David,
I am having difficulties understanding when to use one tailed t test, and when to use two tailed. For Eg: In a practice question, 2009-Gujarati-5-8.pdf, question 7.07c, Ŷt = 5807 + 3.24Xt r² = 0.22 se = (1.634),
Compute the t value under H0:B2 = 0. Is it statistically significant at the 5 percent level? Which t test do you use, one tailed or two-tailed, and why?
Answer: t = 3.24 / 1.634 = 1.9829. Since car sales are expected to be positively related to real disposable income, the null and alternative hypotheses should be: H0 : B2 ≤ 0 and H1 : B2 > 0. Therefore, an one-tailed t test is appropriate in this case. The 5% one-tailed t value for 14 d.f. is 1.761. Since the computed t value of 1.9829 exceeds the critical value, reject the null hypothesis (one- and two tail tests sometimes give different results).
I dont quite understand this.
Also, please help me understand why d.f is k-1 at times, at k at times. I still dont seem to understand that part clearly enough.
Regards,
Arun
I am having difficulties understanding when to use one tailed t test, and when to use two tailed. For Eg: In a practice question, 2009-Gujarati-5-8.pdf, question 7.07c, Ŷt = 5807 + 3.24Xt r² = 0.22 se = (1.634),
Compute the t value under H0:B2 = 0. Is it statistically significant at the 5 percent level? Which t test do you use, one tailed or two-tailed, and why?
Answer: t = 3.24 / 1.634 = 1.9829. Since car sales are expected to be positively related to real disposable income, the null and alternative hypotheses should be: H0 : B2 ≤ 0 and H1 : B2 > 0. Therefore, an one-tailed t test is appropriate in this case. The 5% one-tailed t value for 14 d.f. is 1.761. Since the computed t value of 1.9829 exceeds the critical value, reject the null hypothesis (one- and two tail tests sometimes give different results).
I dont quite understand this.
Also, please help me understand why d.f is k-1 at times, at k at times. I still dont seem to understand that part clearly enough.
Regards,
Arun