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HAT on 15 Feb 2023
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')
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
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];
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')
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
9 Comments Show 7 older commentsHide 7 older comments
Show 7 older commentsHide 7 older comments
HAT 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_2617470
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
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];
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
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_2617550
⋮
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
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_2617565
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
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_2617600
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
HAT 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_2617610
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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