True - since the outlier is way off it will cause your average to mess up which is why we test multiple times to make sure we are more accurate with our numbers
This means that with certain types of data, mean, median, or mode may be more effective for analysis.
Answer:
3
Step-by-step explanation:
<em>Rate of Change </em>is the same as <em>Slope</em><em>.</em><em> </em>According to the Slope-Intercept Formula, <em>y</em><em> </em><em>=</em><em> </em><em>mx</em><em> </em><em>+</em><em> </em><em>b</em><em>,</em><em> </em><em>m</em><em> </em>is the <em>Rate</em><em> </em><em>of</em><em> </em><em> </em><em>Change</em><em> </em>[<em>Slope</em>].
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Answer:
Test statistic = - 0.851063
- 2.520463
Step-by-step explanation:
H0 : μ ≥ 15
H1 : μ < 15
Sample mean, xbar = 14.5
Sample standard deviation, s = 4.7
Sample size = 64
Teat statistic :
(xbar - μ) ÷ (s/√(n))
(14.5 - 15) ÷ (4.7/√(64))
= - 0.851063
The critical value at α = 0.05
Using the T - distribution :
Degree of freedom, df = 64 - 1 = 63
Tcritical(0.05, 63) = 1.6694
Test statistic - critical value
-0.851063 - 1.6694
= - 2.520463
Answer:
with what
Step-by-step explanation:
