Answer:
Marco recycled the empty soda can.
<h2>NATURE IN MY CITY</h2>
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Hello there brainly user! Actually I don't live in city, I'm currently living in a town, where all buildings were made from simple things, like cement, and there are alot of houses in my hometown. My opinion about nature in my hometown is a undescribable feeling. Why? If you are here in my house you can see a mesmerizing mountains, trees and some flowers, I can say that it was really taken care of. Furthermore, nature is valuable in and of itself. This is why Wageningen researchers are working on projects involving threatened animal and plant species in the city, tiny forests, nature-inclusive construction, urban agriculture, and green business parks. “There is lots going on, but there are still substantial barriers.” In addition, Most animals do not like noise, human activity, or disturbances. However, environment and ecology researcher Joost Lahr realised that for roughly 10% of plant and animal species, the city actually serves as a sanctuary.
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Note: This is just my own opinion, and it's up to your hometown or city where do you live and what did you observe or discover in your town or city place. Happy Learning user!
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"Spreading Learning"
B. Andreas first daughter was born in 2015.
Corrected:
Andrea’s first daughter was born in 2015.
"Critical region" redirects here. For the computer science notion of a "critical section", sometimes called a "critical region", see critical section.
A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables.[1] A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis that proposes no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.
An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model.[2] The most common selection techniques are based on either Akaike information criterion or Bayes factor.
Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
