The following sample was collected for a sample of 200 households to analyze food spending across different regions in Metro and non metro locations. Use this data to respond to respond to the questions given. observatio Annual Food SpeAnnual Household Inco Non mortgage household de Region 1 8909 56697.4462495273 23179.8774302355 NE 2 5684 35945.485150296 7051.8760957988 NE 3 10706 52686.830384846 16148.8353705558 NE 4 14112 74041.208942217 21839.1571843007 NE 5 13855 63181.8524096161 18865.9621607861 NE 6 15619 79064.4641406834 21899.1156225151 NE 7 2694 25980.5359644815 8773.5040915199 NE 8 9127 57424.0063238539 15766.162038932 NE 9 13514 72045.2477858634 27685.4301050538 NE 10 6314 38046.29285587 8544.8439055238 NE 11 7622 52407.7542345912 28057.2019300656 NE 12 4322 41405.2443980472 6997.6702115731 NE Location Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro 13 3805 29683.9802002069 4805.5230647209 NE Metro 14 15 16 17 18 19 20 21 22 23 6674 49246.0755290812 7347 41491.4224063978 2911 26702.7566122124 8026 48752.826412674 8567 55555.4767439025 10345 71483.3835848548 8694 50979.5775339444 8821 46403.1030685874 8678 51927.3543481686 14331 84769.0570884151 13591.6136145591 NE 4088.0147616612 NE 15876.0665408103 NE 16713.5861056217 NE 16783.2647684612 NE 21407.2495191125 NE 19113.8222043868 NE 7816.6860561817 NE 14414.5688343327 NE 17295.4694471322 NE Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro 24 9619 59061.8709944101 16686.9379108597 NE Metro 25 26 27 28 29 30 31 32 33 34 9286 57952.2658577479 8206 58354.7186376235 16408 81694.331735489 12757 69522.4027063522 17740 96132.2020378429 7739 57796.0266177251 15383 88276.0140887694 4579 32263.6254975805 11679 65928.4320741426 12877 69924.1060524946 14161.1005937797 NE 19538.044639345 NE 15186.9424052769 NE 14651.1249704112 NE 0 NE 22057.1289582644 NE 1895.8596819779 NE 7979.4708032626 NE 0 NE 27329.7425928526 NE Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro 35 16232 91108.0714507261 9875.7661953336 NE Metro 36 9621 54070.3122421109 19907.5234260875 NE Metro 37 8171 47238.2544304564 17818.8836480025 NE Metro 38 12128 77426.5221883251 31339.7956824396 NE Metro 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 8642 59805.4377202643 12400 60333.7249634496 9185 54113.9469936024 7862 40680.387327302 9775 58262.7190698985 6771 52008.3528094401 3059 39642.7288806444 13211 70309.2873157584 7408 46450.123944669 11581 76139.517336152 14233 80832.6901984401 3352 31898.7532510072 2630 21646.6118115932 9093 65924.3700181833 12652 65922.5999898626 9559 62810.6205970835 6112 42334.850463143 10431 65133.8292905711 12630 64620.6963462464 4578 36553.1594373169 9551 62909.650265763 10262 70726.8760428705 9551 57633.9392762748 10143 56549.0006666805 8955 59661.7850936308 10197 57349.5732774608 11234 56446.892383683 9320 61135.7741287793 9089 51525.8649455063 12300 79979.426118225 11484 66733.3050639136 11215 75359.1793403029 7204 40794.6277946757 5579 39128.2527137082 11723 75482.0848062227 9353 63998.1538571155 7761 45844.891388202 4261 38223.0767374276 9830 66787.0337766362 12386 77852.1868039388 8673 55824.5739132398 10944 57022.4677983787 9910 64262.5719693024 9928 75881.3008965808 4264 34342.9429044772 4962.584866886 NE 6632.4853514438 NE 18592.8181644762 NE 15201.7409956955 NE 1485.6320967432 NE 21713.256944355 NE 12179.3560573481 NE 13220.7512842491 NE 5602.4598643184 NE 33874.2845678702 NE 11477.9093246092 NE 2762.3609321366 NE 2663.4356683702 NE 11355.4458630155 NE 5132.342052972 NE 12613.1860860973 NE 3149.1888557561 NE 15195.9577715083 NE 21433.2007883116 NE 5501.7788008321 NE 11375.7617009163 NE 13286.583896121 NE 11856.5092315897 MW 16135.970517504 MW 11627.3358682577 MW 18432.0422385703 MW 10870.6169748679 MW 0 MW 4901.7396713374 