Interpreting Power For the sample data in Example 1 “Adult Sleep” from this section, Minitab and StatCrunch show that the hypothesis test has power of

Interpreting z-scores: Complete the following statements using your knowledge about z-scores.
a. If the data is weight, the z-score for someone who is overweight would be
-positive
-negative
-zero
b. If the data is IQ test scores, an individual with a negative z-score would have a
-high IQ
-low IQ
-average IQ
c. If the data is time spent watching TV, an individual with a z-score of zero would
-watch very little TV
-watch a lot of TV
-watch the average amount of TV
d. If the data is annual salary in the U.S and the population is all legally employed people in the U.S., the z-scores of people who make minimum wage would be
-positive
-negative
-zero

For Questions I&5, use dimensional analysis witt need unit equivalences from this section and fror 1. How many miles is a 10-kilometer race?

Find the median of -4, 11, -12, 11, and -7.

Prove that if a|b and b|c, then a|c.

Dani says she is thinking of a secret number. As a clue, she says the number is the least whole number that has three different prime factors. What is Dani's secret number? What is its prime factorization?

a student is speeding down route 11 in his fancy red Porschewhen his radar system warns him of an obstacle 400 ft ahead. heimmediately applies the brakes, starts to slow down and spots askunk in the road directly ahead of him. the "black box" in thePorsche records the cars

Three Questions on Interpreting the Outcome of the Probability Density Function
I have three questions: What is the interpretation of the outcome of the Probability Density Function (PDF) at a particular point? How is this result related to probability? What we exactly do when we maximize the likelihood?
To better explain my questions:
(i) Consider a continuous random variable X with a normal distribution such that $\mu =1.5$ and ${\sigma }^2=2$. If we evaluate the PDF at a particular point, say 3.4, using the formula:
$f(X)=\frac{1}{\sqrt{2\pi {\sigma }^2}}{e}^{\frac{-{\left(X-\mu \right)}^2}{2{\sigma }^2}},$
we get $f(3.4)=0.1144$. How we interpret this value?
(ii) I previously read that the result of 0.1144 is not necessarily the probability that X takes the value of 3.4. But how the result is related to probability concept?
(iii) Consider a sample of the continuous random variable X of size N=2.5, such that $X_1=2$ and $X_2=3.5$. We can use this sample to maximize the log-likelihood:
$max\ln L(\mu ,\sigma |X_1,X_2)=\ln f(X_1)+\ln f(X_2)$
If f(X) is not exactly a probability, what are we maximizing? Some texts detail that "we are maximizing the probability that a model (set of parameters) reproduces the original data". Is this phrase incorrect?