How to display a linear regression line along with the scatter plot (2024)

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HAT on 15 Feb 2023

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Edited: HAT on 15 Feb 2023

Accepted Answer: Dyuman Joshi

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I want to diplay a linear regression line (y=b+mx) along with the scattered plot, but I got stuck.

I need only to display the scatter points and the regression line (y=b+mx) without confidence bounds.

Moreover, the scatter points (dots) should remain the same, instead of x.

I wonder if I could get help.

Thank you in advance.

%Two data x and y

x=[0.06309839457075230.05693107619633370.09028491744333860.05865575645363810.03341446972672780.07126318804719290.07599369681925050.1237383391828880.02970326371090490.07958922825838190.02822615962940580.05371832499871050.1505722574324090.09779636990330210.08852692083261750.08663963852494920.06692985048651100.03443124139251160.03486679608968030.04936561884930810.02706505382994660.03809683374340810.08884456429543780.08959452988850490.05413598539551820.07443219954648170.09135045866358170.06565543006217120.1172591952930690.07132773533507690.03373605523729400.08067380458939770.01879387692142770.1122039289348480.03448906166116090.1120701004623410.03543402102419640.04857534580997900.1007188832239410.08754117406964600.03561330318588130.05828179863562050.04827906342289890.1589973229920620.07299922900970770.04712447430219200.04460848096593500.05113160238979590.05064962538439760.1493807528938380.06890454808273980.05185330619200890.02097885521116530.08533212175630260.09334527596721510.06323211220103040.09890456245366580.1186469453957640.07463944914258170.05633649734147520.06994053863106580.1763135304489040.08050859674232520.07119517069343920.09858737778335320.03611237537507500.1076203229906260.1559530113092760.07038443592225950.06983003496596530.01336932916192660.05632515380760250.04205943357734830.02099298072493190.08364806117569990.09187601533425810.1090710239880300.1120111484569820.09329468418169260.05272733055724430.06039650226899180.09978319703701410.08655659485505630.04518649190173010.05349285558988820.07536118495801760.09750807053429450.06413639677131380.04925283781379290.01158959638316130.03956753546458570.05435628125057300.03029120283161280.09529966575865370.07355693582133970.02044152913469910.02914874079428660.06779173912117210.04392156589517290.03338165490416100.04564035519523540.04865533993022020.04705384329017010.08119578579448990.05150618534835910.09146183193038290.05455849546641580.04965850333063480.03946723483194680.05512729490073500.05206441965539220.08344327647565730.07349598361265990.04644065495715000.05977282805885480.04102139626488290.06904048539612150.04968099765649870.03616692050940100.07303227046671410.1077305973500340.02484053550058770.05119423825764930.01895039377524950.04086802887939210.1207850372900730.05945152312210300.03282459665423760.05484581486869530.05299490388655310.07910291044705280.05542372528664840.09949083401251330.05423992437662950.05344481075533410.06735932733335770.09397911929003670.08233009457623240.04425666002408200.06106171212748780.08988843386442920.05897966828047150.09762099489736520.08684448015714540.06520493594170580.05370214996787300.03016545195017580.05125361543515390.03459874376280450.07792823362551900.08251540443375720.08597997771422920.04422071683689850.009872432635278130.09500217131842070.06134378225517520.1424415268164360.1008024302739120.04142167014987310.1090196756087900.02969130823362430.03477428601591560.1358097634201030.09845060292349620.06740374185930820.02294076662960690.01899968522704670.07435713351149890.08471928141228800.1150836906128880.1236961789667090.09564178039590830.05197928740587790.03930760755779760.06773653774106940.05914268578390450.08479462646718230.06067566482028720.07240677342994400.04308352013769810.08081108532927790.04984233753227010.01947088969741060.07511311867277410.03487263898201650.07406122886704350.05943355162868840.05943481980176550.06824063084136350.05318946558616870.07437903025942930.02195581761074920.02721236622580390.1085384395378590.07470598962823690.09468588223297400.05692767023968260.01493835251691370.05980501840408440.05454003093042100.03430027318035690.08116874052151880.03882679548564320.1086318001002040.06745774263631810.05685443418378040.01081676197308900.04230296423731160.03777518600772560.03935527223300520.07101539871478490.1218323511381840.07693823765095650.09435856405268140.02571388418044950.1061639048143680.03643312446913570.06044182707484090.07387447949806140.1376934410016740.0459022534232133];

y=[0.02508616939125110.005851904584649800.06803699267915280.01235495625868420.02825784262639280.02898788785128770.01832478152402380.02640467693512470.02983945888291290.01843754015416120.01282343939982600.02520503879260580.02955893747770740.01725815127142850.01983229143171100.05106121221090210.02653921019727720.01665424591143240.06140140187435780.01215600045568790.007187486930920650.01530447362533970.02931769695732870.01758691749216160.01146932375750250.05065900015594090.006104658398886970.01264871097736350.07025559228962820.04185702990609150.002360605321422300.04555168314593130.01108001401434060.02200562988598200.01056527068648780.03731364966526350.03879075833997520.04054890561585150.05483303838306990.03131050462741290.01633614920311270.02242567993869370.03409183578322050.08565875920792770.02372819659425350.01850484026394380.01605536279792750.02487645045941000.006710411559444680.008602252703395450.02842315208788380.01488588214414660.02408405346431920.03248764875004750.01863923520079120.02903601080408250.03920293193574570.005147746807632230.04037344913441470.005535149481286240.01594729127363010.03910147474113820.01245042942226370.03562269453655350.01816283488626090.01786817791879540.008527726534151020.01381551792770360.01287975068836400.01757290802578200.03326496229591980.01321449196161320.01421899605877210.008674722208722890.01385856439633980.08080963008683750.02758595595651180.04306588379252900.04375295799421200.04321430877500070.02166440208035450.02824670313100190.02641980427494840.01865362058347010.02049446175892890.01614207505089470.04196421047287290.02662221277170960.02382606841623980.01673233865258800.02111662374901620.01384894514545490.01717114934857410.01981547348052070.02882560164927440.02177443980623730.02246401374338890.01192035431071330.01852851059329220.02768439043805910.02604602934448230.01167391734912310.03644104845792420.02617992587653700.01432957287985270.06180402521198640.03607622978139380.02992191014572120.01221044192388760.02245331538654880.04663429690068530.04392416479062210.02391866597289510.04295360630565060.02332041803987100.02579523408652140.04422638103152390.004652671023481240.008314119756583380.02368930524997210.03573911327841130.02144722261569450.03281760166651200.01679887662486930.02028378330330650.01191480521059290.02288268674627090.03437817081711760.05408342362560890.02532217118081910.04683238963675220.07426868195099080.01499216032334250.02993636828616560.01404228004761850.009234665744434230.03393424656031260.02915567745814040.02648005663618670.02540964164753420.02723399019516170.03061757840231540.02658228133770050.02856888114346440.02642818442911060.008469949739236200.01263903471571100.01178236936381920.01064385688237980.02620670375311810.03218054387415190.01487501703890710.01520491010840410.01079179178222010.03602501098042130.02270160643298700.02135934751347250.02442212649283070.04116646680124720.04019678224628380.003102150603142170.03327187839083830.05295847107765880.01354454957371360.008609164878156000.02793237398141910.01605243043049930.01714574035157510.01357765479894870.01063349070076620.02834725147901110.007763856519468300.02398863302822520.01992173257932690.01683000204905450.01981692178225890.01016890244086790.04217152018978650.01508275027525570.02264026156704000.03840037540975820.02439507995838780.02217027907751740.005676357881481870.01849356805114840.009574579838463420.03944340471769480.01378015384557110.006691083665336190.01756979856105050.02571160119947900.02060429770543870.01354852374646910.007413083865351770.01790471405585570.02589819160376720.01570924821449790.03013732000504650.01378315390614410.01714130501252070.01505720446094570.009383182067219050.02889209568875290.05689369870924980.05632160234276680.02386738374113450.007973603096864640.02043170222339950.03269979164399860.07261277693026760.01514350396627880.01232171648086810.01832598746924330.02148468765592650.01402008538721360.03766925500800250.01576112723160320.02585600420594970.01469396499930020.02531022027067200.0188959246844452];

disp('Scatter graph ');

Scatter graph

scatter(x',y','.')

xlabel('x')

ylabel('y')

mdl = fitlm(x,y)

mdl =

Linear regression model: y ~ 1 + x1Estimated Coefficients: Estimate SE tStat pValue ________ _________ ______ __________ (Intercept) 0.017481 0.0022911 7.6298 7.1359e-13 x1 0.1106 0.031273 3.5367 0.00049439Number of observations: 221, Error degrees of freedom: 219Root Mean Squared Error: 0.0144R-squared: 0.054, Adjusted R-Squared: 0.0497F-statistic vs. constant model: 12.5, p-value = 0.000494

plot(mdl)

%Here I need to display only the scatter points and the regression line (y=1+x) without confidence bounds.

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Accepted Answer

Dyuman Joshi on 15 Feb 2023

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%Two data x and y

x = [0.06309839457075230.05693107619633370.09028491744333860.05865575645363810.03341446972672780.07126318804719290.07599369681925050.1237383391828880.02970326371090490.07958922825838190.02822615962940580.05371832499871050.1505722574324090.09779636990330210.08852692083261750.08663963852494920.06692985048651100.03443124139251160.03486679608968030.04936561884930810.02706505382994660.03809683374340810.08884456429543780.08959452988850490.05413598539551820.07443219954648170.09135045866358170.06565543006217120.1172591952930690.07132773533507690.03373605523729400.08067380458939770.01879387692142770.1122039289348480.03448906166116090.1120701004623410.03543402102419640.04857534580997900.1007188832239410.08754117406964600.03561330318588130.05828179863562050.04827906342289890.1589973229920620.07299922900970770.04712447430219200.04460848096593500.05113160238979590.05064962538439760.1493807528938380.06890454808273980.05185330619200890.02097885521116530.08533212175630260.09334527596721510.06323211220103040.09890456245366580.1186469453957640.07463944914258170.05633649734147520.06994053863106580.1763135304489040.08050859674232520.07119517069343920.09858737778335320.03611237537507500.1076203229906260.1559530113092760.07038443592225950.06983003496596530.01336932916192660.05632515380760250.04205943357734830.02099298072493190.08364806117569990.09187601533425810.1090710239880300.1120111484569820.09329468418169260.05272733055724430.06039650226899180.09978319703701410.08655659485505630.04518649190173010.05349285558988820.07536118495801760.09750807053429450.06413639677131380.04925283781379290.01158959638316130.03956753546458570.05435628125057300.03029120283161280.09529966575865370.07355693582133970.02044152913469910.02914874079428660.06779173912117210.04392156589517290.03338165490416100.04564035519523540.04865533993022020.04705384329017010.08119578579448990.05150618534835910.09146183193038290.05455849546641580.04965850333063480.03946723483194680.05512729490073500.05206441965539220.08344327647565730.07349598361265990.04644065495715000.05977282805885480.04102139626488290.06904048539612150.04968099765649870.03616692050940100.07303227046671410.1077305973500340.02484053550058770.05119423825764930.01895039377524950.04086802887939210.1207850372900730.05945152312210300.03282459665423760.05484581486869530.05299490388655310.07910291044705280.05542372528664840.09949083401251330.05423992437662950.05344481075533410.06735932733335770.09397911929003670.08233009457623240.04425666002408200.06106171212748780.08988843386442920.05897966828047150.09762099489736520.08684448015714540.06520493594170580.05370214996787300.03016545195017580.05125361543515390.03459874376280450.07792823362551900.08251540443375720.08597997771422920.04422071683689850.009872432635278130.09500217131842070.06134378225517520.1424415268164360.1008024302739120.04142167014987310.1090196756087900.02969130823362430.03477428601591560.1358097634201030.09845060292349620.06740374185930820.02294076662960690.01899968522704670.07435713351149890.08471928141228800.1150836906128880.1236961789667090.09564178039590830.05197928740587790.03930760755779760.06773653774106940.05914268578390450.08479462646718230.06067566482028720.07240677342994400.04308352013769810.08081108532927790.04984233753227010.01947088969741060.07511311867277410.03487263898201650.07406122886704350.05943355162868840.05943481980176550.06824063084136350.05318946558616870.07437903025942930.02195581761074920.02721236622580390.1085384395378590.07470598962823690.09468588223297400.05692767023968260.01493835251691370.05980501840408440.05454003093042100.03430027318035690.08116874052151880.03882679548564320.1086318001002040.06745774263631810.05685443418378040.01081676197308900.04230296423731160.03777518600772560.03935527223300520.07101539871478490.1218323511381840.07693823765095650.09435856405268140.02571388418044950.1061639048143680.03643312446913570.06044182707484090.07387447949806140.1376934410016740.0459022534232133];

