Amol Musale said:which job will requires less processing time??
Amol Musale said:I am working on simio and i have to decide job priority according to their processing time. smaller the processing time, higher the priority. I don't know how to compare between normal and uniform distribution.
A normal distribution is a statistical concept that describes the frequency distribution of a set of data. It is also known as a Gaussian distribution or bell curve because of its characteristic shape. In a normal distribution, the majority of the data is clustered around the mean, with equal amounts of data on either side. This results in a symmetrical distribution, with the mean, median, and mode all being equal.
There are several properties of a normal distribution that make it a useful concept in statistics. These include a symmetrical shape, with the mean, median, and mode all being equal; a large percentage of data falling within one standard deviation of the mean; and a predictable relationship between the mean and standard deviation.
The main difference between a normal distribution and a uniform distribution is the shape of their frequency distributions. A normal distribution is bell-shaped, with most of the data clustered around the mean. In contrast, a uniform distribution is flat and rectangular, with an equal probability of occurrence for each value in the data set. Additionally, a normal distribution can take on any value on the real number line, while a uniform distribution is limited to a specific range of values.
A normal distribution is used in many real-life applications, including finance, biology, and psychology. For example, in finance, stock prices often follow a normal distribution, which allows investors to make predictions about future price movements. In biology, the height of a population is often normally distributed, and this information can be used to study evolutionary processes. In psychology, scores on intelligence tests often follow a normal distribution, which allows researchers to make comparisons and draw conclusions about the population.
One common misconception about a normal distribution is that all data should conform to this shape. In reality, many real-life data sets do not perfectly fit a normal distribution, and it is only used as an approximation for easier analysis. Another misconception is that the mean and median will always be equal in a normal distribution. While this is true for a perfectly symmetrical distribution, it may not hold true for skewed distributions. Lastly, it is important to remember that a normal distribution does not imply that the data is "normal" or "average" in any way; it simply describes the shape of the frequency distribution.