Correlation, Simple Linear Regression
In this paper, we perform a linear regression analysis on previously collected data related to the number of daily e-mails received (R) and sent (S) by a particular user (the author). We have depicted the original daily e-mail data as a time series, incrementing N. By 1 for each day's measurement. The computed regression coefficient r is the slope of the regression line.
A time series consists of sequencing successive data points at uniform time intervals. As such, this exercise represents a meaningful statistical analysis to determine whether a natural temporal ordering is inherent in the data. It should be recalled that the collection of data for each of R. And S. consisted of 15 daily samples, which were collected during two exercises spanning 10 and 5 days respectively. This factor will be noted in the analysis to follow. Table 1 illustrates the predicted values and subsequent regression analysis for the e-mails received (R). Table 2 illustrates the predicted values and subsequent regression analysis for the e-mails sent (S).
This section summarizes the regression analysis for e-mails received (R).
Time Series (N)
E-mails Received (R)
Predicted Value
1
2.581
2
0.147
3
6.098
4
2.311
5
97
6.909
6
72
10.290
7
81
9.073
8
77
9.614
9
87
8.261
10
93
7.450
11
56
12.454
12
67
10.966
13
70
10.561
14
61
11.778
15
63
11.507
Table 1: Predicted Values for R.
Based on the online analysis using the tools by Waner et. al. (1999), the linear regression equation,...
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