Here is an example of T-test down blow (it doesn\'t have to be exactly) I need t
ID: 2907734 • 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.
T-test looked at how far people travel to visit a healthcare clinic compared to how easy it was to understand the information that their physician was explaining to them (Table 2). The sample group was divided into two categories: people who travel less (?5 miles) and people who travel more (?6 miles). Table 2 shows that, on average, people who travel less understood more information that their physician was explaining to them than the people that traveled more (people who travel less = 4.63, people who travel more = 4.10, p = .026). The conclusion that could be drawn from this finding is that physicians who work in clinics close to dense populations are better at explaining information to their patients. This could be due to these physicians seeing more patients with similar conditions, making it easier for them to explain information to their patients with similar conditions.
Table 2. Distanced Normally Traveled vs. How Easy Information was Explained by Physician
Group Statistics
NormTravel.re
N
Mean
Std. Deviation
Std. Error Mean
Information
People who travel less
People who travel more
19
4.6316
.49559
.11370
.19401
21
4.0952
.88909
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
Information Equal variances assumed
Equal variances not assumed
3.098
.086
2.322
38
31.914
.026
.023
.53634
.23102
.06866
.07824
1.00402
2.385
.53634
.22488
.99444
Here is my data down blow and I need help with interpret or explain just like the exmpale. Thank you so much!
Group Statistics
Gender
N
Mean
Std. Deviation
Std. Error Mean
Most important features
Male
17
2.8824
.60025
.14558
Female
18
2.8889
.58298
.13741
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
Most important features
Equal variances assumed
.203
.655
-.033
33
.974
-.00654
.20002
-.41347
.40040
Equal variances assumed
-.033
32.746
.974
-.00654
.20019
-.41394
.40087
NormTravel.re
N
Mean
Std. Deviation
Std. Error Mean
Information
People who travel less
People who travel more
19
4.6316
.49559
.11370
.19401
21
4.0952
.88909
Explanation / Answer
T-test: t-test is used to compare the mean effect or average effect of two variable.
If the variable is more than two then use the ANOVA.
Testing of Hypothesis:
H0: The average taking phone service from male and female is the same.
against,
H1: The average taking phone service from male and female is different.
Test Statistics:
we want to use paired t-test here,
T = ¯d / SE( ¯d)
SE( ¯d) = Sd / ? n
with n-1 degrees of freedom.
d = difference between male and female data
Sd = standared deviation
Decision Rule:
If the p-value is greater than 0.05 then we accept H0 at 5 % level of significance.
i.e. here in example, the p-value is 0.655 (significance value)
which is greater than 0.05 level of significance.
i.e. accept null hypothesis here
i.e. The average taking phone service from male and female is the same.
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