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HAT on 15 Feb 2023

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https://jmaab.mathworks.com/matlabcentral/answers/1913105-how-to-display-a-linear-regression-line-along-with-the-scatter-plot#comment_2617470

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Edited: HAT on 15 Feb 2023

@Dyuman Joshi Thanks, but how can I include the expression y=1+x instead of "Fit", and with the same scatter points (dots instead of x)?

I wanted the scatter points (dots) remain the same, instead of x.

Dyuman Joshi on 15 Feb 2023

Direct link to this comment

https://jmaab.mathworks.com/matlabcentral/answers/1913105-how-to-display-a-linear-regression-line-along-with-the-scatter-plot#comment_2617525

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Open in MATLAB Online

Ran in:

%Two data x and y

x = [0.06309839457075230.05693107619633370.09028491744333860.05865575645363810.03341446972672780.07126318804719290.07599369681925050.1237383391828880.02970326371090490.07958922825838190.02822615962940580.05371832499871050.1505722574324090.09779636990330210.08852692083261750.08663963852494920.06692985048651100.03443124139251160.03486679608968030.04936561884930810.02706505382994660.03809683374340810.08884456429543780.08959452988850490.05413598539551820.07443219954648170.09135045866358170.06565543006217120.1172591952930690.07132773533507690.03373605523729400.08067380458939770.01879387692142770.1122039289348480.03448906166116090.1120701004623410.03543402102419640.04857534580997900.1007188832239410.08754117406964600.03561330318588130.05828179863562050.04827906342289890.1589973229920620.07299922900970770.04712447430219200.04460848096593500.05113160238979590.05064962538439760.1493807528938380.06890454808273980.05185330619200890.02097885521116530.08533212175630260.09334527596721510.06323211220103040.09890456245366580.1186469453957640.07463944914258170.05633649734147520.06994053863106580.1763135304489040.08050859674232520.07119517069343920.09858737778335320.03611237537507500.1076203229906260.1559530113092760.07038443592225950.06983003496596530.01336932916192660.05632515380760250.04205943357734830.02099298072493190.08364806117569990.09187601533425810.1090710239880300.1120111484569820.09329468418169260.05272733055724430.06039650226899180.09978319703701410.08655659485505630.04518649190173010.05349285558988820.07536118495801760.09750807053429450.06413639677131380.04925283781379290.01158959638316130.03956753546458570.05435628125057300.03029120283161280.09529966575865370.07355693582133970.02044152913469910.02914874079428660.06779173912117210.04392156589517290.03338165490416100.04564035519523540.04865533993022020.04705384329017010.08119578579448990.05150618534835910.09146183193038290.05455849546641580.04965850333063480.03946723483194680.05512729490073500.05206441965539220.08344327647565730.07349598361265990.04644065495715000.05977282805885480.04102139626488290.06904048539612150.04968099765649870.03616692050940100.07303227046671410.1077305973500340.02484053550058770.05119423825764930.01895039377524950.04086802887939210.1207850372900730.05945152312210300.03282459665423760.05484581486869530.05299490388655310.07910291044705280.05542372528664840.09949083401251330.05423992437662950.05344481075533410.06735932733335770.09397911929003670.08233009457623240.04425666002408200.06106171212748780.08988843386442920.05897966828047150.09762099489736520.08684448015714540.06520493594170580.05370214996787300.03016545195017580.05125361543515390.03459874376280450.07792823362551900.08251540443375720.08597997771422920.04422071683689850.009872432635278130.09500217131842070.06134378225517520.1424415268164360.1008024302739120.04142167014987310.1090196756087900.02969130823362430.03477428601591560.1358097634201030.09845060292349620.06740374185930820.02294076662960690.01899968522704670.07435713351149890.08471928141228800.1150836906128880.1236961789667090.09564178039590830.05197928740587790.03930760755779760.06773653774106940.05914268578390450.08479462646718230.06067566482028720.07240677342994400.04308352013769810.08081108532927790.04984233753227010.01947088969741060.07511311867277410.03487263898201650.07406122886704350.05943355162868840.05943481980176550.06824063084136350.05318946558616870.07437903025942930.02195581761074920.02721236622580390.1085384395378590.07470598962823690.09468588223297400.05692767023968260.01493835251691370.05980501840408440.05454003093042100.03430027318035690.08116874052151880.03882679548564320.1086318001002040.06745774263631810.05685443418378040.01081676197308900.04230296423731160.03777518600772560.03935527223300520.07101539871478490.1218323511381840.07693823765095650.09435856405268140.02571388418044950.1061639048143680.03643312446913570.06044182707484090.07387447949806140.1376934410016740.0459022534232133];