MW 17269.8523667408 MW 15145.1056489954 MW 15610.721461568 MW 8975.4621497471 MW 6575.7561499834 MW 12508.4646480157 MW 0 MW 6670.595296577 MW 8576.2939139734 MW 1177.7681401931 MW 935.7034827466 MW 14167.0918901276 MW 9017.5601230352 MW 12767.6103536272 MW 17423.1966093124 MW 21322.8711481555 MW Metro Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 7971 41242.5641671143 8290 53021.4595274883 12669 66990.7157517736 7272 49719.3378931115 9784 58398.5742599179 9187 50477.1323397289 5866 39111.6453404538 9456 51886.4694690564 6270 34796.9676820212 9518 62348.2050437451 10968 78703.7597545713 8865 53620.1470013475 9226 51576.6551213164 4913 34760.8605553833 6976 60967.5898379646 8152 51281.0799186408 2887 25013.023408319 8062 59238.2544304564 8895 47343.7233772419 8444 52645.2813623764 6148 35308.6542114324 4563 34354.7731568105 8185 50629.7000759514 3391 29056.2888393178 7436 48721.0071594745 9522 50459.1538527166 11290 72805.4057818371 10403 56954.4659355306 4693 39342.5228442356 5626 38833.4968495473 11869 55020.5790103063 13055 77605.0514508096 8783 57936.8635144783 13031 63343.0592338845 3681 36478.9514908625 5549 40381.1987375957 4108 26308.8737331418 6314 41421.0206447751 7700 54578.5508367116 7479 40551.4475257659 9093 50368.7132498744 9863 54422.0165919687 8043 51835.9298539871 9552 73599.9585432 9286 51873.0324781208 21008.9135771035 MW 20150.5552149378 MW 9250.0531420286 MW 20837.9058358027 MW 16064.5252117014 MW 9406.823855394 MW 20409.2837202246 MW 11668.0164546473 MW 146.4561455883 MW 5201.0771150584 MW 17001.827713981 MW 32003.7495521014 MW 15921.6777001042 MW 17704.1622600576 MW 17798.8747645635 MW 8167.3258727882 MW 18763.4393705288 MW 10814.523889136 MW 11814.3086774275 MW 22469.1982702701 MW 17138.7407726492 MW 10612.388691667 MW 21186.8107407703 S 15734.8220136249 S 18363.1025401759 S 16477.9132910247 S 21237.8788682749 S 22217.5638236571 S 24696.4939514932 S 14370.6254357006 S 35575.7217812119 S 816.9715919998 S 18591.283392167 S 25530.8075474808 S 17950.3738445113 S 14256.7453303549 S 26580.500857532 S 22470.005446818 S 29064.876584895 S 31756.8444779376 S 6403.6337471102 S 24333.7533583748 S 26212.7012001351 S 36373.9398062229 S 29631.3326225267 S Metro Metro Metro Metro Metro Metro Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Outside Metro Outside Metro Outside Metro 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 7987 48003.4903981431 3875 36519.19210919 10746 75151.5724205819 6888 44974.0788704366 5479 48922.5330511108 6949 43768.6334305908 10650 75947.4206308368 5188 41423.1309352035 5311 40189.0471493825 4691 36771.7643156065 8056 59689.9771051539 11304 53653.8059198064 8112 59066.5800093411 8696 65961.534270682 5869 37254.1809256654 3776 33568.0365473963 11829 56933.5361382109 13087 88822.2391544259 10986 59635.3724680957 5762 38406.7949046585 11617 78627.105216874 9895 47709.703489425 16293 64442.6974454836 8185 58870.9348750999 13972 87954.0814112988 11243 54778.1653595156 4635 39825.4251901817 10063 49536.0747864469 8426 60101.9651436945 7436 49138.5150931891 11747 51051.5873832628 15397 70500.4346803616 6842 54894.3627209519 9678 60570.24471974 12852 57625.3155738231 10114 56955.5583876348 8496 61399.9570658198 6689 50531.5670275886 15696 72774.1168903594 9841 69981.1313702957 12529 66891.0266185703 10210 67431.1251309118 8868 64781.7479753575 6426 38986.9978233473 11096 64866.783085354 17261.3633958157 S 13578.5010251915 S 10658.6592346197 S 23710.9086492332 S 4593.5119169066 S 21221.3715394377 S 33357.100405707 S 33641.3175000111 S 17791.1781656498 S 5828.6739340169 S 19594.0126695554 S 23066.1223804113 S 240.3879125603 S 0S 10157.2775428998 S 14143.1153360521 S 0S 17565.3619998833 W 27863.479558588 W 18866.7238625989 W 11893.7303022598 W 22929.6754377778 