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

Open in MATLAB Online

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')

How to display a linear regression line along with the scatter plot (2)

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)

How to display a linear regression line along with the scatter plot (3)

%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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    https://jmaab.mathworks.com/matlabcentral/answers/1913105-how-to-display-a-linear-regression-line-along-with-the-scatter-plot#answer_1172240

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    Direct link to this answer

    https://jmaab.mathworks.com/matlabcentral/answers/1913105-how-to-display-a-linear-regression-line-along-with-the-scatter-plot#answer_1172240

Open in MATLAB Online

%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];

See Also
Scatter plots and fitting experimental dataHow do I create a legend that explaines scatter plot by marker sizeNeural Network for Beam Selection - MATLAB & Simulink - MathWorks 日本Overcoming Distance Constraints in K-Medoids Clustering: A Case Study with MATLAB

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];

scatter(x,y,'.')

xlabel('x')

ylabel('y')

How to display a linear regression line along with the scatter plot (5)

mdl = fitlm(x,y);

% use handle for plotting

h = plot(mdl)

h =

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

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

How to display a linear regression line along with the scatter plot (6)

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HAT on 15 Feb 2023

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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

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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];

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')

How to display a linear regression line along with the scatter plot (9)

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

How to display a linear regression line along with the scatter plot (10)

Les Beckham on 15 Feb 2023

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See Also
MATLAB observation space diagnostics — DART 11.5.1 documentation

Contrary to what is implied by the confusing output of fitlm (see below underlined and bold), y = 1 + x is not the equation of the fit line.

mdl =

Linear regression model:

y ~ 1 + x1

Estimated Coefficients:

Estimate SE tStat pValue

________ _________ ______ __________

(Intercept) 0.017481 0.0022911 7.6298 7.1359e-13

x1 0.1106 0.031273 3.5367 0.00049439

It is actually y = 0.017481 + 0.1106*x.

Dyuman Joshi 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

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

How to display a linear regression line along with the scatter plot (14)

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 (19)

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

FAQs

How do you add a linear regression line to 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().

Keep Reading ›
How do you read a regression line on a scatter plot? ›

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.

Read The Full Story ›
How do you show lines on a scatter plot? ›

It is simpler to create a line graph with (XY) Scatter when your independent and dependent variables are in columns. The color and size of the line and markers can be set by double-clicking on the line in the graph. Markers can be turned off by double-clicking the line and choosing None under Markers.

Get More Info ›
How do you graph a regression line? ›

Charting a Regression in Excel

To add a regression line, choose "Add Chart Element" from the "Chart Design" menu. In the dialog box, select "Trendline" and then "Linear Trendline." To add the R2 value, select "More Trendline Options" from the "Trendline" menu.

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

You can also add the equation of the regression line to the chart by clicking More Options. In the box that appears to the right, check the box next to Display Equation on chart.

Read More ›
How do you draw a simple linear regression line? ›

To find a regression line by hand, there are a few steps to follow.
  1. Draw a line through the set of points as close to as many points as possible using a ruler.
  2. Choose two points that the line goes through and find the slope between those two points.
  3. Using the slope and one of those points, solve for the y-intercept.

See Details ›
How do you fit a line to a scatter plot? ›

A line of best fit is a straight line that depicts the trend of the given scattered data plots on a graph. It is also known as a trend line or line of regression. It is a line that best displays the trend of a group of points on a scatter plot. It is used to predict the behavior of data using the slope of its line.

See More ›
What does a linear line look like on a scatter plot? ›

This example illustrates a linear relationship. This means that the points on the scatterplot closely resemble a straight line. A relationship is linear if one variable increases by approximately the same rate as the other variables changes by one unit.

Keep Reading ›
Do you draw a line on a scatter graph? ›

Scatter graphs are a visual way of showing if there is a connection between groups of data. If there is a strong connection or correlation, a 'line of best fit' can be drawn.

Keep Reading ›
How to add linear regression equation on scatter plot in Excel? ›

To add a regression line, choose "Add Chart Element" from the "Chart Design" menu. In the dialog box, select "Trendline" and then "Linear Trendline." To add the R2 value, select "More Trendline Options" from the "Trendline" menu.

Find Out More ›

How do you add a line to a scatter plot sheet? ›

Click on the scatter plot and navigate to “Customize” > “Series” > “Trendline” > “Linear” to add the line of best fit.

Discover More Details ›
How do you add a line in a scatter plot in Excel? ›

Click the Insert tab, and then click Insert Line or Area Chart. Click Line with Markers. Click the chart area of the chart to display the Design and Format tabs. Click the Design tab, and then click the chart style you want to use.

Discover More Details ›
How do you add a linear regression line to a scatter plot in Stata? ›

The command for this is twoway lfit [dependent variable] [independent variable] || scatter [dependent variable] [independent variable] The first part of the command draws a regression line, regressing the specified [dept variable] over the specified [indep variable].

Keep Reading ›
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