i need to perform a regression analysis, in which i will predict the profit of startups for a venture capitalist who wants to analyze whether a startup is worth investing to get good returns. I will analyze a dataset which contains the operational details of startups (R&D, marketing spends etc.) is and predicts the profit of a new Startup based on those features. To Venture Capitalists this could be a boon as to whether they should invest in a particular Startup or not.

R&D Spend	Administration	Marketing Spend	State	Profit
165349.2	136897.8	471784.1	New York	192261.8
162597.7	151377.6	443898.5	California	191792.1
153441.5	101145.6	407934.5	Florida	191050.4
144372.4	118671.9	383199.6	New York	182902
142107.3	91391.77	366168.4	Florida	166187.9
131876.9	99814.71	362861.4	New York	156991.1
134615.5	147198.9	127716.8	California	156122.5
130298.1	145530.1	323876.7	Florida	155752.6
120542.5	148719	311613.3	New York	152211.8
123334.9	108679.2	304981.6	California	149760
101913.1	110594.1	229161	Florida	146122
100672	91790.61	249744.6	California	144259.4
93863.75	127320.4	249839.4	Florida	141585.5
91992.39	135495.1	252664.9	California	134307.4
119943.2	156547.4	256512.9	Florida	132602.7
114523.6	122616.8	261776.2	New York	129917
78013.11	121597.6	264346.1	California	126992.9
94657.16	145077.6	282574.3	New York	125370.4
91749.16	114175.8	294919.6	Florida	124266.9
86419.7	153514.1	0	New York	122776.9
76253.86	113867.3	298664.5	California	118474
78389.47	153773.4	299737.3	New York	111313
73994.56	122782.8	303319.3	Florida	110352.3
67532.53	105751	304768.7	Florida	108734
77044.01	99281.34	140574.8	New York	108552
64664.71	139553.2	137962.6	California	107404.3
75328.87	144136	134050.1	Florida	105733.5
72107.6	127864.6	353183.8	New York	105008.3
66051.52	182645.6	118148.2	Florida	103282.4
65605.48	153032.1	107138.4	New York	101004.6
61994.48	115641.3	91131.24	Florida	99937.59
61136.38	152701.9	88218.23	New York	97483.56
63408.86	129219.6	46085.25	California	97427.84
55493.95	103057.5	214634.8	Florida	96778.92
46426.07	157693.9	210797.7	California	96712.8
46014.02	85047.44	205517.6	New York	96479.51
28663.76	127056.2	201126.8	Florida	90708.19
44069.95	51283.14	197029.4	California	89949.14
20229.59	65947.93	185265.1	New York	81229.06
38558.51	82982.09	174999.3	California	81005.76
28754.33	118546.1	172795.7	California	78239.91
27892.92	84710.77	164470.7	Florida	77798.83
23640.93	96189.63	148001.1	California	71498.49
15505.73	127382.3	35534.17	New York	69758.98
22177.74	154806.1	28334.72	California	65200.33
1000.23	124153	1903.93	New York	64926.08
1315.46	115816.2	297114.5	Florida	49490.75
0	135426.9	0	California	42559.73
542.05	51743.15	0	New York	35673.41
0	116983.8	45173.06	California	14681.4

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.975062
R Square	0.950746
Adjusted R Square	0.947534
Standard Error	9232.335
Observations	50
ANOVA
	df	SS	MS	F	Significance F
Regression	3	7.57E+10	2.52E+10	295.9781	4.53E-30
Residual	46	3.92E+09	85236007
Total	49	7.96E+10
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	50122.19	6572.353	7.626218	1.06E-09	36892.73	63351.65	36892.73	63351.65
R&D Spend	0.805715	0.045147	17.84637	2.63E-22	0.714838	0.896592	0.714838	0.896592
Administration	-0.02682	0.051029	-0.52551	0.601755	-0.12953	0.0759	-0.12953	0.0759
Marketing Spend	0.027228	0.016451	1.655077	0.104717	-0.00589	0.060343	-0.00589	0.060343
RESIDUAL OUTPUT
Observation	Predicted Profit	Residuals
1	192521.3	-259.423
2	189156.8	2635.292
3	182147.3	8903.111
4	173696.7	9205.29
5	172139.5	-5951.57
6	163580.8	-6589.66
7	158114.1	-1991.59
8	160021.4	-4268.76
9	151741.7	470.0703
10	154884.7	-5124.72
11	135509	10612.93
12	135573.7	8685.687
13	129138.1	12447.47
14	127488	6819.358
15	149548.6	-16946
16	146235.2	-16318.1
17	116915.4	10077.52
18	130192.4	-4822.08
19	129014.2	-4747.33
20	115635.2	7141.644
21	116639.7	1834.361
22	117319.5	-6006.43
23	114707	-4354.73
24	109996.6	-1262.63
25	113363	-4810.93
26	102237.7	5166.615
27	110600.6	-4867.04
28	114408.1	-9399.76
29	101660	1622.354
30	101795	-790.343
31	99452.37	485.2171
32	97687.86	-204.296
33	99001.33	-1573.49
34	97915.01	-1136.09
35	89039.27	7673.526
36	90511.6	5967.91
37	75286.17	15422.02
38	89619.54	329.6023
39	69697.43	11531.63
40	83729.01	-2723.25
41	74815.95	3423.956
42	74802.56	2996.274
43	70620.41	878.0782
44	60167.04	9591.94
45	64611.35	588.9751
46	47650.65	17275.43
47	56166.21	-6675.46
48	46490.59	-3930.86
49	49171.39	-13498
50	48215.13	-33533.7

having this data i run the following regression and recieved the following results in excell

now i need to make a conclusion based on the results and to match the question at the beginning .what will be the conclusion and the python code