Question

Ans via excel using data analysis toolpak explaining which function you used and why

13a. Do goals by appearance (G/A) and Age predict players' Trading Value? Please run the appropriate test and report your findings.

13b. If we have a new player to this dataset with a G/A ratio of .75 who is 26 years old, how much do we predict her/his Trading Value to be?

			Percentage playing time by year
Player #	Year	Trading Value (Millions of $)	Y-5	Y-4	Y-3	Y-2	Y-1	Goals	Appearances	Goals by Appearance (G/A)	Age	Foot	Pos
1	2015	21.90	0.900	0.900	0.900	0.964	0.769	25	128	0.19531	24	L	MID
2	2015	22.00	0.912	0.912	1.000	0.882	0.938	45	156	0.28846	24	R	MID
3	2015	23.30	0.237	0.868	0.947	0.842	0.850	23	127	0.18110	27	R	MID
4	2015	23.40	0.833	0.846	0.813	0.842	0.921	11	126	0.08730	21	R	MID
5	2015	23.40	0.711	1.000	0.711	0.667	0.921	38	128	0.29688	21	L	MID
6	2015	23.90	0.972	0.714	1.000	0.921	0.947	82	152	0.53947	28	R	MID
7	2015	24.70	0.412	0.912	1.000	1.000	0.967	86	134	0.64179	28	B	MID

Answer 1

The regression output for Players trading value as dependent variable and Goals by Appearance (G/A), Age as independent variables is shown below in the attached image

The regression equation is

Players Trading value (in $ millions) = 2.437676 * Goals by Appearance + Age * 0.013694 + 22.283146

But when you look at the p-values for Goals by appearance and Age variables, the p-value for Goals by Appearance is 0.417856 and p-value for Age is 0.939611 which are way more than 0.05 (the value)

since the p-values for both the independent variables Age and Goals by Appearance are more than 0,05, they are not statistically significant, meaning only Age and Goals by Appearance can't predict the Players Trading value

Question 13b

Given Goals by Appearance = 0.75 and Age is 26

Players Trading value (in $ millions) = 2.437676 * Goals by Appearance + Age * 0.013694 + 22.283146

= 2.437676 * 0.75 + 26 * 0.013694 + 22.283146

= 1.828258 + 0.356051 + 22.283146

= 24.46746

= 24.47 rounded to two decimal places