Answer:
What do you mean???
Step-by-step explanation:
Answer:
a) percentage of respondents that favored neither Obama nor Romney in terms of likeability = 7%
b) For a given survey of 500, the number of respondents that favored Obama than Romney is 145.
Step-by-step explanation:
Given that none of those surveyed can favour the two candidates at the same time,
n(Universal set) = n(U) = 100%
n(Obama) = n(O) = 61%
n(Romney) = n(R) = 32%
n(That favour Obama and Romney) = n(O n R) = 0%
To calculate for the number that favour neither of the candidates
n(O' n R')
n(U) = n(O) + n(R) + n(O n R) + n(O' n R')
100 = 61 + 32 + 0 + n(O' n R')
n(O' n R') = 100 - 93 = 7%
b) For a given survey of 500, how many more respondents favored Obama than Romney?
Number of those surveyed that favour Obama = 61% of 500 = 305
Number of those surveyed that favour Romney = 32% of 500 = 160
Difference = 305 - 160 = 145
Answer:
the intercept is 3/4
Step-by-step explanation:
it's the number in front of the x lol
Answer:
ii) a Bonferonni-corrected alpha level of 0.0167 to control the type I error rate for the overall inference to 5% .
Step-by-step explanation:
Previous concepts
Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".
The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"
The hypothesis for this case are:
Null hypothesis:
Alternative hypothesis: Not all the means are equal
Since we reject the null hypothesis we want to see which method it's the best to determine which group(s) is (are) different is pairwise two-sample t-tests each assessed using.
And on this case the best option is:
ii) a Bonferonni-corrected alpha level of 0.0167 to control the type I error rate for the overall inference to 5% .
The reason is because the Bonferroni correction "compensates for that increase by testing each individual hypothesis at a significance level of
who represent the desired overall alpha level and m is the number of hypotheses". For our case m=3 hypotheses with a desired
, then the Bonferroni correction would test each individual hypothesis at 
One advatange of this method is that "This method not require any assumptions about dependence among the p-values or about how many of the null hypotheses are true" . And is more powerful than the individual paired t tests.