Capstone 230

Capstone 230 Personality is a result of a person's genetics as well as their environment. Psychologist Donald Hebb once answered a journalist's question of "which, nature or nurture, contributes more to personality?" by asking in response, "which contributes more to the area of a rectangle, its length or its width?" The text uses the example of both the musician and the instrument contributing to music. It doesn't matter which contributes more, nor is it possible to really measure. There are aspects to a person's personality that are passed down from parent to child. There is a field dedicated to studying the genetic link to personality traits and behavior called behavioral genetics. In the 1930's, the theory that personality could be determined by one's blood type was popular in Japan. The process of "blood typing" is sometimes still used for hiring practices. In humans, a number of genetically based personality traits have been identified.Center for Advanced PsychologyWe know that mental illnes s and alcoholism have genetic links.There are also aspects of personality that are shaped by a person's environment. The behavioral disposition theory is used to predict how a person will react in a particular environment. When a person consistently reacts the same way in a situation, they are exhibiting personality traits based on that environment. American psychologist John Watson demonstrated that the acquisition of a phobia could be explained by classical conditioning. He said, "Give me a dozen healthy infants, well-formed, and my own specified world to bring them up in and I'll guarantee to take any one at random and train him to become any type of specialist I might select...regardless of his talents, penchants, tendencies, abilities, vocations and race of his ancestors." Genetics and environment are not the only two factors that contribute to personality, however. The experiences a person has can...

How and When to Use Uniform Distribution

How and When to Use Uniform Distribution There are a number of different probability distributions. Each of these distributions has a specific application and use that is appropriate to a particular setting. These distributions range from the ever-familiar bell curve (aka a normal distribution) to lesser-known distributions, such as the gamma distribution. Most distributions involve a complicated density curve, but there are some that do not. One of the simplest density curves is for a uniform probability distribution. Features of the Uniform Distribution The uniform distribution gets its name from the fact that the probabilities for all outcomes are the same. Unlike a normal distribution with a hump in the middle or a chi-square distribution, a uniform distribution has no mode. Instead, every outcome is equally likely to occur. Unlike a chi-square distribution, there is no skewness to a uniform distribution. As a result, the mean and median coincide. Since every outcome in a uniform distribution occurs with the same relative frequency, the resulting shape of the distribution is that of a rectangle. Uniform Distribution for Discrete Random Variables Any situation in which every outcome in a sample space is equally likely will use a uniform distribution. One example of this in a discrete case is rolling a single standard die. There are a total of six sides of the die, and each side has the same probability of being rolled face up. The probability histogram for this distribution is rectangular shaped, with six bars that each have a height of 1/6. Uniform Distribution for Continuous Random Variables For an example of a uniform distribution in a continuous setting, consider an idealized random number generator. This will truly generate a random number from a specified range of values. So if it is specified that the generator is to produce a random number between 1 and 4, then 3.25, 3, e, 2.222222, 3.4545456 and pi are all possible numbers that are equally likely to be produced. Since the total area enclosed by a density curve must be 1, which corresponds to 100 percent, it is straightforward to determine the density curve for our random number generator. If the number is from the range a to b, then this corresponds to an interval of length b - a. In order to have an area of one, the height would have to be 1/(b - a). For example, for a random number generated from 1 to 4, the height of the density curve would be 1/3. Probabilities With a Uniform Density Curve It is important to remember that the height of a curve does not directly indicate the probability of an outcome. Rather, as with any density curve, probabilities are determined by the areas under the curve. Since a uniform distribution is shaped like a rectangle, the probabilities are very easy to determine. Rather than using calculus to find the area under a curve, simply use some basic geometry. Remember that the area of a rectangle is its base multiplied by its height. Return to the same example from earlier. In this example, X is a random number generated between the values 1 and 4. The probability that X is between 1 and 3 is 2/3 because this constitutes the area under the curve between 1 and 3.