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2 years ago

Graph the data. Then find and interpret the slope of the line through the points.

Mathematics

1 answer:

denpristay [2]2 years ago

5 0

Answer:

4. The slope is 1.5 pages per minute

The interpretation is 1.5 pages takes 1 minute to complete

5. The slope is 135 Calorie per mile

The interpretation is 135 calories of energy is required or used in traveling a distance of 1 mile

Step-by-step explanation:

4. The given data are;

Minutes, x; 0, 1, 3, 5

Pages, y; 0, 1.5, 4.5, 7.5

The graph of the data created with MS Excel is included

From the graph, we have;

The slope is given by the rate of change of the y-values to the change in the x-values of the data

∴ Slope = Δy/Δx

$The \ slope =\dfrac{\Delta y }{\Delta x} = \dfrac{y_{2}-y_{1}}{x_{2}-x_{1}} = \dfrac{7.5 - 1.5}{5 - 1} = 1.5$

The slope = 1.5 pages per minute

The interpretation of the slope is that the number of pages completed per minute is 1.5 pages

5. The given data are;

Miles, x; 0, 1, 2, 3

Calories, y; 0, 135, 270, 405

Please find attached the graph of the data created with MS Excel

From the graph, the slope = Δy/Δx = (270 - 135)/(2 - 1) = 135

The slope = 135 Calorie per mile

The interpretation of the slope is it takes 135 calories to complete 1 mile or vice versa.

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vagabundo [1.1K]

The z-score of the considered newborn having 4,000 g weight is $\dfrac{4000 - \mu}{\sigma}$

<h3>How to get the z scores?</h3>

If we've got a normal distribution, then we can convert it to standard normal distribution and its values will give us the z score.

If we have

$X \sim N(\mu, \sigma)$

(X is following normal distribution with mean $\mu$ and standard deviation $\sigma$ )

then it can be converted to standard normal distribution as

$Z = \dfrac{X - \mu}{\sigma}, \\\\Z \sim N(0,1)$

(Know the fact that in continuous distribution, probability of a single point is 0, so we can write

$P(Z \leq z) = P(Z < z) )$

Also, know that if we look for Z = z in z tables, the p value we get is

$P(Z \leq z) = \rm p \: value$

For the considered case, the statement in the problem is missing the mean weight of the newborn and standard deviation of the data set of weights of newborn.

Thus, for them, we can consider variables in place of their actual values.

Let we have: