Answer:
Null hypothesis:
Alternative hypothesis:
Since the p value is lower than the significance level wedon't have enough evidence to conclude that the true mean is significantly different from 52.6 MPG.
Step-by-step explanation:
Information provided
represent the sample mean for the MPG of the cars
represent the population standard deviation
sample size of cars
represent the value that we want to test
represent the significance level for the hypothesis test.
z would represent the statistic (variable of interest)
represent the p value for the test
Hypothesis
We need to conduct a hypothesis in order to check if the true mean of MPG is different from 52.6 MPG, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
Since we know the population deviation the statistic is given by:
(1)
Calculate the statistic
Replacing we have this:
Decision
Since is a two tailed test the p value would be:
Since the p value is lower than the significance level wedon't have enough evidence to conclude that the true mean is significantly different from 52.6 MPG.
Answer:
A and B
Step-by-step explanation:
A. Since 2 angles are congruent you know that there has to be 2 equal sides
B. It has an obtuse angle which is 100 degrees.
C. Triangle has 2 congruent sides so this is incorrect
D. Not all sides are equal
E. Not all angles are acute
F. None of the angles are 90 degrees
Answer:
The graph can show you how much distilled water for 4 milliliters of ammonia and 5 milliliters of ammonia. You can use the graph to find 4.5 by find the midpoint of two amounts of distilled water. Such as, say that 4 milliliters of ammonia = 200 distilled water and 5 milliliters of ammonia= 250 distilled water. Then the answer to 4.5 milliliters of ammonia is 225 distilled water.
Step-by-step explanation:
1 foot is the least. 100 yards is smaller the 3 yards because 3 yards is equal to more than 100 inches. 3 yards is the greatest
Answer:
The correct answer is wins and rebounds are correlated positively ,but we cannot decided that having more rebounds leads to more wins,on average.
Step-by-step explanation:
From the example given, the most appropriate conclusion is that, because causation is not the same as correlation, If two variables are compared,this does not mean that one leads to the other.
An observed data is based on correlation,but for description of causation ,we need to make experiments,as we update the variable treatment regarding to the changes in response variable.