y = [0.02508616939125110.005851904584649800.06803699267915280.01235495625868420.02825784262639280.02898788785128770.01832478152402380.02640467693512470.02983945888291290.01843754015416120.01282343939982600.02520503879260580.02955893747770740.01725815127142850.01983229143171100.05106121221090210.02653921019727720.01665424591143240.06140140187435780.01215600045568790.007187486930920650.01530447362533970.02931769695732870.01758691749216160.01146932375750250.05065900015594090.006104658398886970.01264871097736350.07025559228962820.04185702990609150.002360605321422300.04555168314593130.01108001401434060.02200562988598200.01056527068648780.03731364966526350.03879075833997520.04054890561585150.05483303838306990.03131050462741290.01633614920311270.02242567993869370.03409183578322050.08565875920792770.02372819659425350.01850484026394380.01605536279792750.02487645045941000.006710411559444680.008602252703395450.02842315208788380.01488588214414660.02408405346431920.03248764875004750.01863923520079120.02903601080408250.03920293193574570.005147746807632230.04037344913441470.005535149481286240.01594729127363010.03910147474113820.01245042942226370.03562269453655350.01816283488626090.01786817791879540.008527726534151020.01381551792770360.01287975068836400.01757290802578200.03326496229591980.01321449196161320.01421899605877210.008674722208722890.01385856439633980.08080963008683750.02758595595651180.04306588379252900.04375295799421200.04321430877500070.02166440208035450.02824670313100190.02641980427494840.01865362058347010.02049446175892890.01614207505089470.04196421047287290.02662221277170960.02382606841623980.01673233865258800.02111662374901620.01384894514545490.01717114934857410.01981547348052070.02882560164927440.02177443980623730.02246401374338890.01192035431071330.01852851059329220.02768439043805910.02604602934448230.01167391734912310.03644104845792420.02617992587653700.01432957287985270.06180402521198640.03607622978139380.02992191014572120.01221044192388760.02245331538654880.04663429690068530.04392416479062210.02391866597289510.04295360630565060.02332041803987100.02579523408652140.04422638103152390.004652671023481240.008314119756583380.02368930524997210.03573911327841130.02144722261569450.03281760166651200.01679887662486930.02028378330330650.01191480521059290.02288268674627090.03437817081711760.05408342362560890.02532217118081910.04683238963675220.07426868195099080.01499216032334250.02993636828616560.01404228004761850.009234665744434230.03393424656031260.02915567745814040.02648005663618670.02540964164753420.02723399019516170.03061757840231540.02658228133770050.02856888114346440.02642818442911060.008469949739236200.01263903471571100.01178236936381920.01064385688237980.02620670375311810.03218054387415190.01487501703890710.01520491010840410.01079179178222010.03602501098042130.02270160643298700.02135934751347250.02442212649283070.04116646680124720.04019678224628380.003102150603142170.03327187839083830.05295847107765880.01354454957371360.008609164878156000.02793237398141910.01605243043049930.01714574035157510.01357765479894870.01063349070076620.02834725147901110.007763856519468300.02398863302822520.01992173257932690.01683000204905450.01981692178225890.01016890244086790.04217152018978650.01508275027525570.02264026156704000.03840037540975820.02439507995838780.02217027907751740.005676357881481870.01849356805114840.009574579838463420.03944340471769480.01378015384557110.006691083665336190.01756979856105050.02571160119947900.02060429770543870.01354852374646910.007413083865351770.01790471405585570.02589819160376720.01570924821449790.03013732000504650.01378315390614410.01714130501252070.01505720446094570.009383182067219050.02889209568875290.05689369870924980.05632160234276680.02386738374113450.007973603096864640.02043170222339950.03269979164399860.07261277693026760.01514350396627880.01232171648086810.01832598746924330.02148468765592650.01402008538721360.03766925500800250.01576112723160320.02585600420594970.01469396499930020.02531022027067200.0188959246844452];

s = scatter(x,y,'.');

xlabel('x')

ylabel('y')

mdl = fitlm(x,y);

% use handle for plotting

h = plot(mdl)

h =

4×1 Line array: Line (Data) Line (Fit) Line (Confidence bounds) Line

h(1).Marker = '.';

h(1).Color = [0 0.4470 0.7410];

legend('Data','y = 1 + x')

delete(h([3 4])) %delete bounds

Les Beckham on 15 Feb 2023

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Thanks for the comment, Les. I am aware of the "formula" and the actual equation, however, I answered according to the requirements of OP.

Les Beckham on 15 Feb 2023

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Open in MATLAB Online

Ran in:

Note that this can also be done with polyfit (in base Matlab):

%Two data x and y

x = [0.06309839457075230.05693107619633370.09028491744333860.05865575645363810.03341446972672780.07126318804719290.07599369681925050.1237383391828880.02970326371090490.07958922825838190.02822615962940580.05371832499871050.1505722574324090.09779636990330210.08852692083261750.08663963852494920.06692985048651100.03443124139251160.03486679608968030.04936561884930810.02706505382994660.03809683374340810.08884456429543780.08959452988850490.05413598539551820.07443219954648170.09135045866358170.06565543006217120.1172591952930690.07132773533507690.03373605523729400.08067380458939770.01879387692142770.1122039289348480.03448906166116090.1120701004623410.03543402102419640.04857534580997900.1007188832239410.08754117406964600.03561330318588130.05828179863562050.04827906342289890.1589973229920620.07299922900970770.04712447430219200.04460848096593500.05113160238979590.05064962538439760.1493807528938380.06890454808273980.05185330619200890.02097885521116530.08533212175630260.09334527596721510.06323211220103040.09890456245366580.1186469453957640.07463944914258170.05633649734147520.06994053863106580.1763135304489040.08050859674232520.07119517069343920.09858737778335320.03611237537507500.1076203229906260.1559530113092760.07038443592225950.06983003496596530.01336932916192660.05632515380760250.04205943357734830.02099298072493190.08364806117569990.09187601533425810.1090710239880300.1120111484569820.09329468418169260.05272733055724430.06039650226899180.09978319703701410.08655659485505630.04518649190173010.05349285558988820.07536118495801760.09750807053429450.06413639677131380.04925283781379290.01158959638316130.03956753546458570.05435628125057300.03029120283161280.09529966575865370.07355693582133970.02044152913469910.02914874079428660.06779173912117210.04392156589517290.03338165490416100.04564035519523540.04865533993022020.04705384329017010.08119578579448990.05150618534835910.09146183193038290.05455849546641580.04965850333063480.03946723483194680.05512729490073500.05206441965539220.08344327647565730.07349598361265990.04644065495715000.05977282805885480.04102139626488290.06904048539612150.04968099765649870.03616692050940100.07303227046671410.1077305973500340.02484053550058770.05119423825764930.01895039377524950.04086802887939210.1207850372900730.05945152312210300.03282459665423760.05484581486869530.05299490388655310.07910291044705280.05542372528664840.09949083401251330.05423992437662950.05344481075533410.06735932733335770.09397911929003670.08233009457623240.04425666002408200.06106171212748780.08988843386442920.05897966828047150.09762099489736520.08684448015714540.06520493594170580.05370214996787300.03016545195017580.05125361543515390.03459874376280450.07792823362551900.08251540443375720.08597997771422920.04422071683689850.009872432635278130.09500217131842070.06134378225517520.1424415268164360.1008024302739120.04142167014987310.1090196756087900.02969130823362430.03477428601591560.1358097634201030.09845060292349620.06740374185930820.02294076662960690.01899968522704670.07435713351149890.08471928141228800.1150836906128880.1236961789667090.09564178039590830.05197928740587790.03930760755779760.06773653774106940.05914268578390450.08479462646718230.06067566482028720.07240677342994400.04308352013769810.08081108532927790.04984233753227010.01947088969741060.07511311867277410.03487263898201650.07406122886704350.05943355162868840.05943481980176550.06824063084136350.05318946558616870.07437903025942930.02195581761074920.02721236622580390.1085384395378590.07470598962823690.09468588223297400.05692767023968260.01493835251691370.05980501840408440.05454003093042100.03430027318035690.08116874052151880.03882679548564320.1086318001002040.06745774263631810.05685443418378040.01081676197308900.04230296423731160.03777518600772560.03935527223300520.07101539871478490.1218323511381840.07693823765095650.09435856405268140.02571388418044950.1061639048143680.03643312446913570.06044182707484090.07387447949806140.1376934410016740.0459022534232133];

