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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4Use the table of values you made in part 4 of the example to find the limiting value of the average rate of change in velocity.
- bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.(b) Find a linear model using the first and last data points. (Use x for income measured in dollars and y for ulcers per 100 people. Round your values to eight decimal places.)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=
- A supermarket has a chain of 12 stores in Kuwait. Sales figures and profits for the stores are given in the following table. Obtain a regression equation for the data, and predict profit for a store assuming sales of $20 million. Show all calculations in detail. Sales, x (in millions of dollars) Profits, y (in millions of dollars) 7 0.12 2 0.1 6 0.13 12 0.15 14 0.25 16 0.2 10 0.24 12 0.2 14 0.27 20 0.15 7 0.34 8 0.17The data in the following table show the amount of water in milliliters a professional tennis player loses for various times, in minutes, of play during a tennis match. Time of Workout(in minutes), x 10 20 30 40 50 60 Water Lost(in milliliters), y 500 900 1,200 1,500 2,000 2,200 (a) Use the points (10, 500) and (60, 2,200) to find a linear model that will predict the water lost y (in mL) by a player who has been playing for x minutes. y = (b) Use your linear model to estimate the amount of water lost (in mL) by a player who has been playing for 15 minutes. mLThe following table represents the survey result from the seven online stores. Use linear regression to find the linear equation that is best fit for the data, width x equal to the online advertising. Round your coefficient to three decimal places. Online Store Online Advertising (in thousand dollars) Monthly e-commerce Sales (in thousand dollars) 1 1.5 368 2 1.7 340 3 2.8 665 4 5 954 5 1.3 331 6 2.2 556 7 1.3 376
- The data in the following table show the amount of water in milliliters a professional tennis player loses for various times, in minutes, of play during a tennis match. Time of Workout(in minutes), x 10 20 30 40 50 60 Water Lost(in milliliters), y 600 900 1,200 1,500 2,000 2,200 (a)Use the points (10, 600) and (60, 2,200) to find a linear model that will predict the water lost y (in mL) by a player who has been playing for x minutes. (b) Use your linear model to estimate the amount of water lost (in mL) by a player who has been playing for 15 minutes.The below data on the production volume x and total cost y (in dollars) for a particular manufacturing operation were used to develop the estimated regression equation y = 1,217.33 + 7.68x. Production Volume Total Cost ($) $ (units) 400 Submit Answer 450 550 600 700 750 4,100 4,900 5,400 5,900 6,500 (a) The company's production schedule shows that 650 units must be produced next month. What is the point estimate of the total cost (in dollars) for next month? (Round your answer to the nearest cent.) $6209.33 7,000 (b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.) X to $ X (c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,700, should managers be concerned about incurring such a high total cost for the month? Discuss. Since $6,700 is within the prediction interval, managers should not be concerned about Incurring such a high total cost for one…) A real estate magazine reported the results of a regression analysis designed to predict theprice (y), measured in dollars, of residential properties recently sold in a northern Virginiasubdivision. One independent variable used to predict sale price is GLA, gross living area(x), measured in square feet. Data for 157 properties were used to fit the model,? = β0 + β1x.The results of the simple linear regression are provided below.y = 96,600 +22.5x s = 6500 r2 = -0.77 t = 6.1 (for testing β1)Interpret the estimate of β0 the y-intercept of the line.a. About 95% of the observed sale prices fall within $96,600 of the least squares line.b. There is no practical interpretation, since a gross living area of 0 is a nonsensicalvalue.c. All residential properties in Virginia will sell for at least $96,600.d. For every 1-sq ft. increase in GLA, we expect a property's sale price to increase$96,600.
