# With milk sales sagging of late, The Milk Processor Education Program (MPEP) decided to move on from the famous “Got Milk” ad slogan in favor of a new one, “Milk Life.” The new tagline emphasizes milk’s nutritional benefits, including its protein content. MPEP began collecting data on the number of gallons of milk households consumed weekly (in millions), weekly price per gallon, and weekly expenditures on milk advertising (in hundreds of dollars) for the period following the launch of the new campaign.  These data, in forms to estimate both a linear model and log-linear model, are available via the link below.  Use these data to perform two regressions: a linear regression and a log-linear regression.  Excel Data File Which model does a better job fitting the data? The ————— model. Suppose that the weekly price of milk is \$3.40 per gallon and MPEP decides to ramp up weekly advertising by 35 percent to \$150 (in hundreds). Use the best-fitting regression model to estimate the weekly quantity of milk consumed after this advertising increase.

With milk sales sagging of late, The Milk Processor Education Program (MPEP) decided to move on from the famous “Got Milk” ad slogan in favor of a new one, “Milk Life.” The new tagline emphasizes milk’s nutritional benefits, including its protein content. MPEP began collecting data on the number of gallons of milk households consumed weekly (in millions), weekly price per gallon, and weekly expenditures on milk advertising (in hundreds of dollars) for the period following the launch of the new campaign.  These data, in forms to estimate both a linear model and log-linear model, are available via the link below.  Use these data to perform two regressions: a linear regression and a log-linear regression.

Excel Data File

Which model does a better job fitting the data?

The ————— model.

Suppose that the weekly price of milk is \$3.40 per gallon and MPEP decides to ramp up weekly advertising by 35 percent to \$150 (in hundreds). Use the best-fitting regression model to estimate the weekly quantity of milk consumed after this advertising increase.

Instructions: Round your intermediate calculations and enter your response rounded to three decimal places.

