Advertising Spend vs. Revenue (R² = 0.954) · scatter-regression-linear · python · pygal · anyplot.ai40406060808010010012012014014016016018018020020022022024024026026028028030030032032010102020303040405050Advertising Spend vs. Revenue (R² = 0.954) · scatter-regression-linear · python · pygal · anyplot.aiAdvertising Spend ($K)Revenue ($K)25.59970654: 159.26809851140.5528671416055834.538747384172357.28928685: 286.91856392831.444600008731210.0060192029875545.2596668: 249.69958822189.5684724545895392.100682124592837.92621663: 237.14525471798.2704451480229453.5230366180867313.58102522: 96.82784733499.260408602448361140.029058732695813.57969862: 125.6301994499.1896236052945999.11291330769398.194598669: 25.539742211.85161237094911488.807692307692452.63968802: 291.936812583.3514364912953185.4541394438681438.06132565: 211.64894521805.4795881624902578.264295316776843.94399178: 231.76022992119.3667217937523479.86938979229246.132147186: 64.85957103101.803384302137811296.43455825980758.34504187: 267.61095212887.777546400117304.46893741786250.78434524: 264.65389412484.3541090974086318.9364091150771416.67865109: 118.1498213664.54344204731481035.710829071618315.0003732: 130.6038095574.9939376705147974.779415980699415.08724804: 95.07247706579.62940322945371148.61724661213921.73332336: 120.4204026934.25050278249881024.60195965550533.86160374: 181.73175041581.3909451267493724.634833133619428.75697603: 183.75932781309.0183634867983714.714865733094421.01760271: 137.5984924896.0611004223408940.557774861364738.65190921: 203.26309871836.9919293084622619.292234987936112.67216234: 102.5307605450.76532822886851112.127429578273421.06795567: 133.659992898.7478321644394959.826975638064825.14990138: 168.12515621116.552176341291791.205396674016930.08384913: 160.74875031379.8176212926817827.294625682183848.18467788: 250.75159962345.6411310166436386.953692979665915.98205802: 101.4595201627.37464873678861117.368497007374333.28289411: 161.75804391550.5121731673105822.356634716231337.58280129: 213.21105091779.9464989377157570.62165854667437.5547727: 74.45094932177.711796325088591249.50852766241838.41496685: 211.80088981824.3491658039343577.520903235521714.3788268: 96.92003489541.82948272867361139.5780290339858.577837614: 48.98137967232.30046518787311374.118961053516657.18870455: 290.49642512826.077732288971192.5012541849466758.10976182: 296.13604312875.223480137196164.909298481539549.46185415: 248.08353822413.788666404325400.007242283296821.7537573: 132.1641408935.3408164707449967.14546063793910.37196627: 89.98396027328.031531429738151173.512927702120842.63281646: 265.06230192049.4050660109465316.93826497067229.20838716: 172.50098151333.104745851063769.796574201155211.71210292: 93.51158045399.538504801762771156.25396785972232.23473006: 181.93973181494.5842649914312723.61727974515166.891368661: 32.16241397142.31390235913151456.406123111882455.01262211: 287.5808122709.9663871080384206.7659560591291719.23289899: 124.5554791800.83292950408371004.371006296252741.43872564: 269.9564961985.6908017795547292.9933122900563422.14410918: 133.4004049956.1692010964455961.097011189325933.60374116: 195.00506151567.6319211218467659.694864704945935.06906536: 195.69288881645.8186484160335656.329654415640815.16699505: 83.73197257583.88454204506181204.100915918012858.32715453: 323.87572152886.823114117620729.19230769230762247.63230528: 267.64339892316.167649842449304.310190903919956.67244179: 309.93180732798.530998344356797.4132167403772754.21550427: 268.02234552667.433795836312302.456186405022937.88449883: 234.51899221796.0444681533463466.372083858424555.70308293: 266.00086232746.807975640788312.34633742844549.867087613: 90.95203071301.092230260424861168.776621635654715.77905743: 147.0118655616.5429492118313894.50263898974587.48750089: 51.61285024174.12230900973791361.244433012545922.89316819: 130.1152146996.1374704721544977.16987763234326.37725093: 158.12907891182.0410591138893840.111426269558319.92419675: 117.5887433837.71917586460241038.455915726562450.580563: 239.75864812473.480702046455440.736951754832524.62143297: 149.49272521088.3541736626898882.364977770377520.45139803: 109.9549641865.84956717570631075.80434927906234.84828457: 203.82677281634.0382332396957616.534447005959412.75083237: 77.1041927454.96300188465711236.52747565981249.12083394: 288.85465542395.592519926105200.533648761902449.100285402: 62.76275359260.17721953771511306.693284034611259.27878151: 301.88528772937.6136.7809920012430247.47346231: 268.02123652307.692110939322302.46161185580915.92936248: 86.11950973624.56292190198081192.4198374963895.303716442: 63.4913744657.600000000000011303.128490315356949.85037857: 287.8403112434.5195413763445205.4963514157932443.87715391: 207.90020982115.800388762434596.605066393221145.09539424: 245.7622232180.8032219706884411.364328583250247.41986907: 257.8092882304.832484159196352.423796158886039.072455845: 90.80610858258.692290819426031169.490548871782524.71561507: 126.42671651093.379539593982995.215935877983211.37279827: 63.54665302381.433897251168331302.858038691730852.47068842: 285.76011492574.3339608956035215.6737566376889439.28139698: 221.04015021870.5801197639576532.317616907228423.19939137: 146.22607161012.4769180039862898.34715285996838.495709266: 80.31633039227.91826308677351220.812020891985922.10402769: 124.4380824954.03053405362721004.945372338626322.88508271: 144.451947995.7060456311913907.027096608061845.12833981: 247.86566762182.5611305703674401.0731789614146740.06566092: 206.44371471912.4268534399584603.731000636911553.79670084: 316.04876512645.08729413116967.4858652291218330.97182088: 185.99936861427.1979929939357703.755416490058895% CI BandData PointsRegression Line (y = 4.73x + 30.6)