Data_Analysis_Project Regression_2019 REGRESSIONS DATA ANALYSIS PROJECT EMSE 4765 SPRING 2019 J. René van Dorp1 www.seas.gwu.edu/~dorpjr Write a report addressing the data analyses questions below and...

1 answer below »
UPLOADED


Data_Analysis_Project Regression_2019 REGRESSIONS DATA ANALYSIS PROJECT EMSE 4765 SPRING 2019 J. René van Dorp1 www.seas.gwu.edu/~dorpjr Write a report addressing the data analyses questions below and hand in your report prior to You andApril 30th, 2019. may not work together on this project you should perform your analysis and report writing individually. Please upload your electronic files on Blackboard and provide me with a HARD COPY OF YOUR WRITTEN REPORT. The report should introduce the problem, detail your analysis steps and contain conclusions. 1 Department of Engineering Management and Systems Egineering, School of Engineering and Applied Science, The George Washington University, 800 22nd Street, N.W. Suite 2800,ß Washington D.C. 20052. E-mail: [email protected]. DAP: EMSE 4765 - SPRING 2019 J.R. van Dorp; ; Page [email protected] Question 1: The price of a real estate property can be determined by a number of attributes. The table below contains a sample subset of house price data (10 out of a total of 80 properties) provided in the excel file "Data_Regression_Project_2019.xlsx" Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 Pr op er ty P RI CE be dr oo m s ba th ro om s sq ft _l iv in g sq ft _l ot flo or s N um be rs o f t im es vi ew ed Q ua lit y Gr ad e sq ft _a bo ve sq ft _b as em en t Bu ilt o r R en ov at ed 1 440,000.00$ 3 2.5 1910 66211 2 0 7 1910 0 1997 2 213,000.00$ 2 1 1000 10200 1 0 6 1000 0 1961 3 563,500.00$ 4 1.75 2085 174240 1 0 7 1610 475 1964 4 1,550,000.00$ 5 4.25 6070 171626 2 0 12 6070 0 1999 5 1,600,000.00$ 6 5 6050 230652 2 3 11 6050 0 2001 6 350,000.00$ 3 2.25 1580 47916 1 0 7 1580 0 1979 7 540,000.00$ 3 2.25 2000 217800 2 0 8 2000 0 1996 8 535,000.00$ 3 1 1330 40259 1 0 7 1330 0 1977 9 600,000.00$ 2 2.5 2410 102366 1 0 7 1940 470 1989 10 275,000.00$ 3 1 1370 17859 1 0 7 1150 220 1930 Denoting the price of a real estate property please address the following] questions below in your written report. DAP: EMSE 4765 - SPRING 2019 J.R. van Dorp; ; Page [email protected] a. Motivate the use of as the dependent variable as opposed to P91Ð] Ñ ] Þ b. Find the estimated linear regression of on an appropriate set ofP91Ð] Ñ explanatory variables using the properties and interpret the\ ß á ß\ )!" 10 results Motivate your model development in your report as per the techniquesÞ described in the lecture notes on regression analysis. One is not to use the best subset regression or stepwise regressions procedure in motivating the steps leading to your final selected regression model. c. Perform and detail a diagnostic analysis in your report as per the techniques described in the lecture notes on the regression analysis of your final selected model chosen in Part b. d. Forecast the median and average of price of a real estate property for the] following values of the explanatory variables and provide a 95% prediction interval for and an approximate 95% confidence interval for ] IÒ] Ó Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 Forecast ?????? 3 2 1500 50000 2 0 6 750 0 2000 < ascii85encodepages="" false="" allowtransparency="" false="" autopositionepsfiles="" true="" autorotatepages="" none="" binding="" left="" calgrayprofile="" (dot="" gain="" 20%)="" calrgbprofile="" (srgb="" iec61966-2.1)="" calcmykprofile="" (u.s.