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 statsrishabh 29 minutes ago Missing information reference You 17 minutes ago i need to do the implementation of regression analysis, in which we 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 analyzed 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

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Question: i need to perform a regression analysis , in which i will predict the profit of startups for a venture capitalist who wants to analyze

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

statsrishabh

29 minutes ago

Missing information: reference

You

17 minutes ago

i need to do the implementation of regression analysis, in which we 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 analyzed 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.

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