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

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=	[0.0630983945707523	0.0569310761963337	0.0902849174433386	0.0586557564536381	0.0334144697267278	0.0712631880471929	0.0759936968192505	0.123738339182888	0.0297032637109049	0.0795892282583819	0.0282261596294058	0.0537183249987105	0.150572257432409	0.0977963699033021	0.0885269208326175	0.0866396385249492	0.0669298504865110	0.0344312413925116	0.0348667960896803	0.0493656188493081	0.0270650538299466	0.0380968337434081	0.0888445642954378	0.0895945298885049	0.0541359853955182	0.0744321995464817	0.0913504586635817	0.0656554300621712	0.117259195293069	0.0713277353350769	0.0337360552372940	0.0806738045893977	0.0187938769214277	0.112203928934848	0.0344890616611609	0.112070100462341	0.0354340210241964	0.0485753458099790	0.100718883223941	0.0875411740696460	0.0356133031858813	0.0582817986356205	0.0482790634228989	0.158997322992062	0.0729992290097077	0.0471244743021920	0.0446084809659350	0.0511316023897959	0.0506496253843976	0.149380752893838	0.0689045480827398	0.0518533061920089	0.0209788552111653	0.0853321217563026	0.0933452759672151	0.0632321122010304	0.0989045624536658	0.118646945395764	0.0746394491425817	0.0563364973414752	0.0699405386310658	0.176313530448904	0.0805085967423252	0.0711951706934392	0.0985873777833532	0.0361123753750750	0.107620322990626	0.155953011309276	0.0703844359222595	0.0698300349659653	0.0133693291619266	0.0563251538076025	0.0420594335773483	0.0209929807249319	0.0836480611756999	0.0918760153342581	0.109071023988030	0.112011148456982	0.0932946841816926	0.0527273305572443	0.0603965022689918	0.0997831970370141	0.0865565948550563	0.0451864919017301	0.0534928555898882	0.0753611849580176	0.0975080705342945	0.0641363967713138	0.0492528378137929	0.0115895963831613	0.0395675354645857	0.0543562812505730	0.0302912028316128	0.0952996657586537	0.0735569358213397	0.0204415291346991	0.0291487407942866	0.0677917391211721	0.0439215658951729	0.0333816549041610	0.0456403551952354	0.0486553399302202	0.0470538432901701	0.0811957857944899	0.0515061853483591	0.0914618319303829	0.0545584954664158	0.0496585033306348	0.0394672348319468	0.0551272949007350	0.0520644196553922	0.0834432764756573	0.0734959836126599	0.0464406549571500	0.0597728280588548	0.0410213962648829	0.0690404853961215	0.0496809976564987	0.0361669205094010	0.0730322704667141	0.107730597350034	0.0248405355005877	0.0511942382576493	0.0189503937752495	0.0408680288793921	0.120785037290073	0.0594515231221030	0.0328245966542376	0.0548458148686953	0.0529949038865531	0.0791029104470528	0.0554237252866484	0.0994908340125133	0.0542399243766295	0.0534448107553341	0.0673593273333577	0.0939791192900367	0.0823300945762324	0.0442566600240820	0.0610617121274878	0.0898884338644292	0.0589796682804715	0.0976209948973652	0.0868444801571454	0.0652049359417058	0.0537021499678730	0.0301654519501758	0.0512536154351539	0.0345987437628045	0.0779282336255190	0.0825154044337572	0.0859799777142292	0.0442207168368985	0.00987243263527813	0.0950021713184207	0.0613437822551752	0.142441526816436	0.100802430273912	0.0414216701498731	0.109019675608790	0.0296913082336243	0.0347742860159156	0.135809763420103	0.0984506029234962	0.0674037418593082	0.0229407666296069	0.0189996852270467	0.0743571335114989	0.0847192814122880	0.115083690612888	0.123696178966709	0.0956417803959083	0.0519792874058779	0.0393076075577976	0.0677365377410694	0.0591426857839045	0.0847946264671823	0.0606756648202872	0.0724067734299440	0.0430835201376981	0.0808110853292779	0.0498423375322701	0.0194708896974106	0.0751131186727741	0.0348726389820165	0.0740612288670435	0.0594335516286884	0.0594348198017655	0.0682406308413635	0.0531894655861687	0.0743790302594293	0.0219558176107492	0.0272123662258039	0.108538439537859	0.0747059896282369	0.0946858822329740	0.0569276702396826	0.0149383525169137	0.0598050184040844	0.0545400309304210	0.0343002731803569	0.0811687405215188	0.0388267954856432	0.108631800100204	0.0674577426363181	0.0568544341837804	0.0108167619730890	0.0423029642373116	0.0377751860077256	0.0393552722330052	0.0710153987147849	0.121832351138184	0.0769382376509565	0.0943585640526814	0.0257138841804495	0.106163904814368	0.0364331244691357	0.0604418270748409	0.0738744794980614	0.137693441001674	0.0459022534232133];
