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According to professor Jokela, psychologists can know the social functioning of a person only by assessing their Personality traits. However, empirical studies have been focused on building linear regressions between only one facet of personality and Life Satisfaction, Altruism and Health accordingly; also, the accuracy of the prediction remained debatable. In practice, scales online help researchers to get data measurements of participants’ information needed in the study. Gradient descent works by building the optimized multiple linear regression to model the relationship of a lot of inputs and a single output; python programs enable researchers to test the accuracy of the predicted output of the regressions. The data was from a preparing study by another group of graduated students from Cambridge University, and it contained information of 1769 participants. By splitting the sample into testing sample (33%) and training sample (67%), three multiple linear regressions were built to model the relationship between 120 Personality items and an average Life Satisfaction score, Altruism score and Health score using the training sample; then, the accuracy of the models was tested using the testing sample. According to the small p-values of correlation between the y-reported and y-predicted for all the three predictions, the probability of getting extreme values was very small, which ensured the reliability of these prediction. According to Cohen’s conventions about effect size of correlation in Psychology and another authorized peer research, the Pearson-correlation value of Personality &amp; Life Satisfaction regression shows a very high accuracy of using Personality to predict Life Satisfaction; also, the correlation values for Personality &amp; Altruism and Personality &amp; Health are also above moderate, which indicate nice and acceptable predictability for two regressions.

A Mental Examination---Using Personality to predict Happiness, Altruism and Health