="" web="" coated="" \050swop\051="" v2)="" srgbprofile="" (srgb="" iec61966-2.1)="" cannotembedfontpolicy="" error="" compatibilitylevel="" 1.4="" compressobjects="" tags="" compresspages="" true="" convertimagestoindexed="" true="" passthroughjpegimages="" true="" createjobticket="" false="" defaultrenderingintent="" default="" detectblends="" true="" detectcurves="" 0.0000="" colorconversionstrategy="" cmyk="" dothumbnails="" false="" embedallfonts="" true="" embedopentype="" false="" parseiccprofilesincomments="" true="" embedjoboptions="" true="" dscreportinglevel="" 0="" emitdscwarnings="" false="" endpage="" -1="" imagememory="" 1048576="" lockdistillerparams="" false="" maxsubsetpct="" 100="" optimize="" true="" opm="" 1="" parsedsccomments="" true="" parsedsccommentsfordocinfo="" true="" preservecopypage="" true="" preservedicmykvalues="" true="" preserveepsinfo="" true="" preserveflatness="" true="" preservehalftoneinfo="" false="" preserveopicomments="" true="" preserveoverprintsettings="" true="" startpage="" 1="" subsetfonts="" true="" transferfunctioninfo="" apply="" ucrandbginfo="" preserve="" useprologue="" false="" colorsettingsfile="" ()="" alwaysembed="" [="" true="" ]="" neverembed="" [="" true="" ]="" antialiascolorimages="" false="" cropcolorimages="" true="" colorimageminresolution="" 300="" colorimageminresolutionpolicy="" ok="" downsamplecolorimages="" true="" colorimagedownsampletype="" bicubic="" colorimageresolution="" 300="" colorimagedepth="" -1="" colorimagemindownsampledepth="" 1="" colorimagedownsamplethreshold="" 1.50000="" encodecolorimages="" true="" colorimagefilter="" dctencode="" autofiltercolorimages="" true="" colorimageautofilterstrategy="" jpeg="" coloracsimagedict="">< qfactor="" 0.15="" hsamples="" [1="" 1="" 1="" 1]="" vsamples="" [1="" 1="" 1="" 1]="">> /ColorImageDict < qfactor="" 0.15="" hsamples="" [1="" 1="" 1="" 1]="" vsamples="" [1="" 1="" 1="" 1]="">> /JPEG2000ColorACSImageDict < tilewidth="" 256="" tileheight="" 256="" quality="" 30="">> /JPEG2000ColorImageDict < tilewidth="" 256="" tileheight="" 256="" quality="" 30="">> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict < qfactor="" 0.15="" hsamples="" [1="" 1="" 1="" 1]="" vsamples="" [1="" 1="" 1="" 1]="">> /GrayImageDict < qfactor="" 0.15="" hsamples="" [1="" 1="" 1="" 1]="" vsamples="" [1="" 1="" 1="" 1]="">> /JPEG2000GrayACSImageDict < tilewidth="" 256="" tileheight="" 256="" quality="" 30="">> /JPEG2000GrayImageDict < tilewidth="" 256="" tileheight="" 256="" quality="" 30="">> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict < k="" -1="">> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile () /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description < ara=""> /BGR /CHS /CHT /CZE /DAN /DEU /ESP /ETI /FRA /GRE /HEB /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke. Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.) /HUN /ITA /JPN /KOR /LTH /LVI /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR /POL /PTB /RUM /RUS /SKY /SLV /SUO /SVE /TUR /UKR /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.) >> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ < asreaderspreads="" false="" cropimagestoframes="" true="" errorcontrol="" warnandcontinue="" flattenerignorespreadoverrides="" false="" includeguidesgrids="" false="" includenonprinting="" false="" includeslug="" false="" namespace="" [="" (adobe)="" (indesign)="" (4.0)="" ]="" omitplacedbitmaps="" false="" omitplacedeps="" false="" omitplacedpdf="" false="" simulateoverprint="" legacy="">>< addbleedmarks="" false="" addcolorbars="" false="" addcropmarks="" false="" addpageinfo="" false="" addregmarks="" false="" convertcolors="" converttocmyk="" destinationprofilename="" ()="" destinationprofileselector="" documentcmyk="" downsample16bitimages="" true="" flattenerpreset="">< presetselector="" mediumresolution="">> /FormElements false
Answered Same DayAug 04, 2021

Answer To: Data_Analysis_Project Regression_2019 REGRESSIONS DATA ANALYSIS PROJECT EMSE 4765 SPRING 2019 J....

Sudhanshu answered on Aug 06 2021
144 Votes
REGRESSIONS DATA ANALYSIS PROJECT
Introduction:
The concept of regression has always been fascinat
ing to human mankind. The best part of it is that it enables us to predict the variables depending upon the variables it is effected with. Thus in Regression we have the concept of independent and dependent variables. The variable which is effected by the other variables is called as dependent variable as is generally...
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here