Question

As preparation for the final research paper, you formulated a theory about the correlation between measurable independent variables (causes) and one measurable dependent variable (the effect). Be sure to have at least two independent variables for proposed research paper. This rough draft should include the following four items which serve as the foundation for the final research paper after instructor feedback is given.

Submit this Word File in this Canvas section for your Term Project Rough Draft, with Data.

The dependent variable for this study Major League Baseball’s (MLB) Player Salary is determined by independent variables such as player’s batting average (AVG), on-base plus slugging (OPS), and runs batted in (RBI). The most important independent variable in this relationship is OPS because if the player has a high rate of reaching base it contributes to the player’s AVG and RBI.

I plan to utilize the model below:

Player’s annual salary: b0+b1AVG+b2OPS+b3RBI

PLAYER	OPS	RBI	AVG	SAL USD (MILLION)
TROUT, MIKE	1.083	104	0.291	36,000,000
ARENADO, NOLAN	0.962	118	0.315	35,000,000
MACHADO,MANNY	0.796	85	0.256	30,000,000
CABRERA, MIGUEL	0.745	108	0.283	30,000,000
CESEDES, YOENIS	0.821	29	0.262	29,000,000
PUJOLS, ALBERT	0.735	93	0.244	29,000,000
BETTS, MOOKIE	0.915	80	0.295	27,000,000
GIANCARLO, STANTON	0.895	13	0.288	26,000,000
ALTUVE, JOSE	0.903	74	0.298	26,000,000
HARPER, BRYCE	0.882	114	0.26	26,000,000
RENDON, ANTHONY	1.01	126	0.319	25,500,000
VOTTO, JOEY	0.768	47	0.261	25,000,000
ROBINSON, CANO	0.735	39	0.256	24,000,000
MARTINEZ, J.D.	0.94	105	0.304	23,750,000
FREEMAN, FREDDIE	0.938	121	0.295	22,000,000
GOLDSCHMIDT, PAUL	0.822	97	0.26	22,000,000
POSEY, BUSTER	0.688	38	0.257	21,400,000
UPTON, JUSTIN	0.725	40	0.215	21,000,000
CHOO, SHIN-SOO	0.826	61	0.265	21,000,000
BLACKMON, CHARLIE	0.94	86	0.314	21,000,000
HEYWARD, JASON	0.773	62	0.251	21,000,000
SPRINGER, GEORGE	0.974	96	0.292	21,000,000
DONALDSON, JOSH	0.379	94	0.259	21,000,000
BOGAERTS, XANDER	0.939	117	0.309	20,000,000
MYERS, WIL	0.739	53	0.239	20,000,000
HOSMER, ERIC	0.735	99	0.265	20,000,000
MOLINA, YADIER	0.711	57	0.27	20,000,000
TURNER, JUSTIN	0.372	51	0.29	19,000,000
SEAGER, KYLE	0.784	63	0.237	19,000,000
BRYANT, KRIS	0.903	77	0.282	18,600,000

Answer 1

yes the value of intercept and slopes are below

##########################################################

data=read.csv("D:basket.csv",header=T)
> data
PLAYER OPS RBI AVG SALARY
1 TROUT, MIKE 1.083 104 0.291 36000000
2 ARENADO, NOLAN 0.962 118 0.315 35000000
3 MACHADO,MANNY 0.796 85 0.256 30000000
4 CABRERA, MIGUEL 0.745 108 0.283 30000000
5 CESEDES, YOENIS 0.821 29 0.262 29000000
6 PUJOLS, ALBERT 0.735 93 0.244 29000000
7 BETTS, MOOKIE 0.915 80 0.295 27000000
8 GIANCARLO, STANTON 0.895 13 0.288 26000000
9 ALTUVE, JOSE 0.903 74 0.298 26000000
10 HARPER, BRYCE 0.882 114 0.260 26000000
11 RENDON, ANTHONY 1.010 126 0.319 25500000
12 VOTTO, JOEY 0.768 47 0.261 25000000
13 ROBINSON, CANO 0.735 39 0.256 24000000
14 MARTINEZ, J.D. 0.940 105 0.304 23750000
15 FREEMAN, FREDDIE 0.938 121 0.295 22000000
16 GOLDSCHMIDT, PAUL 0.822 97 0.260 22000000
17 POSEY, BUSTER 0.688 38 0.257 21400000
18 UPTON, JUSTIN 0.725 40 0.215 21000000
19 CHOO, SHIN-SOO 0.826 61 0.265 21000000
20 BLACKMON, CHARLIE 0.940 86 0.314 21000000
21 HEYWARD, JASON 0.773 62 0.251 21000000
22 SPRINGER, GEORGE 0.974 96 0.292 21000000
23 DONALDSON, JOSH 0.379 94 0.259 21000000
24 BOGAERTS, XANDER 0.939 117 0.309 20000000
25 MYERS, WIL 0.739 53 0.239 20000000
26 HOSMER, ERIC 0.735 99 0.265 20000000
27 MOLINA, YADIER 0.711 57 0.270 20000000
28 TURNER, JUSTIN 0.372 51 0.290 19000000
29 SEAGER, KYLE 0.784 63 0.237 19000000
30 BRYANT, KRIS 0.903 77 0.282 18600000
> x1=data$OPS
> x1
[1] 1.083 0.962 0.796 0.745 0.821 0.735 0.915 0.895 0.903 0.882 1.010 0.768
[13] 0.735 0.940 0.938 0.822 0.688 0.725 0.826 0.940 0.773 0.974 0.379 0.939
[25] 0.739 0.735 0.711 0.372 0.784 0.903
> x2=data$RBI
> x2
[1] 104 118 85 108 29 93 80 13 74 114 126 47 39 105 121 97 38 40 61
[20] 86 62 96 94 117 53 99 57 51 63 77
> x3=data$AVG
> x3
[1] 0.291 0.315 0.256 0.283 0.262 0.244 0.295 0.288 0.298 0.260 0.319 0.261
[13] 0.256 0.304 0.295 0.260 0.257 0.215 0.265 0.314 0.251 0.292 0.259 0.309
[25] 0.239 0.265 0.270 0.290 0.237 0.282
> x4=data$SALARY
> x4
[1] 36000000 35000000 30000000 30000000 29000000 29000000 27000000 26000000
[9] 26000000 26000000 25500000 25000000 24000000 23750000 22000000 22000000
[17] 21400000 21000000 21000000 21000000 21000000 21000000 21000000 20000000
[25] 20000000 20000000 20000000 19000000 19000000 18600000
> fit=lm(x4~x1+x2+x3)
> fit

Call:
lm(formula = x4 ~ x1 + x2 + x3)

Coefficients:
(Intercept) x1 x2 x3
13833897 10200269 15440 2395626

>