Consider the graph of the linear function h(x) = –6 + x. Which quadrant will the graph not go through and why?

Mathematics

2 answers:

OLga [1]4 years ago

6 0

Quadrant 11 it is positive slope with a -6 y intercept

Anit [1.1K]4 years ago

3 0

Answer:

Quadrant II, because the slope is positive and the y-intercept is negative

Step-by-step explanation:

Did it.

Proof:

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3 10 in . = 7 8 mi . what is the unit rate

Sav [38]

Answer: (3/10) in = (7/8) mi

1 in = (7/8) mi ÷ (3/10)

= 7/8 * (10/3) = 70/24

1 in = (70/24) mi

It means every 1 inch on the map drawing represents (70/24) miles ≈ 2.917 miles in reality.

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6 0

3 years ago

2.According to www.city-data, the mean price for a detached house in Franklin County, OH in 2009 was $192,723. Suppose we know t

igor_vitrenko [27]

Answer:

0.7123 = 71.23% probability that a random sample of 75 detached houses in Franklin County had a mean price greater than $190,000 in 2009.

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 z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \frac{\sigma}{\sqrt{n}}$ .