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.
If Victoria puts 200 dollars a savings account she will earn 1.05 for x amount of time.<span>
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Answer:
The opposite of subtraction is addition, so you would have to add 4 to both sides:
