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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.
Simple Linear Regression One calculate of the risk or volatility of an individual stock is the standard deviation of the total return (capital appreciation plus dividends) over
how to interpret results, a good explanation to help me understand.
Classification of Universe The universe may be classified either on the basis of number of units and on the basis of existence of units as is clear from the following chart :
The Null Hypothesis - H0: The random errors will be normally distributed The Alternative Hypothesis - H1: The random errors are not normally distributed Reject H0: when P-v
X 110 120 130 120 140 135 155 160 165 155 Y 12 18 20 15 25 30 35 20 25 10
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