Answer:
he wanted slavery outlawed
Explanation:
the south wanted to keep their slavery due to the agricultural opportunities but Lincoln said it wasn't right and then the south became their own
Answer:
both approaches focus exclusively on the impact of culture and society on the individual.
Explanation:
Answer:
People who can take the same data, be logically consistent with it, and arrive at quite different answers to moral, ethical, or religious questions are said to have different:
Explanation:
- Environmental worldviews are those views that tell us that how people think about the working of world, how they fit in it as well as about the moral and ethical values. These views can be human centered, life centered, eco-centered or combination of these.
- The people who can take the same data, be logically consistent with it, and arrive at quite different answers to moral, ethical or religious questions have different environmental views because they are looking at the data with different perspectives.
The sherman Act. This act made monopolies illegal and disbanded standard oil
Answer:
B) The chance of committing a Type I error changes from 0.01 to 0.05., E) The test becomes less stringent to reject the null hypothesis (i.e., it becomes easier to reject the null hypothesis), therefore. C) It becomes harder to prove that the null hypothesis is true and G) The chance that the null hypothesis is true changes from 0.01 to 0.05 are all correct answers
Explanation:
The alfa error or type I, refers to the probability error of rejecting the null hypothesis, when it is true, it is the chance of mistake when affirming that an association exists between two variables tested as a cause of an effect on something. i.e: H1: fast food is responsible for diabetes (this is working hypothesis), H0: red hair is responsible for diabetes (this is the null hypothesis). The beta error or type II is related to the size of the sample, it is the chance of accepting something (the null hypothesis) when it is false, depends mostly on having enough measures (or persons under study) so your hypothesis can be proven and be a real representation of the population under study. The statistical significance, namely the p value, can be narrow (p=0.01) or wide (p=0.05), it can be easily understand if we explain it in terms of percentage: you can have 99% (p=0.01) of certainty to affirm that the null hypothesis (the one that you do not believe is true, in the example, red hair as cause of diabetes) is actually wrong or 95% (p=0.05) of certainty to affirm that the null hypothesis (again, the one that you do not believe is true) is actually wrong.
