{"id":4236,"date":"2023-07-12T15:27:26","date_gmt":"2023-07-12T15:27:26","guid":{"rendered":"https:\/\/statorials.org\/my\/seaborn-regplot-%e1%80%8a%e1%80%ae%e1%80%99%e1%80%bb%e1%80%be%e1%80%81%e1%80%bc%e1%80%84%e1%80%ba%e1%80%b8\/"},"modified":"2023-07-12T15:27:26","modified_gmt":"2023-07-12T15:27:26","slug":"seaborn-regplot-%e1%80%8a%e1%80%ae%e1%80%99%e1%80%bb%e1%80%be%e1%80%81%e1%80%bc%e1%80%84%e1%80%ba%e1%80%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/seaborn-regplot-%e1%80%8a%e1%80%ae%e1%80%99%e1%80%bb%e1%80%be%e1%80%81%e1%80%bc%e1%80%84%e1%80%ba%e1%80%b8\/","title":{"rendered":"Seaborn reglot \u1010\u103d\u1004\u103a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u101e\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b"},"content":{"rendered":"

<\/p>\n

\n

\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101e\u1031\u102c linear regression model \u1000\u102d\u102f \u101b\u1031\u1038\u1006\u103d\u1032\u101b\u1014\u103a seaborn regplot<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/span><\/p>\n

\u1000\u1036\u1019\u1000\u1031\u102c\u1004\u103a\u1038\u1005\u103d\u102c\u1016\u103c\u1004\u1037\u103a\u104a Seaborn \u1010\u103d\u1004\u103a \u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1019\u103e \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101a\u1030\u101b\u1014\u103a built-in \u1021\u1004\u103a\u1039\u1002\u102b\u101b\u1015\u103a \u1019\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038\u104a \u101e\u1004\u103a\u101e\u100a\u103a scipy.stats.linregress<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1019\u103c\u1014\u103a\u101b\u103e\u102c\u1016\u103d\u1031\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n

