Coca-Cola Revenues ($ millions), 2005-2010Quarter200520062007200820092010QtriQtr2Qtr3Qtr45, 2066,3106,0375,5515,2266,4766,4545,9327,3799,0468, 3937,1267,5258,6748,4267,1696,1037,7337,6907,3318, 2678,0447,51010,494Click here for the Excel Data File(a-1) Use MegaStat or Minitab to deseasonalize Coca-Cola's quarterly data. (Round your answers to 3 decimal places.)20051.0450.95720060.8901.0841.0530.9272007e.9101.0991.043e.9522008e.9281.1291.0548.90920090.9321.0741.0320.95220100.9421.032meane.9201.0831.045e.940(a-2) State the adjusted four quarterly Indexes. (Round your answers to 3 decimal places.)Q1Q2Q3Q40.9231.8871.848e.942(a-3) What is the trend model for the deseasonalized time series? (Round your answers to 2 decimal places.)Yt= 154.07 O xp+ 5334.20(b) State the model found when performing a regression using seasonal binarles. (A negative value should be Indicated by a mlnussign. Round your answers to 4 decimal places.)Yt=Q1+Q2 +Q3(c) Use the regression equation to make a prediction for each quarter in 2011. (Enter your answers In millons rounded to 3 decimalplaces.)QuarterPredictedQ1Q2Q3Q4

