A Proper Outlier Detection For the Attached Figure I am wondering how I could detect the red points as outliers using an algorithm (best method for this scenario) not through visualization since it is clear that they are outliers in the figure. Sometimes the red points are close to blue points but still very clear that the trend of data is broken like the attached one (80-90).

[0.00141879500243899,0.00178697795331762,0.00127892495577177,0.00143570026155573,0.00153525992083240,0.00141777922735790,0.00152684591126789,0.00148885286898430,0.00147674740998246,0.00171508938382390,0.00165398976301962,0.00142538361585036,0.00153910246593531,0.00175399308385697,0.00134698941964965,0.00172515548793911,0.00149595587129531,0.00180172725031118,0.00159037639551324,0.00187032999673476,0.00162790342840977,0.00184600681551365,0.00135449709645852,0.00176929230036940,0.00153284963291986,0.00142926162224541,0.00159764194281474,0.00163851042007631,0.00176726005837321,0.00154359218169479,0.00149738650032808,0.00152516973826902,0.00175781634013908,0.00193870678151384,0.00134159374501505,0.00166629459390643,0.00188211161641099,0.00142375632598846,0.00137379155155053,0.00183054686766502,0.00160358781806976,0.00143105342415080,0.00142689725164074,0.00133488790173896,0.00133731094914472,0.00157740531444255,0.00176267948564612,0.00158566715398123,0.00168298974941153,0.00188913573739626,0.00152608364331322,0.00161781941692366,0.00170761490900949,0.00128898986929469,0.00142269915268986,0.00153236250883160,0.00134121959772013,0.00138340739578642,0.00163948156950012,0.00158019133687677,0.00181565712864830,0.00159289331356989,0.00149272763991994,0.00150116573044485,0.00154147840865167,0.00158596715872812,0.00121074317389216,0.00156105400252430,0.00131969571973573,0.00150953288527507,0.00149105878102976,0.00147152491535477,0.00185102147226634,0.00142891794335564,0.00138431150537657,0.00151693760998362,0.00151919782691779,0.00109257864093704,0.00161380666227643,0.00190699585602371,0.000820445758286120,0.000798119153983038,0.000839861185604018,0.000861822463118421,0.000859391873730350,0.000990174686493857,0.000848612912439895,0.000916179621359871,0.00106081255138086,0.00117911733587858,0.00154535094976525,0.00144471958801459,0.00151941232953032,0.00150116342904493,0.00150758186003230,0.00174635072071058,0.00165893906442366,0.00184780915258433,0.00188991692005409,0.00150433295701926]


Thanks.
One approach would be to fit a linear regression and look at Cooks Distance to detect outliers.
https://en.m.wikipedia.org/wiki/Cook%27s_distance

Some R background: http://r-statistics.co/Outlier-Treatment-With-R.html

Some Python discussion: https://stackoverflow.com/a/52322232/9524424