 import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. x<\/span> , y=df. y<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n<\/strong><\/pre>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1025\u1015\u1019\u102c\u101e\u100a\u103a \u1024 syntax \u1000\u102d\u102f \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c- Seaborn Reglot \u1010\u103d\u1004\u103a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1015\u1014\u103a\u1012\u102b DataFrame \u101b\u103e\u102d\u101e\u100a\u103a \u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037 \u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u102c\u1038\u1000\u103b\u1031\u102c\u1004\u103a\u1038\u101e\u1030 \u1021\u101e\u102e\u1038\u101e\u102e\u1038\u104f \u101e\u1004\u103a\u1000\u103c\u102c\u1038\u1001\u1032\u1037\u101e\u100a\u1037\u103a \u1014\u102c\u101b\u102e\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032\u101b\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038\u1021\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038 \u1015\u102b\u1040\u1004\u103a\u101e\u100a\u103a\u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' score<\/span> ': [77, 79, 84, 80, 81, 89, 95, 90, 83, 89]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   hours score\n0 1 77\n1 2 79\n2 3 84\n3 4 80\n4 5 81\n5 6 89\n6 7 95\n7 8 90\n8 9 83\n9 10 89\n<\/strong><\/pre>\n
 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1006\u103d\u1032\u1001\u103b\u1015\u103c\u102e\u1038 \u1012\u1031\u1010\u102c\u101e\u102d\u102f\u1037 \u1010\u1015\u103a\u1006\u1004\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1019\u103b\u1009\u103a\u1038\u1000\u102d\u102f \u1015\u1031\u102b\u1004\u103a\u1038\u1011\u100a\u1037\u103a\u101c\u102d\u102f\u101e\u100a\u103a\u1006\u102d\u102f\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n
 \u1012\u102b\u1000\u102d\u102f\u101c\u102f\u1015\u103a\u1016\u102d\u102f\u1037 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u1010\u101a\u103a\u104b<\/span> <\/p>\n
 import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#display slope and intercept of regression equation\n<\/span>print<\/span> (intercept, slope)\n\n77.39999999999995 1.3272727272727356\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1019\u103b\u1009\u103a\u1038\u1010\u103d\u1004\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n
 y = 77.4 + 1.327<\/strong><\/span><\/p>\n
 seaborn regplot<\/strong> \u1010\u103d\u1004\u103a \u1024\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1021\u102c\u1038 \u1015\u103c\u101e\u101c\u102d\u102f\u1015\u102b\u1000 matplotlib text()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\nimport<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n                                                       y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#add regression equation to plot\n<\/span>plt. text<\/span> (2, 95, ' y =<\/span> ' + str(round(intercept,3)) + ' +<\/span> ' + str(round(slope,3)) + ' x<\/span> ')<\/span><\/strong> <\/pre>\n
\"Seaborn<\/p>\n
 \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1021\u102c\u1038 \u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u104f \u1018\u101a\u103a\u1018\u1000\u103a\u1021\u1015\u1031\u102b\u103a\u1011\u1031\u102c\u1004\u1037\u103a\u1010\u103d\u1004\u103a \u101a\u1001\u102f\u1015\u103c\u101e\u1011\u102c\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n
 text()<\/strong> function \u1010\u103d\u1004\u103a regression equation \u1000\u102d\u102f (x, y) coordinates \u1019\u103b\u102c\u1038\u1019\u103e (2, 95) \u1019\u103e\u1015\u103c\u101e\u101e\u1004\u1037\u103a\u101e\u100a\u103a\u101f\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n
 \u101e\u1004\u1037\u103a\u1000\u102d\u102f\u101a\u103a\u1015\u102d\u102f\u1004\u103a\u1000\u103c\u1036\u1005\u100a\u103a\u1019\u103e\u102f\u1010\u103d\u1004\u103a \u101e\u1004\u103a\u1021\u101c\u102d\u102f\u101b\u103e\u102d\u101e\u100a\u1037\u103a\u1014\u1031\u101b\u102c\u1010\u102d\u102f\u1004\u103a\u1038\u1010\u103d\u1004\u103a \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f\u1015\u103c\u101e\u101b\u1014\u103a \u1024\u101e\u103c\u1012\u102d\u1014\u102d\u1010\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1001\u1019\u1032\u1037\u1015\u103c\u1004\u103a\u1006\u1004\u103a\u1015\u102b\u104b<\/span><\/p>\n
 \u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> – seaborn regplot<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1005\u102c\u101b\u103d\u1000\u103a\u1005\u102c\u1010\u1019\u103a\u1038\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1016\u103d\u1031\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a Seaborn \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u1018\u102f\u1036\u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 Seaborn \u1007\u102c\u1010\u103a\u1000\u103d\u1000\u103a\u104f \u1015\u102f\u1036\u1021\u101b\u103d\u101a\u103a\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1014\u100a\u103a\u1038<\/a>
 Seaborn \u1019\u103e\u102c \u1012\u100f\u1039\u100d\u102c\u101b\u102e\u1010\u1005\u103a\u101a\u1031\u102c\u1000\u103a\u101b\u1032\u1037 \u1021\u1014\u1031\u1021\u1011\u102c\u1038\u1000\u102d\u102f \u1018\u101a\u103a\u101c\u102d\u102f\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1019\u101c\u1032\u104b<\/a>
 Seaborn \u1000\u103d\u1000\u103a\u1010\u103d\u1004\u103a \u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1021\u100a\u103d\u103e\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101e\u1031\u102c linear regression model \u1000\u102d\u102f \u101b\u1031\u1038\u1006\u103d\u1032\u101b\u1014\u103a seaborn regplot \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b \u1000\u1036\u1019\u1000\u1031\u102c\u1004\u103a\u1038\u1005\u103d\u102c\u1016\u103c\u1004\u1037\u103a\u104a Seaborn \u1010\u103d\u1004\u103a \u1019\u103b\u1009\u103a\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1019\u103e \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101a\u1030\u101b\u1014\u103a built-in \u1021\u1004\u103a\u1039\u1002\u102b\u101b\u1015\u103a \u1019\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038\u104a \u101e\u1004\u103a\u101e\u100a\u103a scipy.stats.linregress \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u1002\u100f\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1019\u103c\u1014\u103a\u101b\u103e\u102c\u1016\u103d\u1031\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a- import scipy import seaborn as sns #create regplot p = sns. regplot (data=df, x=df. x , y=df. y ) #calculate slope and intercept of regression equation slope, intercept, r, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\nSeaborn Regplot - 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