Yup B is the answer because you can eliminate all the other answers because the paragraph doesn’t do A, C, or D
Answer:
Walter Lee Younger is a dreamer who tries to find quick solutions to solve his family's problems. He wanted to invest his father's insurance money to start up a liquor store. He thinks money will solve all his family's problems, but that's not true. One prediction is that he will continue to be this way, which will end up in his relationships with Mama, Ruth and Beneatha to be strained.
The second prediction is that he will change his ways, which is seen in the end of the play when Mr. Lindner offers to pay them to prevent them from moving into the white neighborhood. Walter refuses the money that Mr. Lindner offers so his family can live in their dream home, which shows promise of his personality traits changing, knowing that money won't solve all the problems.
Explanation:
This is what I came up with and I truly hope it helps. It was fun kind of going back to this play cause I read it in my sophomore year of high school, so I hope this helps!
Answer:
bc he is hopeful that is mother memory will make him think of the bad things that he has done,though Ignacio never did anything to her.
"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.
The figurative language used in the passage is a simile .
