  The growing volume of data usually creates an interesting challenge for the
need of data analysis tools that discover regularities in these data. Data
mining has emerged as disciplines that contribute tools for data analysis,
discovery of hidden knowledge, and autonomous decision making in many
application domains. The purpose of this study is to compare the performance of
two data mining techniques viz., factor analysis and multiple linear regression
for different sample sizes on three unique sets of data. The performance of the
two data mining techniques is compared on following parameters like mean square
error (MSE), R-square, R-Square adjusted, condition number, root mean square
error(RMSE), number of variables included in the prediction model, modified
coefficient of efficiency, F-value, and test of normality. These parameters
have been computed using various data mining tools like SPSS, XLstat, Stata,
and MS-Excel. It is seen that for all the given dataset, factor analysis
outperform multiple linear regression. But the absolute value of prediction
accuracy varied between the three datasets indicating that the data
distribution and data characteristics play a major role in choosing the correct
prediction technique.
