Question

It is expected that there will be a positive correlation between the three independent variables (ERA, W, & SO) and the dependent variable (salary); and strike-outs will bear the largest correlation to increased salary. Strikeouts is an important variable because the success of the team relies strongly on it.

Player	2018 Salary	ERA	W	SO
Dellin Betances	$5,100,000	2.87	3	100
Zach Britton	$12,000,000	2.89	2	29
Luis Cessa	$507,500	4.75	0	30
Aroldis Chapman	$17,200,000	3.22	4	69
A.J. Cole	$555,300	3.81	3	44
Domingo German	$507,500	3.14	0	18
Sonny Gray	$6,500,000	3.55	10	153
Chad Green	$570,800	1.83	5	103
J.A. Happ	$13,000,000	3.53	10	142
Jonathan Holder	$553,850	3.89	1	40
Tommy Kahnle	$1,310,000	2.59	4	96
Jonathan Loaisiga	$545,000	5.82	2	29
Lance Lynn	$12,000,000	3.43	11	153
David Robertson	$13,000,000	1.84	9	98
CC Sabathia	$10,000,000	3.69	14	120
Luis Severino	$604,980	2.98	14	230
Justus Sheffield	$545,000	3.12	7	88
Masahiro Tanaka	$22,000,000.00	4.74	13	194
Dellin Betances	$5,100,000.00	2.87	3	100

Run a regression analysis on the data above and discuss the results. Is the hypothesis correct, why or why not?

Answer 1

Step by step procedure to run regression analysis report in excel

1. Bring data set in to excel file

2. goto DAta tab --> choose data analysis -- > choose regression .

3. If data analysis is not available in excel then go to excel option --> choose Add-ins --> choose excel add-in and add tas analytics pack.

4. select dependent variable as salary in Y range and select inependent variable of ERA, W,SO in X range

5. Populate the regression analysis report in excel

Is the hypothesis correct, why or why not?

As per the above regression report Significance F value is greater than 0.05

P value is 0.22 "greater than the common alpha level of 0.05, which indicates that it is not statistically significant"

so we Do not reject the null hypothesis