Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person’s other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson’s patients, it is higher in the ALS patients than all control groups.

Ability to pay of a company's short term liabilities is closely related to its strong liquidity structure. One of the most important factors affecting the liquidity of a company is operating cycle. The main factors that determine the operating cycle of the company is its stock supply term, production stage time and debt payment due. These factors determine the amount of net working capital. Net working capital is calculated by subtracting current liabilities from short term liabilities and the management of net working capital is extremely important for the liquidity of a company. Many businesses may have financial difficulty even go bankrupt tragically due to miscalculation net working capital need or incorrect financial resources to finance to the net working capital need. In this study, it is intended to analyse the effects of changes in macroeconomic data over net working capitals of enterprises. For this purpose it has been calculated net working capital of the industrial market by using its sectoral balance sheet which was issued by Central Bank of the Turkish Republic for period of 1996-2014. The later it has been determined positive and negative trends of sector's net working capital by years and modelled for inflation, exchange rates and interest rates to determine if these macro-economic variables effect on net working capital of the industrial market.

A direct parametric test is proposed to detect monotonic and non-monotonic types of heteroscedasticity. After giving brief information about non-monotonic types of heteroscedasticity, the test algorithm is introduced. Proposed test and usual heteroscedasticity tests are compared on monotonic and non-monotonic types of heteroscedasticity in real and artificial data.

All the usual heteroscedasticity tests in the statistics and econometrics literature are based on raw residuals. Although the raw residuals are heteroscedastic, studentized residuals can still be homoscedastic. In this study, the version of Çelik’s RCEV heteroscedasticity test which is based on studentized residuals is introduced.

Double outward box distributed residuals are another type of non monotonic heteroscedasticity that severely violates homoscedasticity assumption. In this study Çelik's ( 2015 Çelik, R. (2015). Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable. J. Appl. Stat. 42(4):705–721. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ) WCEV is applied to double outward box distributed residuals to provide homoscedasticity for simple and multiple regression models.