y=	[0.0250861693912511	0.00585190458464980	0.0680369926791528	0.0123549562586842	0.0282578426263928	0.0289878878512877	0.0183247815240238	0.0264046769351247	0.0298394588829129	0.0184375401541612	0.0128234393998260	0.0252050387926058	0.0295589374777074	0.0172581512714285	0.0198322914317110	0.0510612122109021	0.0265392101972772	0.0166542459114324	0.0614014018743578	0.0121560004556879	0.00718748693092065	0.0153044736253397	0.0293176969573287	0.0175869174921616	0.0114693237575025	0.0506590001559409	0.00610465839888697	0.0126487109773635	0.0702555922896282	0.0418570299060915	0.00236060532142230	0.0455516831459313	0.0110800140143406	0.0220056298859820	0.0105652706864878	0.0373136496652635	0.0387907583399752	0.0405489056158515	0.0548330383830699	0.0313105046274129	0.0163361492031127	0.0224256799386937	0.0340918357832205	0.0856587592079277	0.0237281965942535	0.0185048402639438	0.0160553627979275	0.0248764504594100	0.00671041155944468	0.00860225270339545	0.0284231520878838	0.0148858821441466	0.0240840534643192	0.0324876487500475	0.0186392352007912	0.0290360108040825	0.0392029319357457	0.00514774680763223	0.0403734491344147	0.00553514948128624	0.0159472912736301	0.0391014747411382	0.0124504294222637	0.0356226945365535	0.0181628348862609	0.0178681779187954	0.00852772653415102	0.0138155179277036	0.0128797506883640	0.0175729080257820	0.0332649622959198	0.0132144919616132	0.0142189960587721	0.00867472220872289	0.0138585643963398	0.0808096300868375	0.0275859559565118	0.0430658837925290	0.0437529579942120	0.0432143087750007	0.0216644020803545	0.0282467031310019	0.0264198042749484	0.0186536205834701	0.0204944617589289	0.0161420750508947	0.0419642104728729	0.0266222127717096	0.0238260684162398	0.0167323386525880	0.0211166237490162	0.0138489451454549	0.0171711493485741	0.0198154734805207	0.0288256016492744	0.0217744398062373	0.0224640137433889	0.0119203543107133	0.0185285105932922	0.0276843904380591	0.0260460293444823	0.0116739173491231	0.0364410484579242	0.0261799258765370	0.0143295728798527	0.0618040252119864	0.0360762297813938	0.0299219101457212	0.0122104419238876	0.0224533153865488	0.0466342969006853	0.0439241647906221	0.0239186659728951	0.0429536063056506	0.0233204180398710	0.0257952340865214	0.0442263810315239	0.00465267102348124	0.00831411975658338	0.0236893052499721	0.0357391132784113	0.0214472226156945	0.0328176016665120	0.0167988766248693	0.0202837833033065	0.0119148052105929	0.0228826867462709	0.0343781708171176	0.0540834236256089	0.0253221711808191	0.0468323896367522	0.0742686819509908	0.0149921603233425	0.0299363682861656	0.0140422800476185	0.00923466574443423	0.0339342465603126	0.0291556774581404	0.0264800566361867	0.0254096416475342	0.0272339901951617	0.0306175784023154	0.0265822813377005	0.0285688811434644	0.0264281844291106	0.00846994973923620	0.0126390347157110	0.0117823693638192	0.0106438568823798	0.0262067037531181	0.0321805438741519	0.0148750170389071	0.0152049101084041	0.0107917917822201	0.0360250109804213	0.0227016064329870	0.0213593475134725	0.0244221264928307	0.0411664668012472	0.0401967822462838	0.00310215060314217	0.0332718783908383	0.0529584710776588	0.0135445495737136	0.00860916487815600	0.0279323739814191	0.0160524304304993	0.0171457403515751	0.0135776547989487	0.0106334907007662	0.0283472514790111	0.00776385651946830	0.0239886330282252	0.0199217325793269	0.0168300020490545	0.0198169217822589	0.0101689024408679	0.0421715201897865	0.0150827502752557	0.0226402615670400	0.0384003754097582	0.0243950799583878	0.0221702790775174	0.00567635788148187	0.0184935680511484	0.00957457983846342	0.0394434047176948	0.0137801538455711	0.00669108366533619	0.0175697985610505	0.0257116011994790	0.0206042977054387	0.0135485237464691	0.00741308386535177	0.0179047140558557	0.0258981916037672	0.0157092482144979	0.0301373200050465	0.0137831539061441	0.0171413050125207	0.0150572044609457	0.00938318206721905	0.0288920956887529	0.0568936987092498	0.0563216023427668	0.0238673837411345	0.00797360309686464	0.0204317022233995	0.0326997916439986	0.0726127769302676	0.0151435039662788	0.0123217164808681	0.0183259874692433	0.0214846876559265	0.0140200853872136	0.0376692550080025	0.0157611272316032	0.0258560042059497	0.0146939649993002	0.0253102202706720	0.0188959246844452];
disp('Scatter graph ');

