A [____] is a tentative statement about empirical reality including a relationship between two or more variables.

The function is somewhat sinusoidal but not entirely. Its composed of piecewise line segments when we should have a single continuous smooth curve; however, it's still periodic since it repeats itself.

Let's start at the top at the left most corner. This point has x coordinate of x = 1.

The other endpoint of this top left flat portion is when x = 2. Then the function curve goes downhill until it reaches x = 4. From x = 4 to x = 5, we're at the flat bottom part. From x = 5 to x = 7 is when the function increases.

Once we get to x = 7, the process described earlier starts all over again. So this is when the cycle ends and the length of the period is 7-1 = 6 units. The function repeats itself every 6 units, or you can say the length of each cycle is 6 time units (eg: 6 seconds).

5 0

3 years ago

A sample from a population with μ = 40 and σ = 8 has a mean of M = 36. If the sample mean corresponds to a z = –1.00, then how m

juin [17]

Answer:

There are 4 scores in the sample.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .