12) The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. a) Fill in the tableb)Based on these regression results, what is your forecast of US Exports to Mexico for March 2005?c) Which of the two forecasts do you think is more accurate? Explain. ForecastingForecasting..12) The following results are an autoregression for US Exports to Mexico where the dependent variable isthe lagged value of US Exports.LN ld bo ASUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R ltSquareStandard ErrorObservations0.5580.311o o b A ee o lo o t0.296129948YRAMMUSTUSTUOANOVASignificancedfMSRegressionResidualTotalF210BE8.0FRelotu3.827E-05aupe aeupe A bolmubAYona bsbnat2enotovieedO35017027775832153501702746168659247112600242SETStandardError17780.124UpperLower 95%AV 95%98790.812Coefficientst StatP-valueInterceptnoigle 6300Lagged Exports3.540.000919227210.5634.563.827E-050.3151888S08A1888S8esasaublassBased on these regression results, what is your forecast of US Exports to Mexico for March 2005?bnebnetarae 1owoainololiteofgeavatnlbna TWhich of the two forecasts do you think is more accurate? Explain.a.ca0.0se0 d bluow doirlw) c00s donM 1o) ootzaM ol nog 2U to aool oy ai rfW (d(on moi do TrendSeasonallyAdjustedRatio ofAdjustedForecastOriginalProjection12-monthOriginal toSeasonalSeasonalMonthSeriesMAMAIndexIndexSeriesDATEVALUEFeb-01Mar-0113356.811959.697.613685312253.715523.712322.113892.73112.713768.7Apr-01May-0114403.012390.513349.215108.013368.612458813800.3Jun-0115051.0110.86.13772.312527.2136903Jul-0111700.0109.3112405FofAug-0113764.089.213110.5Sep-0112664.01290812423.0101.913503.09Oct-0113895.0104.611873.1127324Forecasting10Nov-0113214.0110.612567.212800.814153.211Dec-0111319.0105.112567.912869.113530712Jan-0212061.513484.996.411740.112937.512473.513Feb-0289.494.094.012837.813005.912219412368.214Mar-0292.397.697.612672.413074.312760.413954.013271.7105.115Apr-02112.7112.712376.413142.714817.914113.013247.616Маy-02107.7107.713099.413211.014586.013204.114233.3110.517Jun-0214214.0110.813168.113279.414709.313134.3108.218Jul-02109.3109.313006.413347.814587.111607.013126.619Aug-0288.489.289.213006.311972.813416.213913.013139.020Sep-02105.9101.9101.913649.213334.013214.913484.613745.221Oct-02100.9104.6104.612743.813552.914180.714702.013282.122Nov-02110.7110.6110.613297.113621.315060.413908.013340.0A105.123Dec-02104.3105.113228.013689.714393.512161.013410.190.796.496.412613.413758.113264.624Jan-0312889.713479.295.694.013719.313826.512990.425Feb-0313292.613556.298.197.613619.513894.813561.326Mar-0315359.613673.3112.3112.713623.113963.215743.127Apr-0314646.413717.8106.8107.713594.514031.615117.328Мay-0315208.013769.6110.4110.813729.714100.015618.229Jun-0315003.213835.4108.4109.313728.614168.415483.830Jul-0312029.513870.686.789.213479.714236.712705.131Aug-0312989.713793.694.2101.912743.414305.114581.632Sep-03Oct-0314518.113892.3104.5104.613875.414373.515039.23315554.613963.4111.4110.614068.314441.915967.734Nov-0314598.214020.9104.1105.113884.414510.315256.235Dec-0313834.014160.397.796.414348.614578.614055.736Jan-0413290.814193.793.694.014146.214647.013761.337Feb-0414341.714281.2100.497.614694,414715.414362.238Mar-0417458.014456.0120.8112.715484.314783.816668.239Apr-0416014.014570.0109.9107.714863.914852.216001.440Мay-0416334.014663.8111.4110.814746.214920.516527.141Jun-0416438.014783.4111.2109.315041.514988.916380.642Jul-0413824.014932.992.689.215490.615057.313437.443Аug-0416060.015188.8105.7101.915755.515125.715418.116673.0108.5104.615934.915194.115897.8Sep-04Oct-044415368.44516975.015486.7109.6110.615352.915262.516875.0Nov-0416772.015667.9107.0105.115951.915330.816119.04615983.115847.0100.996.416577.715399.214846.947Dec-04Jan-0516037.197.194.016574.615467.614532.34815572.316185.999.697.616523.615536.015163.049Feb-0516127.0SUMMARYOUTPUTAVOWARegression StatisticsMultiple RR Square 3Adjusted R SquareStandard ErrorObservations0.8030,645biee0.638732to49Significance nFANOVASS458238812518692471010805MS45823881535892df863.76219E-121RegressionResidualTotal4748StandardError212.4P-value0.0Lower 95%11758.053.5Upper95%12612.683.3Coefficients12185.3t Stat57.4Intercept9.20.068.47.4Trend

12) a) The objective is to find the missing value in the table. The formula for ratio to moving…

The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. a) Fill in the table b)Based on these regression results, what is your forecast of US Exports to Mexico for March 2005? c) Which of the two forecasts do you think is more accurate? Explai

The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. a) Fill in the table b)Based on these regression results, what is your forecast of US Exports to Mexico for March 2005? c) Which of the two forecasts do you think is more accurate? Explai

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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The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
Show step by step on EXCEL. I am having a hard time on this.
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