Scatter graph 

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

mdl = fitlm(x,y)

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


Number of observations: 221, Error degrees of freedom: 219
Root Mean Squared Error: 0.0144
R-squared: 0.054,  Adjusted R-Squared: 0.0497
F-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.

Answer 1

[Dyuman Joshi]

%Two data x and y
x = [0.0630983945707523	0.0569310761963337	0.0902849174433386	0.0586557564536381	0.0334144697267278	0.0712631880471929	0.0759936968192505	0.123738339182888	0.0297032637109049	0.0795892282583819	0.0282261596294058	0.0537183249987105	0.150572257432409	0.0977963699033021	0.0885269208326175	0.0866396385249492	0.0669298504865110	0.0344312413925116	0.0348667960896803	0.0493656188493081	0.0270650538299466	0.0380968337434081	0.0888445642954378	0.0895945298885049	0.0541359853955182	0.0744321995464817	0.0913504586635817	0.0656554300621712	0.117259195293069	0.0713277353350769	0.0337360552372940	0.0806738045893977	0.0187938769214277	0.112203928934848	0.0344890616611609	0.112070100462341	0.0354340210241964	0.0485753458099790	0.100718883223941	0.0875411740696460	0.0356133031858813	0.0582817986356205	0.0482790634228989	0.158997322992062	0.0729992290097077	0.0471244743021920	0.0446084809659350	0.0511316023897959	0.0506496253843976	0.149380752893838	0.0689045480827398	0.0518533061920089	0.0209788552111653	0.0853321217563026	0.0933452759672151	0.0632321122010304	0.0989045624536658	0.118646945395764	0.0746394491425817	0.0563364973414752	0.0699405386310658	0.176313530448904	0.0805085967423252	0.0711951706934392	0.0985873777833532	0.0361123753750750	0.107620322990626	0.155953011309276	0.0703844359222595	0.0698300349659653	0.0133693291619266	0.0563251538076025	0.0420594335773483	0.0209929807249319	0.0836480611756999	0.0918760153342581	0.109071023988030	0.112011148456982	0.0932946841816926	0.0527273305572443	0.0603965022689918	0.0997831970370141	0.0865565948550563	0.0451864919017301	0.0534928555898882	0.0753611849580176	0.0975080705342945	0.0641363967713138	0.0492528378137929	0.0115895963831613	0.0395675354645857	0.0543562812505730	0.0302912028316128	0.0952996657586537	0.0735569358213397	0.0204415291346991	0.0291487407942866	0.0677917391211721	0.0439215658951729	0.0333816549041610	0.0456403551952354	0.0486553399302202	0.0470538432901701	0.0811957857944899	0.0515061853483591	0.0914618319303829	0.0545584954664158	0.0496585033306348	0.0394672348319468	0.0551272949007350	0.0520644196553922	0.0834432764756573	0.0734959836126599	0.0464406549571500	0.0597728280588548	0.0410213962648829	0.0690404853961215	0.0496809976564987	0.0361669205094010	0.0730322704667141	0.107730597350034	0.0248405355005877	0.0511942382576493	0.0189503937752495	0.0408680288793921	0.120785037290073	0.0594515231221030	0.0328245966542376	0.0548458148686953	0.0529949038865531	0.0791029104470528	0.0554237252866484	0.0994908340125133	0.0542399243766295	0.0534448107553341	0.0673593273333577	0.0939791192900367	0.0823300945762324	0.0442566600240820	0.0610617121274878	0.0898884338644292	0.0589796682804715	0.0976209948973652	0.0868444801571454	0.0652049359417058	0.0537021499678730	0.0301654519501758	0.0512536154351539	0.0345987437628045	0.0779282336255190	0.0825154044337572	0.0859799777142292	0.0442207168368985	0.00987243263527813	0.0950021713184207	0.0613437822551752	0.142441526816436	0.100802430273912	0.0414216701498731	0.109019675608790	0.0296913082336243	0.0347742860159156	0.135809763420103	0.0984506029234962	0.0674037418593082	0.0229407666296069	0.0189996852270467	0.0743571335114989	0.0847192814122880	0.115083690612888	0.123696178966709	0.0956417803959083	0.0519792874058779	0.0393076075577976	0.0677365377410694	0.0591426857839045	0.0847946264671823	0.0606756648202872	0.0724067734299440	0.0430835201376981	0.0808110853292779	0.0498423375322701	0.0194708896974106	0.0751131186727741	0.0348726389820165	0.0740612288670435	0.0594335516286884	0.0594348198017655	0.0682406308413635	0.0531894655861687	0.0743790302594293	0.0219558176107492	0.0272123662258039	0.108538439537859	0.0747059896282369	0.0946858822329740	0.0569276702396826	0.0149383525169137	0.0598050184040844	0.0545400309304210	0.0343002731803569	0.0811687405215188	0.0388267954856432	0.108631800100204	0.0674577426363181	0.0568544341837804	0.0108167619730890	0.0423029642373116	0.0377751860077256	0.0393552722330052	0.0710153987147849	0.121832351138184	0.0769382376509565	0.0943585640526814	0.0257138841804495	0.106163904814368	0.0364331244691357	0.0604418270748409	0.0738744794980614	0.137693441001674	0.0459022534232133];
y = [0.0250861693912511	0.00585190458464980	0.0680369926791528	0.0123549562586842	0.0282578426263928	0.0289878878512877	0.0183247815240238	0.0264046769351247	0.0298394588829129	0.0184375401541612	0.0128234393998260	0.0252050387926058	0.0295589374777074	0.0172581512714285	0.0198322914317110	0.0510612122109021	0.0265392101972772	0.0166542459114324	0.0614014018743578	0.0121560004556879	0.00718748693092065	0.0153044736253397	0.0293176969573287	0.0175869174921616	0.0114693237575025	0.0506590001559409	0.00610465839888697	0.0126487109773635	0.0702555922896282	0.0418570299060915	0.00236060532142230	0.0455516831459313	0.0110800140143406	0.0220056298859820	0.0105652706864878	0.0373136496652635	0.0387907583399752	0.0405489056158515	0.0548330383830699	0.0313105046274129	0.0163361492031127	0.0224256799386937	0.0340918357832205	0.0856587592079277	0.0237281965942535	0.0185048402639438	0.0160553627979275	0.0248764504594100	0.00671041155944468	0.00860225270339545	0.0284231520878838	0.0148858821441466	0.0240840534643192	0.0324876487500475	0.0186392352007912	0.0290360108040825	0.0392029319357457	0.00514774680763223	0.0403734491344147	0.00553514948128624	0.0159472912736301	0.0391014747411382	0.0124504294222637	0.0356226945365535	0.0181628348862609	0.0178681779187954	0.00852772653415102	0.0138155179277036	0.0128797506883640	0.0175729080257820	0.0332649622959198	0.0132144919616132	0.0142189960587721	0.00867472220872289	0.0138585643963398	0.0808096300868375	0.0275859559565118	0.0430658837925290	0.0437529579942120	0.0432143087750007	0.0216644020803545	0.0282467031310019	0.0264198042749484	0.0186536205834701	0.0204944617589289	0.0161420750508947	0.0419642104728729	0.0266222127717096	0.0238260684162398	0.0167323386525880	0.0211166237490162	0.0138489451454549	0.0171711493485741	0.0198154734805207	0.0288256016492744	0.0217744398062373	0.0224640137433889	0.0119203543107133	0.0185285105932922	0.0276843904380591	0.0260460293444823	0.0116739173491231	0.0364410484579242	0.0261799258765370	0.0143295728798527	0.0618040252119864	0.0360762297813938	0.0299219101457212	0.0122104419238876	0.0224533153865488	0.0466342969006853	0.0439241647906221	0.0239186659728951	0.0429536063056506	0.0233204180398710	0.0257952340865214	0.0442263810315239	0.00465267102348124	0.00831411975658338	0.0236893052499721	0.0357391132784113	0.0214472226156945	0.0328176016665120	0.0167988766248693	0.0202837833033065	0.0119148052105929	0.0228826867462709	0.0343781708171176	0.0540834236256089	0.0253221711808191	0.0468323896367522	0.0742686819509908	0.0149921603233425	0.0299363682861656	0.0140422800476185	0.00923466574443423	0.0339342465603126	0.0291556774581404	0.0264800566361867	0.0254096416475342	0.0272339901951617	0.0306175784023154	0.0265822813377005	0.0285688811434644	0.0264281844291106	0.00846994973923620	0.0126390347157110	0.0117823693638192	0.0106438568823798	0.0262067037531181	0.0321805438741519	0.0148750170389071	0.0152049101084041	0.0107917917822201	0.0360250109804213	0.0227016064329870	0.0213593475134725	0.0244221264928307	0.0411664668012472	0.0401967822462838	0.00310215060314217	0.0332718783908383	0.0529584710776588	0.0135445495737136	0.00860916487815600	0.0279323739814191	0.0160524304304993	0.0171457403515751	0.0135776547989487	0.0106334907007662	0.0283472514790111	0.00776385651946830	0.0239886330282252	0.0199217325793269	0.0168300020490545	0.0198169217822589	0.0101689024408679	0.0421715201897865	0.0150827502752557	0.0226402615670400	0.0384003754097582	0.0243950799583878	0.0221702790775174	0.00567635788148187	0.0184935680511484	0.00957457983846342	0.0394434047176948	0.0137801538455711	0.00669108366533619	0.0175697985610505	0.0257116011994790	0.0206042977054387	0.0135485237464691	0.00741308386535177	0.0179047140558557	0.0258981916037672	0.0157092482144979	0.0301373200050465	0.0137831539061441	0.0171413050125207	0.0150572044609457	0.00938318206721905	0.0288920956887529	0.0568936987092498	0.0563216023427668	0.0238673837411345	0.00797360309686464	0.0204317022233995	0.0326997916439986	0.0726127769302676	0.0151435039662788	0.0123217164808681	0.0183259874692433	0.0214846876559265	0.0140200853872136	0.0376692550080025	0.0157611272316032	0.0258560042059497	0.0146939649993002	0.0253102202706720	0.0188959246844452];
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