y = [0.02508616939125110.005851904584649800.06803699267915280.01235495625868420.02825784262639280.02898788785128770.01832478152402380.02640467693512470.02983945888291290.01843754015416120.01282343939982600.02520503879260580.02955893747770740.01725815127142850.01983229143171100.05106121221090210.02653921019727720.01665424591143240.06140140187435780.01215600045568790.007187486930920650.01530447362533970.02931769695732870.01758691749216160.01146932375750250.05065900015594090.006104658398886970.01264871097736350.07025559228962820.04185702990609150.002360605321422300.04555168314593130.01108001401434060.02200562988598200.01056527068648780.03731364966526350.03879075833997520.04054890561585150.05483303838306990.03131050462741290.01633614920311270.02242567993869370.03409183578322050.08565875920792770.02372819659425350.01850484026394380.01605536279792750.02487645045941000.006710411559444680.008602252703395450.02842315208788380.01488588214414660.02408405346431920.03248764875004750.01863923520079120.02903601080408250.03920293193574570.005147746807632230.04037344913441470.005535149481286240.01594729127363010.03910147474113820.01245042942226370.03562269453655350.01816283488626090.01786817791879540.008527726534151020.01381551792770360.01287975068836400.01757290802578200.03326496229591980.01321449196161320.01421899605877210.008674722208722890.01385856439633980.08080963008683750.02758595595651180.04306588379252900.04375295799421200.04321430877500070.02166440208035450.02824670313100190.02641980427494840.01865362058347010.02049446175892890.01614207505089470.04196421047287290.02662221277170960.02382606841623980.01673233865258800.02111662374901620.01384894514545490.01717114934857410.01981547348052070.02882560164927440.02177443980623730.02246401374338890.01192035431071330.01852851059329220.02768439043805910.02604602934448230.01167391734912310.03644104845792420.02617992587653700.01432957287985270.06180402521198640.03607622978139380.02992191014572120.01221044192388760.02245331538654880.04663429690068530.04392416479062210.02391866597289510.04295360630565060.02332041803987100.02579523408652140.04422638103152390.004652671023481240.008314119756583380.02368930524997210.03573911327841130.02144722261569450.03281760166651200.01679887662486930.02028378330330650.01191480521059290.02288268674627090.03437817081711760.05408342362560890.02532217118081910.04683238963675220.07426868195099080.01499216032334250.02993636828616560.01404228004761850.009234665744434230.03393424656031260.02915567745814040.02648005663618670.02540964164753420.02723399019516170.03061757840231540.02658228133770050.02856888114346440.02642818442911060.008469949739236200.01263903471571100.01178236936381920.01064385688237980.02620670375311810.03218054387415190.01487501703890710.01520491010840410.01079179178222010.03602501098042130.02270160643298700.02135934751347250.02442212649283070.04116646680124720.04019678224628380.003102150603142170.03327187839083830.05295847107765880.01354454957371360.008609164878156000.02793237398141910.01605243043049930.01714574035157510.01357765479894870.01063349070076620.02834725147901110.007763856519468300.02398863302822520.01992173257932690.01683000204905450.01981692178225890.01016890244086790.04217152018978650.01508275027525570.02264026156704000.03840037540975820.02439507995838780.02217027907751740.005676357881481870.01849356805114840.009574579838463420.03944340471769480.01378015384557110.006691083665336190.01756979856105050.02571160119947900.02060429770543870.01354852374646910.007413083865351770.01790471405585570.02589819160376720.01570924821449790.03013732000504650.01378315390614410.01714130501252070.01505720446094570.009383182067219050.02889209568875290.05689369870924980.05632160234276680.02386738374113450.007973603096864640.02043170222339950.03269979164399860.07261277693026760.01514350396627880.01232171648086810.01832598746924330.02148468765592650.01402008538721360.03766925500800250.01576112723160320.02585600420594970.01469396499930020.02531022027067200.0188959246844452];

