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Spam is the price we pay for being able to easily communicate by e-mail

Economics

Spam is the price we pay for being able to easily communicate by e-mail. Does spam affect everyone equally? In a preliminary study, university professors, administrators, and students were randomly sampled. Each person was asked to count the number of spam messages received that day. The results follow. Can we infer at the 2.5% significance level that the differing university communities differ in the amount of spam they receive in their e-mails? Professors Administrators Students 7 5 12 4 9 4 0 12 5 3 16 18 18 10 15

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Treatment 1 Treatment 2 Treatment 3
7
4
0
3
18
5
9
12
16
10
12
4
5
18
15

The ANOVA procedure tests these hypotheses:

H0: μ1 = μ2 = μ3, all the means are the same

H1: two or more means are different from the others

Calculating Mean and Standard deviation

  Treatments
1 2 3 Total
N 5 5 5 15
∑X 32 52 54 138
Mean 6.4 10.4 10.8 9.2
∑X2 398 606 734 1738
Std.Dev. 6.9498 4.0373 6.14 5.7842

ANOVA TABLE

SS df MS F
Between groups
(or “Factor”)
SSB = ∑njx?j²−Nx?² dfB = r−1 MSB = SSB/dfB F = MSB/MSW
Within groups
(or “Error”)*
SSW = ∑(nj−1)sj² dfW = Nr MSW = SSW/dfW  
Total* SStot = SSB + SSW dftot = N−1  

r = 3, N = 15

RESULTS -

Source SS df MS  
Between-treatments 59.2 2 29.6 F = 0.86804
Within-treatments 409.2 12 34.1  
Total 468.4 14    

Now, F tabulated at 0.25% level of significance = 5.0958671

Clearly, F calculated < F tabulated as 0.86 < 5.09

Thus, we don't have sufficient evidence to reject the null.

Thus, we conclude that amount of spam does not differ among the three university communities.