[HAT]

@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]

%Two data x and y
x = [0.0630983945707523	0.0569310761963337	0.0902849174433386	0.0586557564536381	0.0334144697267278	0.0712631880471929	0.0759936968192505	0.123738339182888	0.0297032637109049	0.0795892282583819	0.0282261596294058	0.0537183249987105	0.150572257432409	0.0977963699033021	0.0885269208326175	0.0866396385249492	0.0669298504865110	0.0344312413925116	0.0348667960896803	0.0493656188493081	0.0270650538299466	0.0380968337434081	0.0888445642954378	0.0895945298885049	0.0541359853955182	0.0744321995464817	0.0913504586635817	0.0656554300621712	0.117259195293069	0.0713277353350769	0.0337360552372940	0.0806738045893977	0.0187938769214277	0.112203928934848	0.0344890616611609	0.112070100462341	0.0354340210241964	0.0485753458099790	0.100718883223941	0.0875411740696460	0.0356133031858813	0.0582817986356205	0.0482790634228989	0.158997322992062	0.0729992290097077	0.0471244743021920	0.0446084809659350	0.0511316023897959	0.0506496253843976	0.149380752893838	0.0689045480827398	0.0518533061920089	0.0209788552111653	0.0853321217563026	0.0933452759672151	0.0632321122010304	0.0989045624536658	0.118646945395764	0.0746394491425817	0.0563364973414752	0.0699405386310658	0.176313530448904	0.0805085967423252	0.0711951706934392	0.0985873777833532	0.0361123753750750	0.107620322990626	0.155953011309276	0.0703844359222595	0.0698300349659653	0.0133693291619266	0.0563251538076025	0.0420594335773483	0.0209929807249319	0.0836480611756999	0.0918760153342581	0.109071023988030	0.112011148456982	0.0932946841816926	0.0527273305572443	0.0603965022689918	0.0997831970370141	0.0865565948550563	0.0451864919017301	0.0534928555898882	0.0753611849580176	0.0975080705342945	0.0641363967713138	0.0492528378137929	0.0115895963831613	0.0395675354645857	0.0543562812505730	0.0302912028316128	0.0952996657586537	0.0735569358213397	0.0204415291346991	0.0291487407942866	0.0677917391211721	0.0439215658951729	0.0333816549041610	0.0456403551952354	0.0486553399302202	0.0470538432901701	0.0811957857944899	0.0515061853483591	0.0914618319303829	0.0545584954664158	0.0496585033306348	0.0394672348319468	0.0551272949007350	0.0520644196553922	0.0834432764756573	0.0734959836126599	0.0464406549571500	0.0597728280588548	0.0410213962648829	0.0690404853961215	0.0496809976564987	0.0361669205094010	0.0730322704667141	0.107730597350034	0.0248405355005877	0.0511942382576493	0.0189503937752495	0.0408680288793921	0.120785037290073	0.0594515231221030	0.0328245966542376	0.0548458148686953	0.0529949038865531	0.0791029104470528	0.0554237252866484	0.0994908340125133	0.0542399243766295	0.0534448107553341	0.0673593273333577	0.0939791192900367	0.0823300945762324	0.0442566600240820	0.0610617121274878	0.0898884338644292	0.0589796682804715	0.0976209948973652	0.0868444801571454	0.0652049359417058	0.0537021499678730	0.0301654519501758	0.0512536154351539	0.0345987437628045	0.0779282336255190	0.0825154044337572	0.0859799777142292	0.0442207168368985	0.00987243263527813	0.0950021713184207	0.0613437822551752	0.142441526816436	0.100802430273912	0.0414216701498731	0.109019675608790	0.0296913082336243	0.0347742860159156	0.135809763420103	0.0984506029234962	0.0674037418593082	0.0229407666296069	0.0189996852270467	0.0743571335114989	0.0847192814122880	0.115083690612888	0.123696178966709	0.0956417803959083	0.0519792874058779	0.0393076075577976	0.0677365377410694	0.0591426857839045	0.0847946264671823	0.0606756648202872	0.0724067734299440	0.0430835201376981	0.0808110853292779	0.0498423375322701	0.0194708896974106	0.0751131186727741	0.0348726389820165	0.0740612288670435	0.0594335516286884	0.0594348198017655	0.0682406308413635	0.0531894655861687	0.0743790302594293	0.0219558176107492	0.0272123662258039	0.108538439537859	0.0747059896282369	0.0946858822329740	0.0569276702396826	0.0149383525169137	0.0598050184040844	0.0545400309304210	0.0343002731803569	0.0811687405215188	0.0388267954856432	0.108631800100204	0.0674577426363181	0.0568544341837804	0.0108167619730890	0.0423029642373116	0.0377751860077256	0.0393552722330052	0.0710153987147849	0.121832351138184	0.0769382376509565	0.0943585640526814	0.0257138841804495	0.106163904814368	0.0364331244691357	0.0604418270748409	0.0738744794980614	0.137693441001674	0.0459022534232133];
