Report on the Usage of Computers on the Ground Floor of UOW Library

Report on the usage of computers on the ground floor of the UOW library
1. Data collection
The observation of computer usage on the ground floor of UOW library. The data is an observation of the
number of computers occupied at a given time in the library. The times are morning, afternoon and in the
evening as shown in the table below.
S/NO. Morning Afternoon Evening
1. 61 18 46
2. 66 17 51
3. 62 19 47
4. 70 21 55
5. 65 16 50
6. 69 26 54
7. 72 21 57
8. 66 19 51
9. 65 20 50
10. 64 20 49
11. 67 25 52
12. 68 70 53
13. 65 35 50
14. 66 20 51
15. 71 65 56
16. 64 24 49
17. 59 23 44
18. 58 22 43
19. 64 65 49
20. 64 30 49
2. Hypothesis
H0: there is no significance difference on the computer usage at different times of the day
H1: there is significance difference on computer usage at different time of the day
The following gives the output from the R. The data was imported into R by calling the command
data<-read.csv("C:\\Users\\George\\Desktop\\R.csv",header=T), and then attaching the data. To conduct
the different student t-tests between morning and afternoon, morning and evening and afternoon and
evening we used this command in R;
attach(data)
t.test(Morning,Afternoon)
t.test(Morning,Evening)
t.test(Afternoon,Evening)

And the following output followed between computer usage in the morning and afternoon
> t.test(Morning,Afternoon)
Welch Two Sample t-test
data: Morning and Afternoon
t = 9.4214, df = 20.769, p-value = 6.031e-09
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
28.43782 44.56218
sample estimates:
mean of x mean of y
65.3 28.8
From the above output, we find that the t-value is 9.4214.
From the tables we find that at α =0.05∧df =20 , t α
2
=1.725
Since the calculated t>tabulated t we then reject the null hypothesis and conclude that there is significance
difference in mean of computer usage in the morning and afternoon.
> t.test(Morning,Evening)
And the following output followed between computer usage in the morning and evening
Welch Two Sample t-test
data: Morning and Evening
t = 12.968, df = 38, p-value = 1.572e-15
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
12.65844 17.34156
sample estimates:
mean of x mean of y
65.3 50.3

From the above output, we find that the t-value is 12.968.
From the tables we find that at α =0.05∧df =20 , t α
2
=1.725
Since the calculated t>tabulated t we then reject the null hypothesis and conclude that there is significance
difference in mean of computer usage in the morning and eveining.
> t.test(Afternoon,Evening)
And the following output followed between computer usage in the afternoon and evening
Welch Two Sample t-test
data: Afternoon and Evening
t = -5.5496, df = 20.769, p-value = 1.723e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-29.56218 -13.43782
sample estimates:
mean of x mean of y
28.8 50.3
From the above output, we find that the t-value is -5.5496.
From the tables we find that at α =0.05∧df =20 , t α
2
=1.725
Since the calculated t<tabulated t we then fail to reject the null hypothesis and conclude that there is no
significance difference in mean of computer usage afternoon and evening.
I used excel as another software in analyzing the data and the following are the outputs from different
comparison. I used the t two sample tests to establish the statistical difference between the times in which
different number of computer is occupied.