Using the data provided (below), I need to create a quantitative analysis using simple linear regression with 4 equations. The 4 module equations for the analysis will be using square, square root, reciprocal, and natural logarithms for the data transformation. I need to know how to use excel to create the 4 equations for analysis.PCONS = Personal Consumption Expenditures (Seasonally Adjusted, $ billions) DPI = Disposable Personal Income (Seasonally Adjusted, $ billions) TREND = Trend or Observation Year Quarter PCONS DPI TREND1981 2 1831.874 2125.848 11981 3 1885.734 2177.992 21981 4 1917.524 2217.133 31982 1 1958.099 2302.221 41982 2 1974.447 2339.999 51982 3 2014.155 2375.323 61982 4 2039.645 2412.547 71983 1 2085.671 2462.319 81983 2 2145.554 2497.228 91983 3 2184.589 2541.526 101983 4 2249.438 2582.521 111984 1 2319.895 2655.754 121984 2 2372.496 2732.912 131984 3 2418.165 2817.775 141984 4 2475.876 2892.421 151985 1 2513.523 2953.265 161985 2 2561.797 2995.748 171985 3 2636.008 3023.121 181985 4 2681.764 3106.975 191986 1 2754.148 3121.548 201986 2 2779.400 3176.623 211986 3 2823.648 3241.309 221986 4 2851.456 3279.358 231987 1 2917.201 3317.667 241987 2 2952.807 3342.776 251987 3 2983.513 3405.257 261987 4 3053.330 3405.848 271988 1 3117.358 3499.570 281988 2 3150.916 3577.374 291988 3 3231.896 3658.510 301988 4 3291.716 3737.002 311989 1 3361.899 3819.629 321989 2 3434.539 3894.693 331989 3 3490.172 3984.353 341989 4 3553.767 4026.199 351990 1 3609.399 4076.921 361990 2 3653.692 4143.873 371990 3 3737.948 4236.338 381990 4 3783.421 4304.603 391991 1 3846.700 4357.787 401991 2 3867.909 4377.750 411991 3 3873.562 4411.695 421991 4 3926.932 4468.169 431992 1 3973.269 4516.830 441992 2 4000.032 4587.352 451992 3 4100.401 4708.928 461992 4 4155.660 4787.216 471993 1 4226.971 4840.071 481993 2 4307.205 4896.050 491993 3 4349.515 4943.687 501993 4 4418.581 4992.314 511994 1 4487.189 5018.395 521994 2 4552.651 5082.376 531994 3 4621.223 5134.268 541994 4 4683.163 5214.909 551995 1 4752.761 5282.990 561995 2 4826.713 5383.972 571995 3 4862.436 5456.604 581995 4 4933.609 5503.922 591996 1 4998.662 5577.048 601996 2 5055.655 5634.300 611996 3 5130.615 5719.234 621996 4 5220.499 5810.282 631997 1 5274.505 5882.311 641997 2 5352.763 5953.959 651997 3 5433.105 6037.244 661997 4 5471.267 6106.041 671998 1 5579.179 6194.206 681998 2 5663.610 6305.191 691998 3 5721.342 6438.428 701998 4 5832.566 6538.701 711999 1 5926.846 6624.939 721999 2 6028.238 6694.894 731999 3 6102.532 6772.954 741999 4 6225.300 6825.258 752000 1 6328.908 6908.215 762000 2 6459.573 7053.392 772000 3 6613.597 7248.485 782000 4 6707.514 7361.611 792001 1 6815.369 7493.259 802001 2 6912.095 7561.729 812001 3 6986.889 7682.066 822001 4 7036.337 7704.706 832002 1 7064.655 7887.171 842002 2 7174.656 7792.234 852002 3 7209.940 7982.641 862002 4 7302.112 8097.846 872003 1 7390.943 8130.305 882003 2 7467.749 8216.348 892003 3 7555.778 8293.744 902003 4 7642.600 8396.995 912004 1 7802.573 8591.589 922004 2 7891.485 8653.708 932004 3 8027.745 8765.674 942004 4 8133.005 8926.832 952005 1 8264.342 9030.350 962005 2 8425.557 9222.476 972005 3 8522.955 9166.232 982005 4 8671.428 9308.890 992006 1 8849.203 9436.218 1002006 2 8944.885 9631.824 1012006 3 9090.652 9869.055 1022006 4 9210.245 9972.745 1032007 1 9333.029 10070.760 1042007 2 9407.453 10186.422 1052007 3 9549.431 10370.185 1062007 4 9644.740 10481.022 1072008 1 9753.799 10549.508 1082008 2 9877.752 10660.455 1092008 3 9934.253 10779.851 1102008 4 10052.843 11090.176 1112009 1 10080.964 10969.677 1122009 2 9837.261 10899.131 1132009 3 9756.126 10787.251 1142009 4 9760.216 10952.095 1152010 1 9895.401 10903.012 1162010 2 9957.092 10984.568 1172010 3 10040.485 11084.066 1182010 4 10131.767 11279.397 1192011 1 10220.606 11379.686 1202011 2 10350.484 11512.757 1212011 3 10485.358 11731.653 1222011 4 10612.125 11821.422 1232012 1 10705.367 11930.598 1242012 2 10761.586 12008.641 1252012 3 10922.442 12317.344 1262012 4 10964.858 12459.131 1272013 1 11014.245 12405.445 1282013 2 11125.711 12820.583 1292013 3 11223.180 12351.229 1302013 4 11239.592 12452.489 1312014 1 11330.946 12557.126 1322014 2 11475.123 12658.047 1332014 3 11574.229 12894.477 1342014 4 11756.878 13132.356 1352015 1 11915.359 13321.391 1362015 2 12044.547 13480.327 1372015 3 12099.073 13622.086 1382015 4 12255.535 13728.330 1392016 1 12389.313 13859.068 1402016 2 12446.001 13927.682 1412016 3 12551.649 14045.800 1422016 4 12707.522 14120.447 1432017 1 12841.158 14245.148 1442017 2 12979.520 14399.918 1452017 3 13153.192 14632.653 1462017 4 13241.268 14822.832 1472018 1 13370.922 14984.248 1482018 2 13596.036 15167.770 1492018 3 13755.546 15462.953 1502018 4 13939.903 15685.872 1512019 1 14086.264 15876.070 1522019 2 14191.412 16041.292 1532019 3 14276.587 16196.046 1542019 4 14497.320 16258.409 1552020 1 14645.313 16400.478 1562020 2 14759.183 16539.568 1572020 3 14545.460 16698.583 1582020 4 13097.348 18300.923 1592021 1 14394.780 17664.177

Project Description: One of the most important empirical relationships in macroeconomics is the link between personal consumption and disposable income. This relationship impacts economic growth and the effectiveness of fiscal and monetary policy. The purpose of this project is two-fold. The first is to outline the relationship between personal consumption and disposable income. The second is to provide a simple forecast of personal consumption You will not need to look for data as I will be providing you all with individual data sets. Your data set is different than others in the class, so you will want to work only with the data set I email you. These data sets all look the same and are randomly assigned but each results in different final models. The data set specific to you will be sent via email so look for this. Each data set includes quarterly data going from the second quarter of 1981 (1981:2) to the first quarter of 2021 (2021:1). The variables included are personal consumption (PCONS), disposable personal income (DPI) and trend (TREND). For the first portion of this project, you need to discuss the relationship between personal consumption and disposable income. You will want to include a discussion surrounding a simple linear regression and one that also includes trend. You will also want to consider an additional model that addresses any nonlinearity in DPI that might be useful. Only use the square, square root, reciprocal, and natural logarithm as the potential nonlinear transformations. Do not transform trend in this analysis but you may still include that variable, if you wish, in any of your work. The second part of this project is to provide a simple forecast of personal consumption (PCONS) using the simple and quadratic trend models. You want to forecast PCONS for the next two quarters (2021:2 and 2021:3) so there is only a forecast horizon of two