y = [0.0250861693912511	0.00585190458464980	0.0680369926791528	0.0123549562586842	0.0282578426263928	0.0289878878512877	0.0183247815240238	0.0264046769351247	0.0298394588829129	0.0184375401541612	0.0128234393998260	0.0252050387926058	0.0295589374777074	0.0172581512714285	0.0198322914317110	0.0510612122109021	0.0265392101972772	0.0166542459114324	0.0614014018743578	0.0121560004556879	0.00718748693092065	0.0153044736253397	0.0293176969573287	0.0175869174921616	0.0114693237575025	0.0506590001559409	0.00610465839888697	0.0126487109773635	0.0702555922896282	0.0418570299060915	0.00236060532142230	0.0455516831459313	0.0110800140143406	0.0220056298859820	0.0105652706864878	0.0373136496652635	0.0387907583399752	0.0405489056158515	0.0548330383830699	0.0313105046274129	0.0163361492031127	0.0224256799386937	0.0340918357832205	0.0856587592079277	0.0237281965942535	0.0185048402639438	0.0160553627979275	0.0248764504594100	0.00671041155944468	0.00860225270339545	0.0284231520878838	0.0148858821441466	0.0240840534643192	0.0324876487500475	0.0186392352007912	0.0290360108040825	0.0392029319357457	0.00514774680763223	0.0403734491344147	0.00553514948128624	0.0159472912736301	0.0391014747411382	0.0124504294222637	0.0356226945365535	0.0181628348862609	0.0178681779187954	0.00852772653415102	0.0138155179277036	0.0128797506883640	0.0175729080257820	0.0332649622959198	0.0132144919616132	0.0142189960587721	0.00867472220872289	0.0138585643963398	0.0808096300868375	0.0275859559565118	0.0430658837925290	0.0437529579942120	0.0432143087750007	0.0216644020803545	0.0282467031310019	0.0264198042749484	0.0186536205834701	0.0204944617589289	0.0161420750508947	0.0419642104728729	0.0266222127717096	0.0238260684162398	0.0167323386525880	0.0211166237490162	0.0138489451454549	0.0171711493485741	0.0198154734805207	0.0288256016492744	0.0217744398062373	0.0224640137433889	0.0119203543107133	0.0185285105932922	0.0276843904380591	0.0260460293444823	0.0116739173491231	0.0364410484579242	0.0261799258765370	0.0143295728798527	0.0618040252119864	0.0360762297813938	0.0299219101457212	0.0122104419238876	0.0224533153865488	0.0466342969006853	0.0439241647906221	0.0239186659728951	0.0429536063056506	0.0233204180398710	0.0257952340865214	0.0442263810315239	0.00465267102348124	0.00831411975658338	0.0236893052499721	0.0357391132784113	0.0214472226156945	0.0328176016665120	0.0167988766248693	0.0202837833033065	0.0119148052105929	0.0228826867462709	0.0343781708171176	0.0540834236256089	0.0253221711808191	0.0468323896367522	0.0742686819509908	0.0149921603233425	0.0299363682861656	0.0140422800476185	0.00923466574443423	0.0339342465603126	0.0291556774581404	0.0264800566361867	0.0254096416475342	0.0272339901951617	0.0306175784023154	0.0265822813377005	0.0285688811434644	0.0264281844291106	0.00846994973923620	0.0126390347157110	0.0117823693638192	0.0106438568823798	0.0262067037531181	0.0321805438741519	0.0148750170389071	0.0152049101084041	0.0107917917822201	0.0360250109804213	0.0227016064329870	0.0213593475134725	0.0244221264928307	0.0411664668012472	0.0401967822462838	0.00310215060314217	0.0332718783908383	0.0529584710776588	0.0135445495737136	0.00860916487815600	0.0279323739814191	0.0160524304304993	0.0171457403515751	0.0135776547989487	0.0106334907007662	0.0283472514790111	0.00776385651946830	0.0239886330282252	0.0199217325793269	0.0168300020490545	0.0198169217822589	0.0101689024408679	0.0421715201897865	0.0150827502752557	0.0226402615670400	0.0384003754097582	0.0243950799583878	0.0221702790775174	0.00567635788148187	0.0184935680511484	0.00957457983846342	0.0394434047176948	0.0137801538455711	0.00669108366533619	0.0175697985610505	0.0257116011994790	0.0206042977054387	0.0135485237464691	0.00741308386535177	0.0179047140558557	0.0258981916037672	0.0157092482144979	0.0301373200050465	0.0137831539061441	0.0171413050125207	0.0150572044609457	0.00938318206721905	0.0288920956887529	0.0568936987092498	0.0563216023427668	0.0238673837411345	0.00797360309686464	0.0204317022233995	0.0326997916439986	0.0726127769302676	0.0151435039662788	0.0123217164808681	0.0183259874692433	0.0214846876559265	0.0140200853872136	0.0376692550080025	0.0157611272316032	0.0258560042059497	0.0146939649993002	0.0253102202706720	0.0188959246844452];
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]