million gallons per week

 Linear Model Log-Linear Model Q P A lnQ lnP lnA 4.76 2.46 472.68 1.56 0.90 6.16 0.90 4.28 326.41 -0.10 1.45 5.79 1.74 3.72 357.36 0.55 1.31 5.88 0.96 4.20 475.82 -0.04 1.43 6.17 2.38 4.14 494.25 0.87 1.42 6.20 1.28 4.59 458.62 0.25 1.52 6.13 2.86 3.30 421.67 1.05 1.19 6.04 1.87 4.34 534.85 0.63 1.47 6.28 2.19 3.31 524.75 0.78 1.20 6.26 1.38 3.35 370.35 0.32 1.21 5.91 0.21 4.53 420.16 -1.54 1.51 6.04 3.55 2.63 333.79 1.27 0.97 5.81 2.44 4.40 437.32 0.89 1.48 6.08 1.94 4.36 442.70 0.66 1.47 6.09 2.50 3.24 375.67 0.91 1.18 5.93 2.92 3.45 546.36 1.07 1.24 6.30 4.94 2.97 391.17 1.60 1.09 5.97 2.14 3.22 498.00 0.76 1.17 6.21 3.89 3.34 530.17 1.36 1.20 6.27 6.91 2.24 527.36 1.93 0.81 6.27 3.41 4.04 440.93 1.23 1.40 6.09 1.16 4.10 480.35 0.15 1.41 6.17 1.60 3.99 404.91 0.47 1.38 6.00 4.09 3.22 512.00 1.41 1.17 6.24 2.69 2.98 346.29 0.99 1.09 5.85 2.41 4.30 383.47 0.88 1.46 5.95 2.25 2.84 434.26 0.81 1.04 6.07 2.48 3.96 548.37 0.91 1.38 6.31 3.79 2.49 357.71 1.33 0.91 5.88 3.33 3.29 445.73 1.20 1.19 6.10 2.61 4.02 524.55 0.96 1.39 6.26 2.40 4.05 487.87 0.88 1.40 6.19 3.92 2.46 343.13 1.37 0.90 5.84 3.42 3.45 353.81 1.23 1.24 5.87 0.80 3.40 334.47 -0.23 1.22 5.81 5.79 2.95 330.57 1.76 1.08 5.80 3.58 2.69 363.91 1.28 0.99 5.90 1.58 3.79 383.71 0.46 1.33 5.95 1.14 3.37 430.37 0.13 1.21 6.06 1.04 4.64 501.84 0.04 1.54 6.22 4.88 2.66 447.12 1.59 0.98 6.10 4.31 2.25 404.38 1.46 0.81 6.00 2.23 3.94 449.29 0.80 1.37 6.11 1.38 4.42 327.99 0.32 1.49 5.79 1.62 3.13 332.39 0.49 1.14 5.81 1.38 4.45 450.16 0.33 1.49 6.11 6.20 2.38 467.40 1.82 0.87 6.15 4.17 3.69 528.60 1.43 1.31 6.27 4.08 4.02 533.73 1.41 1.39 6.28 0.08 4.30 355.81 -2.55 1.46 5.87 3.82 2.80 462.42 1.34 1.03 6.14 1.17 4.51 549.78 0.16 1.51 6.31 3.26 2.42 366.63 1.18 0.88 5.90 2.44 4.37 429.74 0.89 1.47 6.06 4.16 2.53 399.57 1.42 0.93 5.99 2.63 3.63 521.95 0.97 1.29 6.26 4.94 2.80 356.59 1.60 1.03 5.88 1.84 4.36 416.24 0.61 1.47 6.03 4.71 3.12 435.99 1.55 1.14 6.08 6.46 2.40 464.62 1.87 0.87 6.14 2.79 3.51 353.37 1.03 1.25 5.87 4.09 3.07 425.12 1.41 1.12 6.05 4.76 2.32 481.72 1.56 0.84 6.18 3.05 3.45 376.30 1.12 1.24 5.93 0.87 4.44 536.86 -0.13 1.49 6.29 3.12 2.50 493.52 1.14 0.92 6.20 1.34 3.11 454.69 0.29 1.13 6.12 1.93 3.24 487.07 0.66 1.17 6.19 1.64 2.87 461.69 0.50 1.05 6.13 4.39 2.97 410.84 1.48 1.09 6.02 5.76 2.33 480.66 1.75 0.84 6.18 4.40 2.82 381.62 1.48 1.04 5.94 6.22 3.14 456.97 1.83 1.14 6.12 1.10 3.89 461.39 0.09 1.36 6.13 4.12 2.67 430.43 1.42 0.98 6.06 5.40 2.73 438.53 1.69 1.01 6.08 2.75 4.52 336.00 1.01 1.51 5.82 5.12 2.28 519.90 1.63 0.83 6.25 3.94 3.25 536.25 1.37 1.18 6.28 5.69 2.18 439.75 1.74 0.78 6.09 0.44 4.27 352.57 -0.82 1.45 5.87 1.89 3.62 397.69 0.64 1.29 5.99 4.02 3.32 345.17 1.39 1.20 5.84 3.70 3.43 507.56 1.31 1.23 6.23 3.26 2.43 330.67 1.18 0.89 5.80 2.98 2.97 433.20 1.09 1.09 6.07 2.09 4.32 462.14 0.74 1.46 6.14 5.68 2.25 515.33 1.74 0.81 6.24 4.33 2.65 508.14 1.47 0.98 6.23 4.97 3.63 510.41 1.60 1.29 6.24 2.89 3.60 343.16 1.06 1.28 5.84 2.25 3.37 365.82 0.81 1.22 5.90 0.17 3.77 425.56 -1.79 1.33 6.05 3.96 2.87 347.36 1.38 1.06 5.85 4.08 2.97 326.06 1.40 1.09 5.79 3.49 3.94 527.12 1.25 1.37 6.27 4.21 4.10 475.28 1.44 1.41 6.16 2.25 4.09 475.69 0.81 1.41 6.16 2.40 3.93 536.42 0.88 1.37 6.28 1.61 4.10 325.89 0.48 1.41 5.79

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