This seems like more of an opinion based question, but since they added "over a longer distance" If I were you I would go with false.
You should definitely consider what kind of audience you are appealing to. For example, if you were running a business based on cosmetic products you may want to focus your website on self-care and makeup tips rather than something like cooking. By making your website direct about what you offer, the better the audience will understand. This will make your website succeed. Hope this helped :))
Answer:
The answer varies from person to person.
Explanation:
All kinds of people are using Word, so people would recognize if the answer if plagiarized. So, simply answer truthfully; no matter h1ow embarrasing.
Answer:
architectures, tools, databases, analytical tools, applications, and methodologies
Explanation:
There are several features of business intelligence. It is a content-free expression, which means that it means different things to different people, and not same thing as suggested by Option B. While its major objective is to enable or allow easy access to data, it is not limited to data and IT only as suggested by Option C. Instead it provides managers of businesses with the ability of analysis of data. And finally it helps in the transformation of data to information and to action, which is contrary to the suggestions of Option D. Hence the first option is the only correct option.
Answer:
accounting system
Explanation:
The most common response variable modeled for cropping systems is yield, whether of grain, tuber, or forage biomass yield. This yield is harvested at a single point in time for determinate annual crops, while indeterminate crops and grasslands may be harvested multiple times. Although statistical models may be useful for predicting these biological yields in response to some combination of weather conditions, nutrient levels, irrigation amounts, etc. (e.g., Schlenker and Lobell, 2010, Lobell et al., 2011), they do not predict responses to nonlinearities and threshold effects outside the range of conditions in data used to develop them.
In contrast, dynamic cropping and grassland system models may simulate these biological yields and other responses important to analysts, such as crop water use, nitrogen uptake, nitrate leaching, soil erosion, soil carbon, greenhouse gas emissions, and residual soil nutrients. Dynamic models can also be used to estimate responses in places and for time periods and conditions for which there are no prior experiments. They can be used to simulate experiments and estimate responses that allow users to evaluate economic and environmental tradeoffs among alternative systems. Simulation experiments can predict responses to various climate and soil conditions, genetics, and management factors that are represented in the model. “Hybrid” agricultural system models that combine dynamic crop simulations with appropriate economic models can simulate policy-relevant “treatment effects” in an experimental design of climate impact and adaptation (Antle and Stockle, 2015).