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]

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]

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

%Two data x and y
x = [0.0630983945707523	0.0569310761963337	0.0902849174433386	0.0586557564536381	0.0334144697267278	0.0712631880471929	0.0759936968192505	0.123738339182888	0.0297032637109049	0.0795892282583819	0.0282261596294058	0.0537183249987105	0.150572257432409	0.0977963699033021	0.0885269208326175	0.0866396385249492	0.0669298504865110	0.0344312413925116	0.0348667960896803	0.0493656188493081	0.0270650538299466	0.0380968337434081	0.0888445642954378	0.0895945298885049	0.0541359853955182	0.0744321995464817	0.0913504586635817	0.0656554300621712	0.117259195293069	0.0713277353350769	0.0337360552372940	0.0806738045893977	0.0187938769214277	0.112203928934848	0.0344890616611609	0.112070100462341	0.0354340210241964	0.0485753458099790	0.100718883223941	0.0875411740696460	0.0356133031858813	0.0582817986356205	0.0482790634228989	0.158997322992062	0.0729992290097077	0.0471244743021920	0.0446084809659350	0.0511316023897959	0.0506496253843976	0.149380752893838	0.0689045480827398	0.0518533061920089	0.0209788552111653	0.0853321217563026	0.0933452759672151	0.0632321122010304	0.0989045624536658	0.118646945395764	0.0746394491425817	0.0563364973414752	0.0699405386310658	0.176313530448904	0.0805085967423252	0.0711951706934392	0.0985873777833532	0.0361123753750750	0.107620322990626	0.155953011309276	0.0703844359222595	0.0698300349659653	0.0133693291619266	0.0563251538076025	0.0420594335773483	0.0209929807249319	0.0836480611756999	0.0918760153342581	0.109071023988030	0.112011148456982	0.0932946841816926	0.0527273305572443	0.0603965022689918	0.0997831970370141	0.0865565948550563	0.0451864919017301	0.0534928555898882	0.0753611849580176	0.0975080705342945	0.0641363967713138	0.0492528378137929	0.0115895963831613	0.0395675354645857	0.0543562812505730	0.0302912028316128	0.0952996657586537	0.0735569358213397	0.0204415291346991	0.0291487407942866	0.0677917391211721	0.0439215658951729	0.0333816549041610	0.0456403551952354	0.0486553399302202	0.0470538432901701	0.0811957857944899	0.0515061853483591	0.0914618319303829	0.0545584954664158	0.0496585033306348	0.0394672348319468	0.0551272949007350	0.0520644196553922	0.0834432764756573	0.0734959836126599	0.0464406549571500	0.0597728280588548	0.0410213962648829	0.0690404853961215	0.0496809976564987	0.0361669205094010	0.0730322704667141	0.107730597350034	0.0248405355005877	0.0511942382576493	0.0189503937752495	0.0408680288793921	0.120785037290073	0.0594515231221030	0.0328245966542376	0.0548458148686953	0.0529949038865531	0.0791029104470528	0.0554237252866484	0.0994908340125133	0.0542399243766295	0.0534448107553341	0.0673593273333577	0.0939791192900367	0.0823300945762324	0.0442566600240820	0.0610617121274878	0.0898884338644292	0.0589796682804715	0.0976209948973652	0.0868444801571454	0.0652049359417058	0.0537021499678730	0.0301654519501758	0.0512536154351539	0.0345987437628045	0.0779282336255190	0.0825154044337572	0.0859799777142292	0.0442207168368985	0.00987243263527813	0.0950021713184207	0.0613437822551752	0.142441526816436	0.100802430273912	0.0414216701498731	0.109019675608790	0.0296913082336243	0.0347742860159156	0.135809763420103	0.0984506029234962	0.0674037418593082	0.0229407666296069	0.0189996852270467	0.0743571335114989	0.0847192814122880	0.115083690612888	0.123696178966709	0.0956417803959083	0.0519792874058779	0.0393076075577976	0.0677365377410694	0.0591426857839045	0.0847946264671823	0.0606756648202872	0.0724067734299440	0.0430835201376981	0.0808110853292779	0.0498423375322701	0.0194708896974106	0.0751131186727741	0.0348726389820165	0.0740612288670435	0.0594335516286884	0.0594348198017655	0.0682406308413635	0.0531894655861687	0.0743790302594293	0.0219558176107492	0.0272123662258039	0.108538439537859	0.0747059896282369	0.0946858822329740	0.0569276702396826	0.0149383525169137	0.0598050184040844	0.0545400309304210	0.0343002731803569	0.0811687405215188	0.0388267954856432	0.108631800100204	0.0674577426363181	0.0568544341837804	0.0108167619730890	0.0423029642373116	0.0377751860077256	0.0393552722330052	0.0710153987147849	0.121832351138184	0.0769382376509565	0.0943585640526814	0.0257138841804495	0.106163904814368	0.0364331244691357	0.0604418270748409	0.0738744794980614	0.137693441001674	0.0459022534232133];
