Answer:
Hello your question is incomplete attached is the image of the complete question
answer : chi square Goodness of fit
Explanation:
The chairperson should run a statistical analysis known as Chi-square Goodness of fit. this is a non-parametric test that is used to determine/discover how the observed value of a given condition/phenomena is very much different from the expected value from the observer.
The chairperson will carry out this analysis because it is expected that every faculty member is expected to maintain the same level of relationship with the students and not to be more popular with some alone.
This question is incomplete, here's the complete question.
See attached venn diagram.
How many women at the party are under 30?
How many men at the party are not under 30?
How many women are at the party? How many people are at the party?
Answer:
- 16 women under 30
- 22 men not under 30
- 44 women at the party
- 81 people at the party
Explanation:
A Venn diagram has overlapping circles, each one containing all the components of a group. Where the circles
The overlap reveals the elements that different groups have in common.
Total of people at the party:
22 men not under 30
+
15 men under 30
+
16 women under 30
+
28 not men, nor under 30
Total = 81 people at the party
If there are 22 men not under 30, and 15 men under 30, it means there´re 37 men and 44 women at the party.
They disregard them by comparing one black slave to only 3/5 of a human. it demoralized them bc liberty means freedom but they were slaves, equality is automatically thrown out bc they are only 3/5 of a white man. and self governing is no bc they were forced to do things such as cultivating cotton without their own consent per se
Answer: i belive its the twelth amendent
Explanation:
sorry for bad spelling
Answer:
It is not reasonable to say there is a correlation because it is categorical data. However if it was quantitative data, correlation doesn’t always mean causation, because there might be athird variable (lurking variable) that may have a better explanation for the correlation.
Explanation:
Is it not reasonable to say that there's a correlation between the type of car you own and the risk that it will be stolen because it is categorical data. However if it was quantitative data, correlation doesn’t always mean causation, because there might be a third variable that may have a better explanation for the correlation and it might as well means that the third-lurking variable affects the correlation; for example, those cars that are most frequently reported stolen may simply be the cars that are more commonly sold because they are cheaper than the cars that are stolen least often, and thus have a higher chance of being exposed to thieves.
