The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. Year Retail Sales 85.851 3 108.426 6 141.781 9. 169.256 12 202.297 15 222.266 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. 220 210 200 190 180 170 160 150 140 130 120 110 100 90 3 6 12 15 804 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2012.

The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. Year Retail Sales 85.851 3 108.426 6 141.781 9. 169.256 12 202.297 15 222.266 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. 220 210 200 190 180 170 160 150 140 130 120 110 100 90 3 6 12 15 804 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2012.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

See similar textbooks

Similar questions

The following are data on the average weekly profits(in $1,000) of five restaurants, their seating capacities, andthe average daily traffic (in thousands of cars) that passestheir locations: Seating Traffic Weekly netcapacity count profitx1 x2 y120 19 23.8200 8 24.2150 12 22.0180 15 26.2240 16 33.5 (a) Assuming that the regression is linear, estimate β0, β1,and β2.(b) Use the results of part (a) to predict the averageweekly net profit of a restaurant with a seating capacityof 210 at a location where the daily traffic count averages14,000 cars.
Let YY represent the profit (or loss) for a certain company XX years after 1970. Based on the data shown below, a statistician calculates a linear model Y=−0.81X+19.46Y=-0.81X+19.46. x y 2 18.91 3 16.34 4 16.47 5 14.1 6 15.43 7 12.86 8 12.79 9 13.12 10 10.55 11 11.28 12 8.81 13 9.14 14 9.67 15 6.1 Use the model to estimate the profit in `1977y =
Refer to the following nonlinear model which relates W to P, Q, and R: W = aPbQcRd The computer output form the regression analysis is: DEPENDENT VARIABLE: LNW R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 18 0.9023 43.12 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 2.50 0.45 5.56 0.0001 LNP -5.10 1.75 -2.91 0.0113 LNQ 12.40 3.20 3.88 0.0017 LNR -6.00 1.50 -4.00 0.0010 Based on the information in the table, the nonlinear relation can be transformed into the following linear regression model:
The following data is available from a batch fermentor where glucose is converted to ethanol. In the data, t is the time of the sample collection, and P is the concentration of ethanol. t 0 0.2 0.4 0.5 P 2 5 15 30 apply a black-box modeling approach to the data, and fit it to the following nonlinear model in the images. with A and B are model parameters, and x is the time, and y is the ethanol concentration in the fermentor. apply a transformation to the data for to make it solvable on pen-and-paper
Consider the following two a.m. peak work trip generation models, estimated by household linear regression: T = 0.62 + 3.1 X1 + 1.4 X2 R2= 0.590 (2.3) (7.1) (5.9) T = 0.01 + 2.4 X1 + 1.2 Z1 + 4.0 Z2 R2= 0.598 (0.8) (4.2) (1.7) (3.1) X1 = number of workers in the household X2 = number of cars in the household, Z1 is a dummy variable which takes the value 1 if the household has one car, Z2 is a dummy variable which takes the value 1 if the household has two or more cars. Compare the two models and choose the best. If a zone has 1000 households, of which 50% have no car, 35% have one car, and the rest have exactly two cars, estimate the total number of trips generated by this zone. Use the preferred trip generation model and assume that each household has an average of two workers
