The conditional probabilities that are correct are P(D | F) = 6/34, P(E | D) = 7/25 and P(F | E) = 8/18
<h3>How to determine the true
conditional probabilities ?</h3>
The formula to compute the conditional probability P(A|B) is:
P(A | B) = P(A and B)/P(B)
The above means that the probability of event A such that the event B has already occurred
When the above formula is applied to the give data in the complete, we have:
P(D | F) = 6/34
P(E | D) = 7/25
P(D | E) = 7/18
P(F | E) = 8/18
P(E | F) = 8/34
Hence, the conditional probabilities that are correct are P(D | F) = 6/34, P(E | D) = 7/25 and P(F | E) = 8/18
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Answer:
For this case, the parent function is given by:
f (x) = x ^ 2
We apply the following transformations:
Horizontal translations:
Suppose that h> 0
To graph y = f (x + h), move the graph of h units to the left.
We have then for h = 5:
y = (x + 5) ^ 2
Vertical translations :
Suppose that k> 0
To graph y = f (x) -k, move the graph of k units down.
We then have for k = 9:
g (x) = (x + 5) ^ 2 - 9
Answer:
The graph of g (x) is the graph of f (x) 5 units to the left and 9 units to down
<h2>
Please mark me as brainlyist</h2>
Answer:
Step-by-step explanation:
Given that for a repeated measures study comparing two treatment conditions, an experiment was conducted.
n=9: Mean difference =4:
Variance = s^2 =36
Std error = 
t statistic = Mean diff/SE
= 
In 2000 - 3250
<span>In 2001 - 3640 </span>
<span>3640 - 3250 = 390 </span>
<span>(390/3250)*100 = 12% </span>
<span>2001 - 3640 </span>
<span>2002 - 4100 </span>
<span>4100 - 3640 = 460 </span>
<span>(460/3640) * 100 = 12.6%</span>