Answered: Image /qna-images/answer/166d6bb5-44ef-4123-9647-1d503d76a15c.jpg

Answered: Coca-Cola Revenues ($ millions),…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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State Unemployment Rate(Previous Year) Unemployment Rate(Current Year) Alabama 6.8 6.1 Alaska 6.9 6.5 Arizona 6.8 6.1 Arkansas 6.0 5.0 California 7.5 6.2 Colorado 5.0 3.9 Connecticut 6.6 5.7 Delaware 5.7 4.9 Florida 6.3 5.5 Georgia 7.1 6.0 Hawaii 4.4 3.6 Idaho 4.8 4.2 Illinois 7.1 6.0 Indiana 6.0 4.8 Iowa 4.2 3.8 Kansas 4.5 4.2 Kentucky 6.5 5.3 Louisiana 6.4 6.3 Maine 5.6 4.4 Maryland 5.8 5.1 Massachusetts 5.7 4.8 Michigan 7.2 5.4 Minnesota 4.2 3.7 Mississippi 7.5 6.4 Missouri 6.1 5.0 Montana 4.7 4.2 Nebraska 3.3 3.0 Nevada 7.9 6.8 New Hampshire 4.3 3.4 New Jersey 6.8 5.8 New Mexico 6.7 6.5 New York 6.3 5.3 North Carolina 6.3 5.7 North Dakota 2.7 2.8 Ohio 5.8 4.9 Oklahoma 4.5 4.4 Oregon 6.8 5.6 Pennsylvania 5.9 5.3 Rhode Island 7.7 6.0 South Carolina 6.5 6.0 South Dakota 3.4 3.1 Tennessee 6.6 5.6 Texas 5.1 4.4 Utah 3.8 3.6 Vermont 3.9 3.6 Virginia 5.2 4.5 Washington 6.1 5.6 West Virginia 6.6…
Price SqFt 600000 2767 545000 2731 314900 2051 419000 2084 365000 2270 479000 1950 323000 1235 339900 2116 399000 1644 552000 2415 223017 1375 315777 1529 499000 2223 575000 2327 342000 1879 375000 1558 349000 2012 485000 1200 549888 1410 295000 1943 399000 1827 306999 1840 265200 1636 479000 2357 249700 1152 539500 2650 365000 2154 295900 1279 499000 2156 526000 2493 514900 2390 346000 1347 460000 2120 389000 1917 499000 1200 405000 2643 389900 1789 545000 2327 339000 1742 425000 1746 479000 1421 392000 2569 Can square feet explain prices? Provide evidence to support your answer. If the square feet is 2418, what is the expected price for the home? Please include the excel function.
I am struggling with this practice question can you assist?
A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The results are displayed in the accompanying table, and the regression equation and other values are provided. Use this information to complete parts (a) through (c) below. Y = -12,195.7+2.4032X; n n Σ Υ = 4,230,133, Σ γ = 520,438,426,235,Σ X,Y, = 234,279,400,152 i=1 i=1 Click the icon to view the data table. Since the value of n O D. It measures the variability in the actual per-year tuition from the predicted number of starting salary. b. Determine the standard error of the estimate. Syx = (Round to two decimal places as needed.) c. How useful do you think this regression model is for predicting starting salary? ₁2 the regression model is is i=1 for predicting starting salary.
Simple Accounting – Income Statement. Revenue Expense Net Income $23,943,930.50 ? $10,759,730.30 a. $17,351,830.40 b. $34,703,660.80 c. $13,184,200.20 d. -$13,184,200.20
Monthly Sales 6267.19 7058.06 7119.5 7147.18 7198.52 7298.09 7325.7 7335.68 7355.97 7481.05 7490.23 7530.08 7616.09 7682.69 7684.14 7704.12 7704.98 7779.28 7798.23 7815.15 7844.16 7890.21 7977.6 7993.16 8021.03 8028.37 8068.86 8082.42 8096.17 8119.25 8129.21 8190.68 8255.28 8282.44 8376.31 8392.4 8400.95 8451.16 8456.66 8505.35 8539.25 8543.65 8573.05 8641.78 8667.48 8751.08 8777.97 8800.08 8888.65 8907.03 9096.87 9241.74 9411.68 9450.73 9484.62 9514.57 9521.4 9524.91 9733.44 10123.24 Given the company’s performance record and based on the empirical rule of normal distribution (also known as the 68%-95%-99.7% rule), what would be the lower bound of the range of sales values that contains 68% of the monthly sales? What would be the upper bound of the range of sales values that contains 68% of the monthly sales?
Quarterly Glass Container Demand Year 1990 1991 1992 First Qtr. 17,446. 17,748. 17,595. Second Qtr. 17,785. 19,360. 18,338. Third Qtr. 18,971. Fourth Qtr. 19140. 17,802. 19,413. 18,315. 18,083. Question: Quarterly sales (in thousands of units) of a product over the last two years have been 26, 29, 30, 27, 28, 32, 33, and 30. Plot the data and forecast for the next period using Winter's Seasonal Model. Now, assume sales for the ninth quarter turned out to be 32. Update your parameter estimates and forecast sales for period 10.
Coca-Cola Revenues ($ millions), 2014 to 2019 Quarter 2014 2015 2016 2017 2018 2019 Quarter 1 10.58 9.94 10.28 9.12 7.63 8.02 Quarter 2 12.57 11.63 11.54 9.70 9.42 10.00 Quarter 3 11.98 11.77 10.63 9.08 8.78 9.51 Quarter4 10.87 10.13 9.41 7.51 5.36 9.07 What is the trend model for the deseasonalized time series? (Round your answers to 2 decimal places.) State the model found when performing a regression using seasonal binaries. (A negative value should be indicated by a minus sign. Round your answers to 4 decimal places.) Use the regression equation to make a prediction for each quarter in 2020. (Enter your answers in millions rounded to 3 decimal places.)
The following table gives the U.S. national debt for selected years from 1900 to 2013. U.S. Debt ($ billions) 3233 5225 Year 1900 1910 1920 1930 1940 1945 1955 1965 1975 1965 U.S. Debt ($ billions) 1 1 24 16 43 259 273 314 533 1823 Year 1990 1996 2000 2005 2009 2010 2011 2012 2013 5674 7933 11,957 13,529 15,476 16,067 16,856 (a) Using a function of the form ya b^x, with x=0 in 1900 and y equal to the national debt in billions, model the data. (Round your coefficients to four decimal places.) y= (b) Use the model to predict the debt in 2023. (Round your answer to the nearest billion.) $ billion (c) Predict the year in which the debt will be $71 trillion ($71,000 billion).
Year Population 1790 3,929,214 1800 5,308,483 1810 7,239,881 1820 9,638,453 1830 12,866,020 1840 17,069,453 1850 23,191,876 1860 31,443,321 1870 39,818,449 1880 50,155,783 1890 62,947,714 1900 75,994,575 1910 91,972,266 1920 105,710,620 1930 122,775,046 1940 131,669,275 1950 150,697,361 1960 179,323,175 1970 203,302,031 1980 226,545,805 1990 248,709,873 2000 281,421,906 2010 308,745,538 Use excel to calculate the Least Squares Regression Line for the census data? (need to include correct values for a and b) Using the line, you calculated in number 5, what would you predict the population to have been in 1985? EXPLAIN. Should you use the previously calculated Regression Line to predict population in the year 2200? EXPLAIN.

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