Wh ich of the following regressions represents the weakest linear relationship between x and y? Regression 1y=ax+by=ax+ba=11.9a=11.9b=0.1b=0.1r=0.7944r=0.7944 Regression 2y=ax+by=ax+ba=12.9a=12.9b=19.8b=19.8r=0.0346r=0.0346 Regression 3y=ax+by=ax+ba=9.8a=9.8b=15.6b=15.6r=0.2439r=0.2439 Regression 4y=ax+by=ax+ba=-9.4a=−9.4b=11b=11r=-0.5893r=−0.5893 Regression }1Regression 1 t{Regression }2Regression 2 {Regression }3Regression 3 {Regression }4Regression 4
The following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384.The yearly income of a 24-year-old male individual is _____. a. $46,800 b. $49,800 c. $13.80 d. $13,800
Consider the following log-wage regression results for women (W) and men (M) where wages are predicted by schooling (S) and age (A). wW = 2.23 + 0.077Sw + 0.017Aw and wM = 2.33 + 0.0745SM + 0.026AM. Sample means for the variables by gender are: women average a logged wage of 3.90, 12.7 years of schooling, and 40.8 years-old; men average a logged wage of 4.53, 14.2 years of schooling, and 43.9 years-old. Decompose the raw difference in average logged wages using the Oaxaca-Blinder decomposition. Specifically, decompose the raw difference into the portion due to differences in schooling, differences in age, and the portion left unexplained, possibly due to gender discrimination.
Six years of quarterly data of a seasonally adjusted series are used to estimate a linear trend model as TˆT^ t = 151.60 + 1.16t. In addition, quarterly seasonal indices are calculated as SˆS^ 1 = 0.87, SˆS^ 2 = 0.81, SˆS^ 3 = 1.07, and SˆS^ 4 = 1.23. Make a forecast for all four quarters of next year. (Do not round intermediate calculations. Round your answers to 2 decimal places.) yˆty^t Quarter 1 Quarter 2 Quarter 3 Quarter 4
Demand for milk production in US is given by the following regression function. Qd = 10.21 + 3.73 Pd + 1.32 C + 3.67 Y R2=0.87 (7.4) (1.17) (0.02) (2.47) Where Pd denotes the price for milk per liter, C denotes the cost of production of milk and Y denotes the average income level. When C is omitted from the regression equation; Qd = 23.12 + 0.34 Pd- 2.73 Y R2=0.73 (7.3) (3.23) (-5.25) What are the values given in brackets ? Compare above two regression functions t-stat values and R2’s to indicate which regression function is better than the other to be used to decide future milk demand. Will the adjusted R2 seem to decrease or increase after the exclusion of C from the regression function ? Why ?
To fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R2 Day Date Weekday Daily Demand Weekend 1 4/25/2016 Mon 297 0 2 4/26/2016 Tue 293 0 3 4/27/2016 Wed 327 0 4 4/28/2016 Thu 315 0 5 4/29/2016 Fri 348 0 6 4/30/2016 Sat 447 1 7 5/1/2016 Sun 431 1 8 5/2/2016 Mon 283 0 9 5/3/2016 Tue 326 0 10 5/4/2016 Wed 317 0 11 5/5/2016 Thu 345 0 12 5/6/2016 Fri 355 0 13 5/7/2016 Sat 428 1 14 5/8/2016 Sun 454 1 15 5/9/2016 Mon 305 0 16 5/10/2016 Tue 310 0 17 5/11/2016 Wed 350 0 18 5/12/2016 Thu 308 0 19 5/13/2016 Fri 366 0 20 5/14/2016 Sat 460 1 21 5/15/2016 Sun 427 1 22 5/16/2016 Mon 291 0 23 5/17/2016 Tue 325 0 24 5/18/2016 Wed 354 0 25 5/19/2016 Thu 322 0 26 5/20/2016 Fri 405 0 27 5/21/2016 Sat 442 1 28 5/22/2016 Sun 454 1 29 5/23/2016 Mon 318 0 30 5/24/2016 Tue 298 0 31 5/25/2016 Wed 355 0 32 5/26/2016 Thu 355 0 33 5/27/2016 Fri 374 0 34 5/28/2016 Sat 447 1 35 5/29/2016…
Consider the following table containing unemployment rates for a 10-year period. Unemployment Rates Year Unemployment Rate (%) 1 5.85.8 2 3.23.2 3 5.55.5 4 8.68.6 5 6.16.1 6 6.86.8 7 7.57.5 8 5.25.2 9 11.111.1 10 7.47.4 Step 1 of 2 : Given the model Estimated Unemployment Rate=β0+β1(Year)+εi,Estimated Unemployment Rate=�0+�1(Year)+��, write the estimated regression equation using the least squares estimates for β0�0 and β1�1. Round your answers to two decimal places.