Answer:
The <em>p</em>-value of the test is 0.0512.
Step-by-step explanation:
The <em>p</em>-value of a test is well-defined as per the probability, [under the null hypothesis (H₀)], of attaining a result equivalent to or more extreme than what was the truly observed value of the test statistic.
In this case the output of the t-test_ind method from scipy module is provided as follows:
Output = (-1.99, 0.0512)
The first value within the parentheses is the test statistic value.
So the test statistic value is, -1.99.
And the second value within the parentheses is the <em>p</em>-value of the test.
So the <em>p</em>-value of the test is 0.0512.
Answer:
Step-by-step explanation:
Given sequence
-9, -5, -1,3,7,...
Is A.P(,airthmetic progression)
So
nth term of an A.P =a+(n-1)d
Where a=first term (-9)
D=difference=(-5)-(-9)=4
So nth term=-9+(n-1)4
=4n-13
Answer is 4n-13
Answer: can you explain what you mean because its just numbers
Answer:
See explanation
Step-by-step explanation:
The average rainfall when you add all 10 years of rainfall up and divide by 10 is an average of 59.946 inches of rain each year. The equation for the data is y = -0.53x + 64.45, this means that the rainfall is getting less each year at a -0.53 inches of rain each year.
x = year (2004 would be 4, etc)
Answer:
Dependent Variable : Tire tread wear ; Independent Variable : Tire Brand ; Confounding Variable : Person driving
Step-by-step explanation:
Dependent Variable is the variable being affected by independent variable(s). Independent Variable(s) are the causal variable, bring change in dependent variable.
Goodrich wants to demonstrate that his tires were better than those of his competitor (Goodyear). For that, he has got conducted an independent research on tires worn quality - brand wise & various factors affecting wear
- Dependent Variable is the 'Tire tread wear '.
- Independent Variables determining it is primarily brand : Goodrich / Goodyear ; secondarily - price, mileage, time etc
Confounding variable is an extraneous influence variable; that changes the relationship between independent & dependent variable, outcome of experimental research.
In this case : Individuals driving the vehicles could be a confounding variable. A particular person could wear out tire more than another person.
