Basic Statistics and Pearson Correlation - Essay Example

In the experiment, it would be possible to observe how depression changed with significant changes in age and not vice versa because age was bound to increase regardless of the changes in depression. The Pearson correlation implies that when the data is plotted graphically the values will not fall into a straight line. A negative correlation implies that the likelihood of depression reduces with an increase in age.

A positive correlation would have been indicated by a straight line implying that as a person ages, there is an increase in the likelihood of individuals being depressed according to the data provided in book 5 (Weiss, 2006). In essence, an analysis of the data using SYSTAT implies that there is no significant correlation between age and depression. Age should therefore not be a factor when predicting the likelihood of an individual becoming depressed. Therefore, one can conclude that biological factors related to aging do not affect the capacity of individuals to deal effectively and healthily with depression.

The weak correlation implies that as one gets older perhaps due to experience and hardening they become less prone to depression. 2. Is there a correlation between work and depression? Pearson CorrelationNumber of Observations: 698Table 2Pearson Correlation Matrix WORKDEPRESSIONWORK1.000 DEPRESSION-0.1131.000Graph 2Analysis using SYSTAT indicates that the correlation between work and depression work and depression is -0.113. A correlation with a figure less than 1 indicates that there is no correlation between the two variables or the correlation could be very weak to be of any statistical significance.

In this case, one can comfortably conclude that there is no significant relationship between work and depression. The independent variable in this case was work and the dependent variable was depression. The correlation value in this case assumes a negative integer. The positive integer implies that there is a negative correlation between work and depression according to the data provided in Book 5 (Weiss, 2006). This implies that if the value of the integer was 1 then it would have been expected that the people without work were more likely to be depressed that the people who were working.

In essence, the data may be implying that although work is not a significant determinant of the prevalence of depression according to the data, it would have been expected that in cases where a relationship occurred, it would have been a negative relationship. The data can be explained in terms of socio-economic factors. People who are working and can earn a living are less likely to be depressed because they can satisfy most of their needs as compared to people who are not working and do not have any other source of income.