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# Data Analysis - Essay Example

Summary
Gender is a discrete variable which can take on either male or female values. It has a non-parametric nominal level of measurement because its value represents a category of objects where there is no measured difference between its values (Black 2006, p.8).

## Extract of sample"Data Analysis"

Download file to see previous pages Blood pressure is a discrete variable where its value can only take on certain values. Its level of measurement is parametric interval because its values can be ordered and the distance between different values has a meaning (Black 2006, p.9).
The height variable is a continuous variable where its value can be set to any value. It has an interval level of measurement as its different values are ordered and the difference between different values has a meaning (Black 2006, p.9).
Temperature is a continuous variable because it can be equal to any real value. It has a parametric interval level of measurement since its values can be ordered and difference between different variable has a meaning. The zero value of temperature does not mean an absolute null value.
The Satisfaction rating variable is a discrete variable since its value can only be set to specific values. It has a non-parametric ordinal level of data because its values are categories which can be ordered (Black 2006, p.8).
Employment status is a discrete variable since it can be set to only one of two possible values. It has a non-parametric nominal level of data since its values are categories which can not be ordered (Black 2006, p.8).
Examples of ordinal data that occur in categories but can be ordered are: heart murmurs grades I (heard only with special effort) to VI (audible with the stethoscope off the chest), the risk of birth defects from drugs during pregnancy as graded by the U.S. Food and Drug Administration on a 5-point scale ranging from "controlled studies show no risk", "no evidence of risk in humans", "risk cannot be ruled out", "positive evidence of risk", to "contraindicated in pregnancy" (Fletcher & Fletcher 2005, p.19).
Examples of Interval data that has numerical values which can be ordered and their differences can be ordered too are: blood glucose reading, measurement of patient temperature, number of migraine attacks a patient had per weak.
Examples of ratio data that has same characteristics like interval data in addition to the value of zero refers to null value are: percentage of doctors available in the evening shift and number of temperature measures conducted during one day.
1. Normal Distribution Curve:
The data is symmetric and equally divided to the left and right of the mean of the data. The mean, mode, and medial descriptive values of the data all lay on the center line that splits the data in two halves. The area under the curve is equal to 1 representing the probability of any value to exist. The further away from the center or mean value, the less the probability of the value to occur. 68% of the data is located at a distance equal to the standard deviation from the mean. 95% of the data is located at a distance of two times the standard deviation from the mean (Black 2006, p.61). The graph of the normal distributed data is a bell-shape curve as shown in figure 1.

Figure 1: Normal Distribution Curve Obtained from (Black 2006, p.76).

2. Positively Skewed Distribution Curve:
The positively skewed distribution curve is more weight to the left than to the right. This is because more data exist to the left than to the right of the mean of the population. The mode and median exist to the left of the mean too as shown in figure 2.

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