Answer: part a is A and part b is C if not i am truly very sorry i tried to help.
Explanation:
Answer:
Compound-complex (I believe)
Explanation:
There's a part of that sentence you do not need, the 'whose fair complexion makes her prone to sunburns'. If you took that out, you would still have a complete sentence. So I think it would be compound comple
Explanation:
Answer:
Golden Slumbers Kiss Your Eyes
Explanation:
When wanting to lull a child to sleep, mothers or any other caretaker or guardian usually choose soothing songs with lyrics that are not difficult to understand and that deal with sweet, tender themes.
The title of this song brings to mind a beautiful sleepiness that is so inviting it is impossible to resist. The kisses on the eyes refer to the part of the body required to start sleeping and is soothing in a gentle way.
Answer:
C. very personal and reflects the way you think about yourself.
"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.