y = [0.0250861693912511	0.00585190458464980	0.0680369926791528	0.0123549562586842	0.0282578426263928	0.0289878878512877	0.0183247815240238	0.0264046769351247	0.0298394588829129	0.0184375401541612	0.0128234393998260	0.0252050387926058	0.0295589374777074	0.0172581512714285	0.0198322914317110	0.0510612122109021	0.0265392101972772	0.0166542459114324	0.0614014018743578	0.0121560004556879	0.00718748693092065	0.0153044736253397	0.0293176969573287	0.0175869174921616	0.0114693237575025	0.0506590001559409	0.00610465839888697	0.0126487109773635	0.0702555922896282	0.0418570299060915	0.00236060532142230	0.0455516831459313	0.0110800140143406	0.0220056298859820	0.0105652706864878	0.0373136496652635	0.0387907583399752	0.0405489056158515	0.0548330383830699	0.0313105046274129	0.0163361492031127	0.0224256799386937	0.0340918357832205	0.0856587592079277	0.0237281965942535	0.0185048402639438	0.0160553627979275	0.0248764504594100	0.00671041155944468	0.00860225270339545	0.0284231520878838	0.0148858821441466	0.0240840534643192	0.0324876487500475	0.0186392352007912	0.0290360108040825	0.0392029319357457	0.00514774680763223	0.0403734491344147	0.00553514948128624	0.0159472912736301	0.0391014747411382	0.0124504294222637	0.0356226945365535	0.0181628348862609	0.0178681779187954	0.00852772653415102	0.0138155179277036	0.0128797506883640	0.0175729080257820	0.0332649622959198	0.0132144919616132	0.0142189960587721	0.00867472220872289	0.0138585643963398	0.0808096300868375	0.0275859559565118	0.0430658837925290	0.0437529579942120	0.0432143087750007	0.0216644020803545	0.0282467031310019	0.0264198042749484	0.0186536205834701	0.0204944617589289	0.0161420750508947	0.0419642104728729	0.0266222127717096	0.0238260684162398	0.0167323386525880	0.0211166237490162	0.0138489451454549	0.0171711493485741	0.0198154734805207	0.0288256016492744	0.0217744398062373	0.0224640137433889	0.0119203543107133	0.0185285105932922	0.0276843904380591	0.0260460293444823	0.0116739173491231	0.0364410484579242	0.0261799258765370	0.0143295728798527	0.0618040252119864	0.0360762297813938	0.0299219101457212	0.0122104419238876	0.0224533153865488	0.0466342969006853	0.0439241647906221	0.0239186659728951	0.0429536063056506	0.0233204180398710	0.0257952340865214	0.0442263810315239	0.00465267102348124	0.00831411975658338	0.0236893052499721	0.0357391132784113	0.0214472226156945	0.0328176016665120	0.0167988766248693	0.0202837833033065	0.0119148052105929	0.0228826867462709	0.0343781708171176	0.0540834236256089	0.0253221711808191	0.0468323896367522	0.0742686819509908	0.0149921603233425	0.0299363682861656	0.0140422800476185	0.00923466574443423	0.0339342465603126	0.0291556774581404	0.0264800566361867	0.0254096416475342	0.0272339901951617	0.0306175784023154	0.0265822813377005	0.0285688811434644	0.0264281844291106	0.00846994973923620	0.0126390347157110	0.0117823693638192	0.0106438568823798	0.0262067037531181	0.0321805438741519	0.0148750170389071	0.0152049101084041	0.0107917917822201	0.0360250109804213	0.0227016064329870	0.0213593475134725	0.0244221264928307	0.0411664668012472	0.0401967822462838	0.00310215060314217	0.0332718783908383	0.0529584710776588	0.0135445495737136	0.00860916487815600	0.0279323739814191	0.0160524304304993	0.0171457403515751	0.0135776547989487	0.0106334907007662	0.0283472514790111	0.00776385651946830	0.0239886330282252	0.0199217325793269	0.0168300020490545	0.0198169217822589	0.0101689024408679	0.0421715201897865	0.0150827502752557	0.0226402615670400	0.0384003754097582	0.0243950799583878	0.0221702790775174	0.00567635788148187	0.0184935680511484	0.00957457983846342	0.0394434047176948	0.0137801538455711	0.00669108366533619	0.0175697985610505	0.0257116011994790	0.0206042977054387	0.0135485237464691	0.00741308386535177	0.0179047140558557	0.0258981916037672	0.0157092482144979	0.0301373200050465	0.0137831539061441	0.0171413050125207	0.0150572044609457	0.00938318206721905	0.0288920956887529	0.0568936987092498	0.0563216023427668	0.0238673837411345	0.00797360309686464	0.0204317022233995	0.0326997916439986	0.0726127769302676	0.0151435039662788	0.0123217164808681	0.0183259874692433	0.0214846876559265	0.0140200853872136	0.0376692550080025	0.0157611272316032	0.0258560042059497	0.0146939649993002	0.0253102202706720	0.0188959246844452];
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]

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]

@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]

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]

Thank you very much. Now I edited my question.