Having successfully completed your first year in university, you began your second year with an evaluation of your past performance. You observed that you performed well in those subjects where you were diligent with class attendance whilst you performed poorly in those courses where you missed a number of classes. Upon learning about OLS regression you realize that you are able to predict your average performance based on the number of classes attended. The table below shows your data set. Number of Lectures (X) Percentage Scored (Y) 1 30 2 45 3 51 4 57 5 60 6 65 7 70 8 71 9 72 10 73 11 66 12 71 13 47 14 81 15 83 16 84 17 89 18 99 19 82 20 86
Having successfully completed your first year in university, you began your second year
with an evaluation of your past performance. You observed that you performed well in
those subjects where you were diligent with class attendance whilst you performed poorly
in those courses where you missed a number of classes. Upon learning about OLS
regression you realize that you are able to predict your average performance based on
the number of classes attended. The table below shows your data set.
Number of Lectures (X) Percentage Scored (Y)
1 30
2 45
3 51
4 57
5 60
6 65
7 70
8 71
9 72
10 73
11 66
12 71
13 47
14 81
15 83
16 84
17 89
18 99
19 82
20 86
OLS is utilized to anticipate the upsides of a ceaseless reaction variable utilizing at least one illustrative factor and can likewise distinguish the strength of the connections between these factors (these two objectives of relapse are frequently alluded to as forecast and clarification). The t measurement is the coefficient separated by its standard blunder. It tends to be considered as a proportion of the accuracy with which the relapse coefficient is estimated. In the event that a coefficient is enormous contrasted with its standard mistake, it is presumably not the same as 0.
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