Analyses. Better Impact and its related and affiliated entities may create analyses utilizing, utilising in part, Data and information derived from Customer’s use of the SaaS and Consulting Services, as set forth below (“Analyses”). Analyses will always anonymise and aggregate information and will be treated as Materials. Unless otherwise agreed, personal data contained in the Data is only used to provide the SaaS and Consulting Services. Analyses may be used for the following purposes:
Analyses. A/ – AS soon as possible after each shipment, the Buyer and the Seller shall exchange, at a date to be agreed upon, the results of analyses made in their respective laboratories on the samples drawn during the loading operations displaying both moisture content of the product as delivered and dry basis BPL concentration. In case the difference between the dry basis BPL contents shown by the two analyses is below or equal to one BPL unit per cent, the average of moisture contents of the product as delivered and the average of dry basis BPL contents shall be taken into consideration as concerns the corresponding cargo for the drafting of the debit or credit note provided for in Article 7 below.
Analyses. The Contractor shall perform any analyses deemed necessary to demonstrate compliance with Contract requirements or to substantiate the integrity of the spacecraft delivered under this Contract. All analyses shall be consistent with requirements of other applicable Contract exhibits. The Contractor may use, where relevant, results of valid and applicable analyses already performed for similar spacecraft or equipment and properly updated for this Contract. The Contractor's existing valid, and applicable computer/mathematical models and analytical/design tools used for similar spacecraft or equipment may also be used to perform the analyses defined herein.
Analyses. HRMANTRA or its Affiliates may create analyses utilizing, in part, Customer Data and information derived from Customer’s use of the Cloud Service and Services. Analyses will anonymize and aggregate information, and will be treated as Cloud Materials. Examples of how analyses may be used include: optimizing resources and support; research and development; automated processes that enable continuous improvement, performance optimization and development of new HRMANTRA products and services; verification of security and data integrity; internal demand planning; and data products such as industry trends and developments, indices and anonymous benchmarking.
Analyses. An autoregressive panel model and a latent growth model were both fit to the data using Structural Equation Modeling (SEM) and the Mplus statistical software package. Full Information Maximum Likelihood (FIML) treatment was used for missing data for both models. This is a missing data estimation approach for structural equation modeling which has been shown to produce unbiased parameter estimates and standard errors by estimating a likelihood function for each case based on the available data so that all cases are included. An autoregressive panel model (see Figure 1 in appendix) allows for estimation of the impact of one variable (e.g., sleep at time 1) on another variable (psychotic symptoms at time 2), while controlling for the prior level of the construct being predicted (i.e., controlling for psychotic symptoms at time 1). The addition of the autoregressive effect (AR1) in the model allows one to rule-out the possibility that any cross-lagged effect (CL) is simply due to the fact that the two variables were correlated at time 1. This results in more conservative estimations of the potential cross-lagged effect that minimizes bias (Xxxx & Xxxxxxx, 2003). Other paths can also be added to the model including a second-order autoregressive effect (AR2). In Figure 1, the curved arrow between both variables at Time 1 represents a correlation (Corr.). At later time points, the curved arrows represent residual correlations (Res. Corr.), which accounts for the residual variance after the effects of prior time points are estimated. Although there are many advantages to using an autoregressive panel model to investigate longitudinal relationships between variables, there are also limitations to this type of model, such as the assumption of linear change, lack of focus on intra-individual variability, and its lack of theory and ability to show how change occurs (Xxxxx & Little, 2012). Latent growth models have been suggested as alternatives that benefit from taking these issues into account (Rogosa, 1987). Since there is a lack of longitudinal research in this area and therefore no strong theories of longitudinal change process, either model could be appropriate. Thus, both models were used and then evaluated for fit. See Figure 2 in appendix for the full growth curve model used in these analyses. Order of Analyses First, both the full autoregressive panel model and the growth curve model were evaluated for fit using the absolute fit index, Root Mean Square Error ...
Analyses. The Cliffs Pellets delivered hereunder will be sampled and analyzed by mine technicians or such independent chemists as may be mutually agreed upon, and said analyses shall be final and the weighted average of all such analyses of each grade of Cliffs Pellets delivered hereunder shall constitute the basis of settlement hereunder for such grade of Cliffs Pellets. The cost of sampling and analyzing by independent chemists, if requested by any party, shall be borne by the party requesting such sampling and analyzing.