In Arabic inna wa-aḫawātu-hā ‘inna and its related sisters’ are traditionally considered as verb-like particles. They are specified as introducing equational sentences and change their constituents’ case to a different pattern from what verbs do. Therefore, they are called nawāsiḫ in Arabic, or words that cause a shift to the accusative case (Ryding 2005). The medieval grammarians’ treatment of inna and its sisters as verb-like particles and of the equational sentence in general is based on the theory of ‘amal, ‘government’ which Sībawayhi has described it in his book Al-kitab. The theory presumes a grammatical operation (‘amal) in which an operator (‘āmil) assigns to a unique operand (ma’mūl) a grammatical function (Carter, 1973, 151). However, in modern linguistics, government is realized as a syntactic relation that imposes case agreement between the syntactic elements in the sentential structure. And this structure has a deep representation and surface representation. The Medieval treatment for the equational sentence introduced by inna is problematic, because it attributes to inna a verbal power to resolve the issue of the case assignment to the equational sentence which lacks an overt syntactic operator. Modern approaches to equational sentence differ totally from the traditional account. Some modern approaches propose a copula for the equational sentence; this copula is either covert or deleted. Other modern approaches propose a tense projection in deep structure that determines an equational sentence’s surface form. Neither sort of approach gives a reasonable explanation for the case assignment pattern, for the general properties of equational sentences, or for the status of inna. In this study, I propose a new approach focusing on the role of semantics in the assignment of case in equational sentences in Arabic. My hypothesis is based on a new interpretation to Sībawayhi’s description of the ‘ibtida’ sentence; according to this new interpretation ibtida’ is not a syntactic operator but rather a semantic one. I also propose that a sentence’s syntactic properties are sensitive to its semantic MODE, a specification of whether it expresses a topic-based proposition; or an event-based proposition. My new hypothesis is intended to apply to all varieties of Arabic including Classical Arabic, and Modern Standard Arabic, as well as the regional dialects of Arabic.

The analysis of big data is now very popular. Big data may be very important for companies, societies or even human beings if we can take full advantage of them. Data scientists defined big data with four Vs: volume, velocity, variety and veracity. In a short, the data have large volume, grow with high velocity, represent with numerous varieties and must have high quality. Here we analyze data from many sources (varieties). In small area estimation, the term ``big data' refers to numerous areas. We want to analyze binary for a large number of small areas. Then standard Markov Chain Monte Carlo methods (MCMC) methods do not work because the time to do the computation is prohibitive. To solve this problem, we use numerical approximations. We set up four methods which are MCMC, method based on Beta-Binomial model, Integrated Nested Normal Approximation Model (INNA) and Empirical Logistic Transform (ELT) method. We compare the processing time and accuracies of these four methods in order to find the fastest and reasonable accurate one. Last but not the least, we combined the empirical logistic transform method, the fastest and accurate method, with time series to explore the sales data over time.