Answer

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   Kurtosis

   It is universally acknowledged in research that satisfactory knowledge of” measures of central tendency, dispersion and skewness” is insufficient for understanding distribution of data. Consequently, in addition to the aforestated variables, it is necessary to deploy a different measure to gain a clearer understanding of the outline of the distribution. This measure is known as Kurtosis. This measure, popularly ascribed to Prof. Karl Pearson has on various occasions been referred to as “Convexity of a Curve” (Rashinkar, 2020). Essentially, Kurtosis adds statistical logic by giving a measure of the extent of flatness of a distribution.

   A study by Rashinkar (2020) defines Kurtosis as the extent of “flatness or peakedness” around the modal value of the frequency distribution. In situations where most scores are around the region of the central tendency values, the frequency distribution will be comparatively constricted in width. The extent of Kurtosis is determined with respect to “the peakedness” of a normal distribution graph.

   Stages/ Categories of Kurtosis

   Three categories of Kurtosis can be defined from extant literature: Mesokurtic, Leptokurtic, and platykurtic (Walker & Maddan, 2019).

   Mesokurtic Distribution

   This also referred to as the Normal Distribution and is regarded in scholarly circles as “standard”. Inferentially, this distribution is characterized by peaks which are congruent with those from normal curves. In other words, data that conforms to mesokurtic variety tend to have “excess kurtosis of zero or close to zero” (Rashinkar, 2020).

   Applying this distribution to a hypothetical situation in criminal justice, a scenario in which the numbers of prisoners who are on parole are to be determined before they return for other crimes, or some technicalities. Arguing from the supposition that the prisoners began their terms at the same time, then Kurtosis can be applied to determine the rate of return (and rate of repeat offenses) to prison (Cunneen & Tauri, 2019).

   Leptokurtic Distribution

               These are frequency distributions whose curves peak above the mesokurtic (normal) curve. As a result, they are comparatively higher than the other two and narrower as well, indicating a concentration of data around measures of central tendency. In statistical analysis, Leptokurtic phenomena point toward a “positive excess kurtosis” (Rashinkar, 2020). They indicate dense tails on both sides of the distribution, an attribution to the presence of a large number of outliers. In criminology, leptokurtic curves may be used to model the number of complaints against the police from members of the public (Cunneen & Tauri, 2019). Specifically, it could indicate an unusually high number of complaints in a specified month or week as the researcher may determine.

   Platykurtic Distribution

   This frequency distribution curve has a characteristically “flat topped” than the leptokurtic (normal curve) (Walker & Maddan, 2019). Thus, this curve is relatively lower than the normal distribution. In practical situations, platykurtic distributions generally point toward “negative excess kurtosis” (Rashinkar, 2020). The kurtosis reveals a distribution with flat tails. It could be used to model situations in criminology. For instance, it could help to show an unusually low number of complaints against the police by members of the public for specific months or weeks as may be the case.

    

    

    

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Answer

Assessing Risk and Reward

The Nissan Motor Company is one of the leading automobile makers in the world. The Japanese carmaker has primarily enjoyed a successful run, allowing it to enter various regional and international markets such as the United States. However, the changing business environment was not favorable to the company in 2019. Notably, the cooperation recorded losses amounting to 7.8%. The experience pushed the management into making tough decisions, requiring almost all of its North American workforce to go for unpaid leaves.

In late 2019, the company announced that the decline in sales necessitated a two-day unpaid leave for the North American workers. The stated days for the vacation were January 2 and 3^rd    (Chicago Tribune, 2019).  Notably, this move was a crucial decision for the company because of its conflicting impacts. Whereas on the positive side, it could help the firm minimize expenses, it threatened to affect the public perception of the company regarding employee welfare.

The rewards for the decision involved cutting expenses by not paying the workers on leave, which eventually would translate into reduced expenses. Another reward was that the decision could allow the company to optimize performance by evaluating employee performances then developing new milestones. However, on the low side, the company risked affecting its public image and brand name, especially in the North American market. As per Chakravorti (2010), the way an organization treats its employees influences the firm’s public perception. Thus, Nissan risked eliciting a negative public perception. With a distorted public image, the company could fail to revive its declining sales.

I would have advised the management of Nissan to utilize the Predictive Analytic perspective in determining the right decision to take. Ideally, the approach tries to predict what might happen in the future if particular decisions or actions are undertaken at the moment (Traymbak & Aggarwal, 2019). Looking at the situation at Nissan, the company needed to develop a goal such as increasing sales. After that, they would have made decisions aimed at realizing the set goal. In this regard, the predicted outcome could give the management an overview of whether more risks existed or significant rewards could be realized.

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