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3 years ago

Here’s a graph of a linear function. Write the equation that describes that function. (Zoom in if blurry)

Mathematics

2 answers:

nika2105 [10]3 years ago

8 0

Answer: y = x + 1

Step-by-step explanation:

The equation of a straight line can be represented in the slope-intercept form, y = mx + c

Where c = intercept

Slope, m =change in value of y on the vertical axis / change in value of x on the horizontal axis

change in the value of y = y2 - y1

Change in value of x = x2 -x1

y2 = final value of y

y 1 = initial value of y

x2 = final value of x

x1 = initial value of x

From the graph,

y2 = 6

y1 = 2

x2 = 5

x1 = 1

Slope,m = (6 - 2)/(5 - 1) = 4/4 = 1

To determine the intercept, we would substitute x = 1, y = 2 and m= 1 into y = mx + c. It becomes

2 = 1 × 1 + c

2 = 1 + c

c = 2 - 1

c = 1

The equation becomes

y = x + 1

NeTakaya3 years ago

5 0

Answer:

y=x+1

Step-by-step explanation:

rise of 1, run of 1, and y-intercept is one

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67.30% probability that the mean weight will be between 16.6 and 22.6 lb

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 normally distributed samples are 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 sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$