coeffs = polyfit(x, y, 1)

coeffs = 1×2

0.1106 0.0175

xfit = [0 max(x)];

yfit = polyval(coeffs, xfit);

plot(x, y, '.', xfit, yfit, 'r-');

xlabel('x')

ylabel('y')

legend('data', sprintf('y = %.6f*x + %.6f', coeffs))

grid on

HAT on 15 Feb 2023

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Thanks. But I have still doubt, whether y=1+x is a fit for the given data or not. I wonder if you confirm me whether y=1+x is a linear regression fit for the given data.

Les Beckham on 15 Feb 2023

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@Dyuman Joshi I realize that you were just doing that because it is what OP asked for.

@HAT y = 1 + x is definitely NOT the equation of the fit line for this data as I said in my previous comment:

"It is actually y = 0.017481 + 0.1106*x".

Dyuman Joshi on 15 Feb 2023

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No, it is not the regression fit.

It is an approximation, giving an idea of the order of the equation (via a particular notation).

The regression fit is as mentioned by Les in the comment above.

HAT on 15 Feb 2023

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Edited: HAT on 15 Feb 2023

Thank you very much. Now I edited my question.

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How to display a linear regression line along with the scatter plot (2024)

FAQs

How to plot a regression line in a scatter plot? ›

A scatter plot can be created using the function plot(x, y). The function lm() will be used to fit linear models between y and x. A regression line will be added on the plot using the function abline(), which takes the output of lm() as an argument. You can also add a smoothing line using the function loess().

How do you interpret a scatter plot with a regression line answer? ›

If the regression line has a positive slope, the data has a positive linear relationship; if the regression line of the data has a negative slope, the data has a negative linear relationship. If the data is clustered tightly around its regression line, we might say it shows a strong linear relationship.

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How to display the equation of the regression line on a scatter chart? ›

Once we have recreated the scatter plot, we find the equation of the regression line by clicking the three dots at the top right of the plot, selecting “Edit chart,” then clicking on “Customize” and “Series.” We add the regression line by checking the box next to “Trendline,” and then we show the equation by selecting ...

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How do I add a linear regression line to a scatter plot in Excel? ›

Create your regression curve by making a scatter plot. Add the regression line by choosing the “Layout” tab in the “Chart Tools” menu. Then select “Trendline” and choose the “Linear Trendline” option, and the line will appear as shown above.

Get More Info Here ›

How do you write a linear regression equation from a scatter plot? ›

The Linear Regression Equation

The equation has the form Y= a + bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept.

Keep Reading ›

How do you show regression results on a graph? ›

15 Ways to Visualize Regression Results!

2.1 Ribbon Plot of Expected Values.
2.2 Scatterplot of Expected Values.
2.3 Line Plot of Expected Values, by Percentile.
2.4 Ribbon Plot by Level of Confidence.
2.5 2-D Density Plane of Expected Values.
2.6 Two Ribbons or More!
2.7 Dot and Whisker Plots.

More items...

How do you plot a regression model? ›

Create Scatter Plot for Simple Linear Regression

pValue of the Weight variable is very small, which means that the variable is statistically significant in the model. Visualize this result by creating a scatter plot of the data, along with a fitted curve and its 95% confidence bounds, using the plot function.

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How do I add a regression line to a scatter plot in Matplotlib? ›

By plotting the line m*x + b , we are adding the regression line to our scatterplot. You can further customize your plot with additional Matplotlib features, such as setting labels for the x and y axes, creating a title, or adjusting the figure size.

Explore More ›