Question

Here is an example of T-test down blow (it doesn't have to be exactly) I need to help with my data which it is about phone service survey. Please help and I really appreciate your time. Remember first data is an example.

A third T-test was conducted to compare the relationship of how far people are willing to travel and how important the affordability of the clinic is (Table 3). The sample group was divided into two categories: people who aren’t willing to travel far (?15 miles) and people who are willing to travel far (?16 miles). It can be concluded that on average people that are willing to travel farther to visit a healthcare, don’t think the affordability of the clinic is as important as the people that aren’t willing to travel far (people that aren’t willing to travel far = 4.71, people that are willing to travel far = 4.11, p = .026). This finding indicates that healthcare consumers on average may be willing to travel farther to visit a specialty clinic or a more reputable clinic. An example of this could be someone from Minneapolis, MN traveling to Rochester, MN to visit Mayo Clinic, one of the most reputable clinics in the world. This data could also tell us that people who can’t afford to travel to a clinic farther away also care more about the affordability of the clinic

they are visiting.

Table 3. How Far People are Willing to Travel vs. Importance of Affordability

Group Statistics

	WillingTravel.re	N		Mean	Std. Deviation	Std. Error Mean
Affordability	1. People that   aren’t willing to travel far		21	4.7143	.71714 .93659	.15649
	2. People that are willing to travel far		19	4.1053		.21487

Independent Samples Test

		Levene's Test for Equality of Variances				t-test for Equality of Means
		F	Sig.	t	df	Sig. (2tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Affordability	Equal variances assumed Equal variances not assumed	1.101	.301	2.322	38	.026	.60902	.26228	.07806	1.13998
				2.291	33.642	.028	.60902	.26582	.06861	1.14944

Here is my data down blow and I need help with interpret or explain just like the example. Thank you so much!

Group Statistics
	Gender	N	Mean	Std. Deviation	Std. Error Mean
Cost per line	Male	17	2.9412	1.24853	.30281
	Female	18	2.1111	1.18266	.27876

Levene's Test for Equality of Variances					t-test for Equality of Means
		F	Sig.	T		df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95 % confidence interval of the difference
										Lower	Upper
Cost per line	Equal variances assumed	.451	.507	2.020		33	.052	.83007	.41093	-.00597	1.66610
	Equal variances assumed			2.017		32.584	.052	.83007	.41158	-.00771	1.66784

Answer 1

T-test was conducted to compare the relationship of Cost per line and gender. The sample group was divided into two categories: male and female. the average Cost per line for male (M=2.94, SD=1.24) is slightly greater than the average cost per line for female (M=2.1111, SD=1.18266).

ho: there is no significant difference in the variance cost per line between male and female. h1: there is significant difference in the variance cost per line between male and female. with (F=.451, P>5%), I FAIL to reject ho and conclude that there is no significant difference in the variance cost per line between male and female. and hence equality of variances is assumed.

ho: there is no significant difference in the mean cost per line between male and female. h1: there is a significant difference in the mean cost per line between male and female. with (t=2.020, P>5%), I FAIL to reject ho and conclude that there is no significant difference in the mean cost per line between male and female.