Question

A statistical program is recommended.

Data showing the values of several pitching statistics for a random sample of 20 pitchers from the American League of Major League Baseball is provided.

Player	Team	W	L	ERA	SO/IP	HR/IP	R/IP
Verlander, J	DET	24	5	2.40	1.00	0.10	0.29
Beckett, J	BOS	13	7	2.89	0.91	0.11	0.34
Wilson, C	TEX	16	7	2.94	0.92	0.07	0.40
Sabathia, C	NYY	19	8	3.00	0.97	0.07	0.37
Haren, D	LAA	16	10	3.17	0.81	0.08	0.38
McCarthy, B	OAK	9	9	3.32	0.72	0.06	0.43
Santana, E	LAA	11	12	3.38	0.78	0.11	0.42
Lester, J	BOS	15	9	3.47	0.95	0.10	0.40
Hernandez, F	SEA	14	14	3.47	0.95	0.08	0.42
Buehrle, M	CWS	13	9	3.59	0.53	0.10	0.45
Pineda, M	SEA	9	10	3.74	1.01	0.11	0.44
Colon, B	NYY	8	10	4.00	0.82	0.13	0.52
Tomlin, J	CLE	12	7	4.25	0.54	0.15	0.48
Pavano, C	MIN	9	13	4.30	0.46	0.10	0.55
Danks, J	CWS	8	12	4.33	0.79	0.11	0.52
Guthrie, J	BAL	9	17	4.33	0.63	0.13	0.54
Lewis, C	TEX	14	10	4.40	0.84	0.17	0.51
Scherzer, M	DET	15	9	4.43	0.89	0.15	0.52
Davis, W	TB	11	10	4.45	0.57	0.13	0.52
Porcello, R	DET	14	9	4.75	0.57	0.10	0.57

An estimated regression equation was developed to predict the average number of runs given up per inning pitched (R/IP) given the average number of strikeouts per inning pitched (SO/IP) and the average number of home runs per inning pitched (HR/IP).

R/IP = 0.5365 - 0.2483 SO/IP + 1.032 HR/IP

(a)

Use the F test to determine the overall significance of the relationship.

State the null and alternative hypotheses.

H₀: β₀ = 0
H_a: β₀ ≠ 0H₀: β₀ ≠ 0
H_a: β₀ = 0    H₀: β₁ = β₂ = 0
H_a: One or more of the parameters is not equal to zero.H₀: One or more of the parameters is not equal to zero.
H_a: β₁ = β₂ = 0H₀: β₁ = β₂ = 0
H_a: All the parameters are not equal to zero.

Calculate the test statistic. (Round your answer to two decimal places.)

Calculate the p-value. (Round your answer to three decimal places.)

p-value =

What is your conclusion at the 0.05 level of significance?

Reject H₀. There is sufficient evidence to conclude that there is a significant overall relationship.Do not reject H₀. There is sufficient evidence to conclude that there is a significant overall relationship.    Do not reject H₀. There is insufficient evidence to conclude that there is a significant overall relationship.Reject H₀. There is insufficient evidence to conclude that there is a significant overall relationship.

(b)

Use the t test to determine the significance of SO/IP.

State the null and alternative hypotheses.

H₀: β₁ = 0
H_a: β₁ ≠ 0H₀: β₁ = 0
H_a: β₁ > 0    H₀: β₁ ≠ 0
H_a: β₁ = 0H₀: β₁ ≥ 0
H_a: β₁ < 0H₀: β₁ ≤ 0
H_a: β₁ > 0

Find the value of the test statistic for β₁. (Round your answer to two decimal places.)

Find the p-value for β₁. (Round your answer to three decimal places.)

p-value =

What is your conclusion at the 0.05 level of significance?

Reject H₀. There is sufficient evidence to conclude that SO/IP is a significant factor.Reject H₀. There is insufficient evidence to conclude that SO/IP is a significant factor.    Do not reject H₀. There is insufficient evidence to conclude that SO/IP is a significant factor.Do not reject H₀. There is sufficient evidence to conclude that SO/IP is a significant factor.

Use the t test to determine the significance of HR/IP.

State the null and alternative hypotheses.

H₀: β₂ ≤ 0
H_a: β₂ > 0H₀: β₂ = 0
H_a: β₂ ≠ 0    H₀: β₂ ≠ 0
H_a: β₂ = 0H₀: β₂ = 0
H_a: β₂ > 0H₀: β₂ ≥ 0
H_a: β₂ < 0

Find the value of the test statistic for β₂. (Round your answer to two decimal places.)

Find the p-value for β₂. (Round your answer to three decimal places.)

p-value =

What is your conclusion at the 0.05 level of significance?

Do not reject H₀. There is sufficient evidence to conclude that HR/IP is a significant factor.Reject H₀. There is sufficient evidence to conclude that HR/IP is a significant factor.    Reject H₀. There is insufficient evidence to conclude that HR/IP is a significant factor.Do not reject H₀. There is insufficient evidence to conclude that HR/IP is a significant factor.

Answer 1

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.750641904
R Square	0.563463268
Adjusted R Square	0.512106005
Standard Error	0.053785
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	2	0.063476954	0.031738	10.97144	0.000871318
Residual	17	0.049178046	0.002893
Total	19	0.112655
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	0.536507528	0.081408539	6.59031	4.59E-06	0.364750525	0.708264531
SO/IP	-0.248347526	0.071806578	-3.45856	0.003002	-0.399846162	-0.096848889
HR/IP	1.03193134	0.435884204	2.367444	0.030037	0.112296056	1.951566623