Question

Aron
	1	2	3	4	Average
8/6/2017	90	138	118	105	112.8
8/19/2017	162	101	120	145	132
9/16/2017	101	129	132	111	118.3
Average	117.7	122.7	123.3	120.3	121

Mjorgan
	1	2	3	4	Average
8/6/2017	115	88	94	102	99.8
8/19/2017	89	75	77	90	82.8
9/16/2017	74	110	117	90	97.8
Average	92.7	91	96	94	93.4

Is there a correlation between the two bowlers themselves? In other words – does Aron’s score in each game depend on Mjorgan’s score at all? You might want to create a data table of each bowler’s score in each respective game to find out. Develop a statistical model to estimate Aron’s score based on Mjorgan’s score, then interpret its findings and its accuracy.

Answer 1

We first arrange the data in the below manner

Arons	Mjorgan
90	115
138	88
118	94
105	102
162	89
101	75
120	77
145	90
101	74
129	110
132	117
111	90

Using the corellation functon in excel =CORREL () we find the correlation coefficeint

The correlation coefficient = 0.01048

Using regression we find the below results

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.01048
R Square	0.00011
Adjusted R Square	-0.09988
Standard Error	22.03966
Observations	12
ANOVA
	df	SS	MS	F	Significance F
Regression	1	0.533571	0.533571	0.001098	0.974213
Residual	10	4857.466	485.7466
Total	11	4858
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	119.6154	42.25733	2.830644	0.017832	25.46024	213.7706	25.46024	213.7706269
Mjorgan	0.014821	0.447197	0.033143	0.974213	-0.98159	1.011238	-0.98159	1.011237779

Here we see that the significance F = 0.97 which is more than 0.05 hence the model is imsignificant

Using the model we can say Arons score = 119.61 + 0.014821 * Mjorgan

As the significance is not good the equations accuracy will we extremely poor