Question

A researcher analyzes the factors that may influence amusement park attendance. She estimates the following model: Attendance = β₀ + β₁Price + β₂Temperature + β₃Rides + ε, where Attendance is the daily attendance (in 1,000s), Price is the gate price (in $), Temperature is the average daily temperature (in ^oF), and Rides is the number of rides at the amusement park. A portion of the regression results is shown in the accompanying table.

	df	SS	MS	F	Significance F
Regression	3	29,524.41	9,841.47		2.18E-14
Residual	26	2,564.37	98.63
Total	29	32,088.78
	Coefficients	Standard Error	t-stat	p-value	Lower 95%	Upper 95%
Intercept	27.328	40.254		0.5032	−55.415	110.071
Price	−1.201	0.294		0.0004	−1.805	−0.598
Temperature	0.008	0.208		0.9693	-0.419	0.435
Rides	3.621	0.364		2.32E-10	2.874	4.369

When testing whether Temperature is significant at the 5% significance level, she ________.

Multiple Choice

• rejects H₀:β₂ = 0, and concludes that Temperature is significant

• does not reject H₀:β₂ = 0, and concludes that Temperature is significant

• rejects H₀:β₂ = 0, and cannot conclude that Temperature is significant

• does not reject H₀:β₂ = 0, and cannot conclude that Temperature is significant

Answer 1

Answer: does not reject H0:β2 = 0, and cannot conclude that Temperature is significant

Explanation:

Null Hypothesis, Ho: β2 = 0 i.e Temperature is not significant

Alternative Hypothesis, Ha: β2   0 i.e. Temperature is significant

Decision Rule: Reject the Null Hypothesis Ho if p-value <

Conclusion: Since p-value = 0.9693 > so we fail to Reject the Null Hypothesis and conclude that   β2 = 0 i.e Temperature is not significant