Probability Model

The Probability Model is an appropriate modeling tool that is able to specify the possible outcomes for a sample space, and provide assumptions which are based on the calculation of probabilities for events composed of those outcomes (Agresti and Franklin, 2006). In a differentiated environment, the probability that a stimulus which has not been seen before will be correctly recognized and associated to its appropriate class (the probability of correct generalization) approaches the same configuration as the probability of a correct response to the previously reinforced stimulus (Rosenblatt, 1958).

Such a procedure amounts to a process of curve fitting and extrapolation, in the hope that the constants which describe one set of curves will hold good for other curves in similar

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situations. A Probability Model, being derived from basic physical variables, is not specific to any one particular situation. In principle, it can be generalized to cover any form of behavior in any system for which the physical parameters are known. A Probability Model constructed on this foundation should be considerably more powerful than the other two which have been previously proposed (Lancet, Sadovsky &

Seidemann, 1993). In this research, it will not only explain what behavior may occur in satisfied residents in a given property, but it becomes increasingly qualitative as they are generalized. In this specific case, a Probability Model can provide a description of the relation between two variables, X and Y, that are not deterministically related (Peck, et al., 2001). More generally, the proposed model provides a better understanding of the relationship between an average employee engagement score and the number of satisfied residents. Knowledge of the distribution of satisfied residents against employee engagement scores may help answer some basic questions related to satisfied residents.

One important question is: “At what point does an employee have to be engaged to ensure at least 70% of the satisfied residents”?

8.3.1 Methodology

In order to construct a probability model, a threshold of resident satisfaction is set at 4 on a scale from 1 to 5; in this scale, 1 to 3 is not satisfied and any rating of 4 and above is satisfied. Then, the question of interest is: “What is the distribution of average satisfied residents’ satisfaction-score of a multi-family rental property for a particular value of average employee engagement-score X (on scale of 1-5)?” To answer this question, the concept of a probability distribution is used to determine the probability distribution function for the survey data. The probability distribution function (PDF) for a

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discrete random variable (average employee engagement score X in this case) is a function that assigns a probability to each value of the random variable. The probability that the random variable X assumes for any specific value xi is the value of the PDF for xi

and is denoted Px (xi). Collectively, these discrete values xi of X along with their associated probabilities constitute the probability distribution function (Wardrop, 1995).

It satisfies the following conditions:

(i) 0 ൑ Px (xi) ൑ 1 (8.1)

(ii) P_x (x_i) = 1 (8.2)

In which (i = 1, 2, 3, 4, 5)

In order to determine the probability distribution function (PDF), the Histogram Interpretation method is used (Sullivan, 2004). Probability histograms are constructed like relative frequency histograms, except the vertical axis represents the probability of a value of the random variable rather than its relative frequency (Sullivan, 2004). The most common form of the histogram is obtained by splitting the range of the data into equal-sized bins (called classes). Then, for each bin, the number of points from the data set that fall into each bin is counted. That is:

• Vertical axis: Frequency (i.e., counts for each bin) or the percentage values of occurrence.

• Horizontal axis: Response variable.

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Figure 8.1: A Histogram of Left-Skewed Distribution (Survey data for Q54)

Once a histogram has been developed, its nature can then be studied to fit a probability distribution to the data set. As indicated in Figure 8.1, the distribution for Q54 is not symmetric and is left-skewed. A symmetric distribution is one in which the

“two halves" of the histogram appear as mirror-images of one another. A skewed (non-symmetric) distribution is a distribution in which there is no such mirror-imaging. For skewed distributions, it is quite common to have one tail of the distribution considerably longer or drawn out relative to the other tail. A "skewed right" distribution is one in which the tail is on the right side. A "skewed left" distribution is one in which the tail is on the left side. The histogram above is for a distribution that is skewed left (Wardrop, 1995). Distribution of satisfied-residents (avg satisfaction-score > 4) 0.48

0.44

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Skewed data often occur due to lower or upper bounds on the data. That is, data that have a lower bound are often skewed right, while data that have an upper bound are often skewed left. Skewness can also result from start-up effects. For example, in reliability applications, some processes may have a large number of initial failures that could cause left skewness. On the other hand, a reliability process could have a long start-up period where failures are rare, resulting in right-skewed data.

Once the data distribution has been obtained, it is necessary to determine the best-fit distribution by trying different commonly known skewed distributions, like Weibull, Gamma, Chi-square and Burr, depending on the shape of the distribution (Peck, et al., 2001). Using statistical analysis software, EasyFit, to analyze the shape, it is concluded that the Three-Parameter Burr Distribution is the most appropriate for fitting the data (Mathwave Technologies, 2009).

8.3.2 Three-Parameter Burr Distribution

The three-parameter Burr type distribution was first introduced in the statistical literature by Burr (1942), and has gained special attention in the past two decades due the importance of using it in practical situations. It has been applied in the areas of reliability studies and failure time modeling (Abd-Elfattah and Assar, 2005). Burr distribution is a continuous probability distribution for a non-negative random variable. It is also known as the Singh-Maddala distribution and is one of a number of different distributions sometimes called the "generalized log-logistic distribution" (Maddala, 1996). The Burr distribution has a flexible shape and controllable scale and location which makes it appealing to fit to data. It is frequently used to model insurance claim sizes and

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household income (Tadikamalla, 1980). The parameters that determine the nature and shape of Burr distribution are as follows.

8.3.3 Parameters

A shape parameter is any parameter of a probability distribution that is neither a location parameter nor a scale parameter. Such a parameter must affect the shape of a distribution, rather than simply shifting it (as a location parameter does) or stretching/shrinking it (as a scale parameter does). A scale parameter determines the spread of a probability distribution. The larger the scale parameter, the more spread out the distribution. The location parameter determines where the origin will be located. The Three-Parameter Burr Distribution takes into account three parameters (two shape parameters and one scale parameter) to form a distribution function. The location parameter ߛ is zero for a Three-Parameter Burr Distribution. The parameters of the Burr distribution are as follows:

ߢ- continuous shape parameter ሺߢ ൐ 0) ߙ- continuous shape parameter ሺߙ ൐ 0) ߚ- continuous scale parameter (ߚ ൐ 0)

ߛ- continuous location parameter (ߛ ൌ 0 yields the three-parameter Burr distribution)

The domain is defined as:

ߛ ൑ ݔ ൑ ൅∞ (8.3)

140 8.3.4 Probability Density Function

The concept of a population as a smooth curve is needed as a mathematical device to prove many of the results that can be used to obtain probabilities. The smooth curve corresponding to a population is called its probability density function and can be written as follows (Wardrop, 1995):

݂ሺݔሻ ൌ ^ఈ఑ቀ

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ഁቁ^ഀషభ ఉ൬ଵାቀ_ഁ^ೣቁ^ഀ൰^ഉశభ