Here is an example of T-test down blow (it doesn\'t have to be exactly) I need t
ID: 2907732 • Letter: H
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
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
Explanation / Answer
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.
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