal place as needed.) or (c) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (d) The predicted yield is ns pounds per acre. (Round to one decimal place as needed.) uest Que Quest Enter your answer in each of the answer boxes. 09 EGO DEC tv cBook Pr ....
The equation used to predict annual cauliflower yield (in pounds per acre) is y=24,209+4.556x₁ -4.785x2, where x, is the number of acres planted and x₂ is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x₁ = 36,100, x₂ = 36,500 (b) x₁ = 37,800, x₂ = 38,100 (c) x₁ = 38,900, x₂=39,000 (d) x₁ = 42,100, x₂ = 42,200 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (b) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (c) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (d) The predicted yield is pounds per acre. (Round to one decimal place as needed.) BEEDID
The equation used to predict annual cauliflower yield (in pounds per acre) is y=24,786+4.563x, -4.736x2, where x, is the number of acres planted and x₂ is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, 37,000, x₂=37,400 (b)x₁=38,700, x₂ = 39,000 (c) x₁=39,700, x₂-39,900 (d) x, 43,000, x₂=43,100 (a) The predicted yield is pounds per acre. (Round to one decimal place as needed.) CIT
The equation used to predict annual cauliflower yield (in pounds per acre) is y = 23,482 +4.455x, - 4.602x,, where x, is the number of acres planted and x, is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 37,000, x, = 37,400 (b) x, = 38,800, x, = 39,000 (c) x, = 39,800, x, = 39,900 (d) x, = 43,000, X, = 43,100 (a) The predicted yield is (Round to one decimal place as needed.) pounds per acre. (b) The predicted yield is pounds per acre. (Round to one decimal place as needed.) (c) The predicted yield is (Round to one decimal place as needed.) pounds per acre. (d) The predicted yield is pounds per acre. (Round to one decimal place as needed.)
The equation used to predict annual cauliflower yield (in pounds per acre) is y = 24,321 +4.534x₁ - 4.671x2, where x₁ is the number of acres planted and x2 is the number of acres harvested. Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x₁ = 36,200, x₂ = 36,500 (b) x₁ = 37,800, x₂ = 38,100 (c) x₁ = 38,900, x₂ = 39,000 (d) x₁ = 42,100, x₂ = 42,200 (a) The predicted yield is pounds per acre (Round to one decimal place as needed.) **
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 51.3 + 0.4x, +5.2x,, where x, is the tree's height (in feet) and x, is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. (a) x, = 71, x2 = 8.9 (b) x, = 65, x2 = 11.4 (c) x, = 81, x2 = 17.2 (d) x, = 88, X2 = 2 %3D (a) The predicted volume is cubic feet. (Round to one decimal place as needed.)