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Descriptive Statistics:
Carrying out an extensive analysis the data was not a subject to ambiguity and there were no missing values. Below are descriptive statistics that have been constructed after identifying no ambiguous or missing data for each variable that were included:
The 'N' in the descriptive statistics shows the total number of data items that is present which is 1519, however 'N*' reveals the number of items that are missing data which is 0.
The mean portrays central tendencies with the inclusion of all the data provided but the trimmed mean displays central tendency of data with 5 to 25 percent being discarded as it may include extreme data sets which can be relevant as it is less sensitive to outliers. Observing the mean and trimmed mean from the descriptive statistics for wfood, income, totexp and nk and age suggests they have a small change between their mean and trimmed mean which isn't highly significant.
The standard deviation measures the spread of data as well as the variance however the variance is the square root of standard deviation. Wfood and nk have low standard deviation and variance values; age has relatively high standard deviation and variance values where as on the other hand totexp and income has a high standard deviation and variance value. As there is a huge data sample many extreme data sets are established.
The coefficient of variation is the ratio of standard deviation to the mean which is a measure of dispersion of data of a variable. Wfood, totexp, income, age and nk have relatively high coefficient of variance which indicate that data is relatively highly dispersed from income being the most dispersed to age being the least dispersed.
Applications of Standard Error Standard Error is used to test whether the difference between the sample statistic and the population parameter is significant or is d
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