I need help with the one in red please 09:067A myopenmath.comThe following table shows retail sales in drug storesin billions of dollars in the U.S. for years since 1995.YearRetail Sales85.851108.4266141.7819.169.25612202.29715222.266Let S(t) be the retails sales in billions of dollars in tyears since 1995. A linear model for the data isF(t)9.44t + 84.182.220-210-200190-180170-160-150140-130120-110100-90-612804Use the above scatter plot to decide whether thelinear model fits the data well.The function is not a good model for the dataThe function is a god model for the data 09:06 7DoneA myopenmath.comAA190-180-170160150-140-130120-110100-9036912804Use the above scatter plot to decide whether thelinear model fits the data well.The function is not a good model for the dataThe function is a good model for the data.Estimate the retails sales in the U. S. in 2016.282billions of dollars.Use the model to predict the year that correspondsto retails sales of $246 billion. 17Hint: Help

Name: I need help with the one in red please 09:067A myopenmath.comThe follo
Uploaded: 2024-03-20T07:07:12.000Z
Channel: Bartleby
Description: I need help with the one in red please 09:067A myopenmath.comThe following table shows retail sales in drug storesin billions of dollars in the U.S. for years since 1995.YearRetail Sales85.851108.4266141.7819.169.25612202.29715222.266Let S(t) be the

The linear model for the data is Ft=9.44t+84.182.

Answered: Use the model to predict the year that…

Math

Statistics

Use the model to predict the year that corresponds to retails sales of $246 billion. 17

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.
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
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:
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 =
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
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…
Suppose that in a certain chemical process the reaction time y (hr) is related to the temperature (°F) in the chamber in which the reaction takes place according to the simple linear regression model with equation y = 5.10 − 0.01x and ? = 0.07. (a) What is the expected change in reaction time for a 1°F increase in temperature? For a 12°F increase in temperature? 1°F increase hr 12°F increase hr (b) What is the expected reaction time when temperature is 190°F? When temperature is 240°F? 190°F hr 240°F hr (c) Suppose five observations are made independently on reaction time, each one for a temperature of 240°F. What is the probability that all five times are between 2.58 and 2.82 hours? (Round your answer to four decimal places.)(d) What is the probability that two independently observed reaction times for temperatures 1° apart are such that the time at the higher temperature exceeds the time at the lower temperature? (Round your answer to four decimal…
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
Snowpacks contain a wide spectrum of pollutants thatmay represent environmental hazards. The article“Atmospheric PAH Deposition: Deposition Velocitiesand Washout Ratios” (J. of EnvironmentalEngineering, 2002: 186–195) focused on the depositionof polyaromatic hydrocarbons. The authors proposeda multiple regression model for relating depositionover a specified time period (y, in mg/m2) to tworather complicated predictors x1 (mg-sec/m3) and x2 (mg/m2), defined in terms of PAH air concentrations forvarious species, total time, and total amount of precipitation.Here is data on the species fluoranthene andcorresponding Minitab output:obs x1 x2 flth1 92017 .0026900 278.782 51830 .0030000 124.533 17236 .0000196 22.654 15776 .0000360 28.685 33462 .0004960 32.666 243500 .0038900 604.707 67793 .0011200 27.698 23471 .0006400 14.189 13948 .0004850 20.6410 8824 .0003660 20.6011 7699 .0002290 16.6112 15791 .0014100 15.0813 10239 .0004100 18.0514 43835 .0000960 99.7115 49793 .0000896 58.9716 40656…
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.
Based on the data shown below, a statistician calculates a linear model y=2.41x+2.76y=2.41x+2.76. x y 4 12.5 5 14.35 6 16.9 7 20.65 8 22.1 9 23.95 Use the model to estimate the yy-value when x=9x=9y=y=
Assume that there is a positive linear correlation between the variable R (return rate in percent of financial investment) and the variable t (age in years of the investment) given by the regression equation R = 2.5t + 5.3. 1- If the investment continues to grow at a constant rate, what is the expected return rate when the investment is 7 years old?