Melanie said:
Every angle bisector in a triangle bisects the opposite side perpendicularly.
A 'counterexample' would show an angle bisector in a triangle that DOESN'T
bisect the opposite side perpendicularly.
See my attached drawing of a counterexample.
Both of the triangles that Melanie examined have
equal sides on both sides
of the angle bisector. That's the only way that the angle bisector can bisect
the opposite side perpendicularly. Melanie didn't examine enough different
triangles.
Answer: 49 students
Step-by-step explanation:
2 /7 of students in a school wore shorts today. The number of students to be selected in order to get 14 students who will wear shorts to school will be:
Let the number of students that will be sampled be y. This will be:
2/7 of y = 14
2/7 × y = 14
2y/7 = 14
Cross Multiply
2y = 7 × 14
2y = 98
y = 98/2
y = 49
49 students will be selected randomly.
It began in 1929...right after the stock market crashed
In probability problems, look out for the word OR and AND.
OR means adding the probability
AND means multiplying the probability
P(a) = P(jack) + P(queen) + P(king) = (4/52) + (4/52) + (4/52) = 12/52 = 3/13
P(b) = [P(9) + P(10) + P(jack)] × P(red) = [(4/52) + (4/52) + (4/52)] × (26/52)
P(b) = (12/52) × (26/52) = 3/26
P(c) = 13/52 = 1/4
P(d) = P(a diamond) + P(a heart) + P(a spade) = (13/52) + (13/52) + (13/52) = 3/4
Answer:
Correct option: B. Less between group variability
Step-by-step explanation:
The Analysis of Variance (ANOVA) test is performed to determine whether there is a significant difference between the different group mean.
The hypothesis is defined as:
<em>H₀</em>: There is no difference between the group means, i.e. <em>μ</em>₁ = <em>μ</em>₂ = ... = <em>μ</em>ₙ
<em>Hₐ</em>: At least one of the mean is different from the others, i.e. <em>μ</em>
≠ 0.
The test statistic is defined as:

If the null hypothesis is true then the test statistic will be small and if it is false then the test statistic will be large.
In this case it is provided that the null hypothesis is true.
This implies that:

Implying that the sum of squares for between group variability is less than within group variability.
Thus, if the null hypothesis is true there will be less between group variability.