W 31687.2226585168 W 35424.0180649562 W 11549.2591839633 W 12551.5450826846 W 19494.3547876086 W 12194.6593617089 W 13787.1163724107 W 22356.0912354209 W 4552.9484532075 W 12025.1295492984 W 16217.1043166076 W 4106.3410799019 W 31227.7914099162 W 25907.2474045446 W 1092.548486637 W 17106.0899128555 W 17793.4860084555 W 21606.7017072113 W 17688.7576935464 W 19994.963408832 W 14488.9734337688 W 17863.5057318083 W 5839.4514462329 W Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro Metro 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 10086 50421.1032369639 2587 27075.5359653267 12492 51784.2729296535 8456 54135.0664408383 6801 53291.111170256 6339 49804.3381310708 7802 52205.4916826164 9717 72840.5611374182 6026 46237.9092216725 5618 45938.1290055899 10217 77715.8961680834 8338 59710.9620532137 9048 42105.9850041638 4017 36461.8948171264 10906 53403.2814760576 15148 71290.4211163986 8830 66758.856786997 8481 57615.6288994389 11358 76220.6220516236 10553 78201.7641286802 6969 55163.6277728539 13219 61171.1552158521 3543 34092.7615070104 7326 50647.049773761 8458 59897.970347898 11766 52884.080458898 9908 73628.5040399234 8688.8301347848 W 17534.4846547581 W 20284.0576684743 W 22037.040494039 W 23342.2449718695 W 34943.0772666587 W 28579.2973636184 W 22348.7811717554 W 20165.2094483201 W 10537.741914019 W 18516.2614479545 W 7980.3792368388 W 19785.8592082106 W 9935.3835730348 W 18176.8380252528 W 6695.5588090117 W 20972.1699911519 W 28766.7669590213 W 1372.9911779286 W 5919.8053031345 W 24795.4128689948 W 21482.3283067439 W 25969.002094795 W 10749.6428108483 W 22939.6344047622 W 25969.9798015994 W 7111.8345340132 W Metro Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro Outside Metro QUESTION 1 CREATE a scatterplot that illustrates the re and another Scatter plot that illustrates th QUESTION 2 Run a regression analysis to predict Annua Include supplemental excel worksheets used in this file Regression 1 F statistic What proportion of variability on Food spending is exp Is household income a good predictor of Annual food s QUESTION 3 Run a regression analysis to predict Annua Regression 2 F statistic What proportion of variability on Food spending is exp Are these explanatory variables good predictors of Ann QUESTION 4 Run a regression analysis to predict Annua To incorporate a Categorical variable in a r Create a new column named "Metro" and TIP: You can Use Excel "IF" function to mak Regression 3 F statistic What proportion of variability on Food spending is exp Are these explanatory variables good predictors of Ann plot that illustrates the relatiionship between Annual Food Spending (Y) and Household Income (X) tter plot that illustrates the relationship between Annual Food Spending (Y) and Non mortgage household d analysis to predict Annual Food spending based on Household Income excel worksheets used in this file if you use any. Regression Equation ariability on Food spending is explained by this model ? a good predictor of Annual food spending ? Why ? analysis to predict Annual Food spending based on Household Income and Non-mortage debt Regression Equation ariability on Food spending is explained by this model ? y variables good predictors of Annual food spending ? Why ? analysis to predict Annual Food spending based on Household Income and Metro Location. Categorical variable in a regression model, you must code the variable as a "Dummy" variable umn named "Metro" and assign 1 if the location is Metro location and 0 if it's outside metro. Excel "IF" function to make the assignment of 1 or 0 based on location. Regression Equation ariability on Food spending is explained by this model ? y variables good predictors of Annual food spending ? Why ? Income (X) ortgage household debt