I believe the answer is D
Answer:
A 0.6
Step-by-step explanation:
the fraction is 3/5 so if you do the eqation 3 divided by 5 u will get 0.6
If he used 34 bottles for every batch just divide 214 bottles he used by 34 it takes for a batch
he made 6.3 batches
Answer:
311.25 hours ; almost 13 days
Step-by-step explanation:
actually you will not able to drive a car all the way around the earth (the Pacific and Atlantic oceans are really big).
but According to the calculation it would take you around 311.25 hours ( almost 13 days) to drive the 24,900 miles around the circumference of the earth at its widest point (the equator).
because we know ,
t=s/v
=(24,900/80)hours [ s= the circumference of the earth]
=311.25 hours [v=Velocity]
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.