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igor_vitrenko [27]
3 years ago
11

A line passes through the points (1, 4) and (3, 4). Which is the equation of the line?

Mathematics
1 answer:
xeze [42]3 years ago
8 0

Answer:

y = -4x + 8

Step-by-step explanation:

Slope-Intercept Form: y = mx + b

Slope Formula: m=\frac{y_2-y_1}{x_2-x_1}

Step 1: Find slope <em>m</em>

<em />m=\frac{-4-4}{3-1}<em />

<em />m=\frac{-8}{2}<em />

m = -4

y = -4x + b

Step 2: Find y-intercept <em>b</em>

4 = -4(1) + b

4 = -4 + b

8 = b

Step 3: Rewrite linear equation

y = -4x + 8

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Negative positive or no association
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Answer:

Positive association

Step-by-step explanation:

As the Temperature goes up the more money you have to spend because you pay for the electricity that produces the heat

positive association is when one varriable increases so does the other.

7 0
2 years ago
The results of a common standardized test used in psychology research is designed so that the population mean is 155 and the sta
galina1969 [7]

Answer:

The value <em>155</em> is zero standard deviations from the [population] mean, because \\ x = \mu, and therefore \\ z = 0.

Step-by-step explanation:

The key concept we need to manage here is the z-scores (or standardized values), and we can obtain a z-score using the next formula:

\\ z = \frac{x - \mu}{\sigma} [1]

Where

  • z is the <em>z-score</em>.
  • x is the <em>raw score</em>: an observation from the normally distributed data that we want <em>standardize</em> using [1].
  • \\ \mu is the <em>population mean</em>.
  • \\ \sigma is the <em>population standard deviation</em>.

Carefully looking at [1], we can interpret it as <em>the distance from the mean of a raw value in standard deviations units. </em>When the z-score is <em>negative </em>indicates that the raw score, <em>x</em>, is <em>below</em> the population mean, \\ \mu. Conversely, a <em>positive</em> z-score is telling us that <em>x</em> is <em>above</em> the population mean. A z-score is also fundamental when determining probabilities using the <em>standard normal distribution</em>.

For example, think about a z-score = 1. In this case, the raw score is, after being standardized using [1], <em>one standard deviation above</em> from the population mean. A z-score = -1 is also one standard deviation from the mean but <em>below</em> it.

These standardized values have always the same probability in the <em>standard normal distribution</em>, and this is the advantage of using it for calculating probabilities for normally distributed data.

A subject earns a score of 155. How many standard deviations from the mean is the value 155?

From the question, we know that:

  • x = 155.
  • \\ \mu = 155.
  • \\ \sigma = 50.

Having into account all the previous information, we can say that the raw score, <em>x = 155</em>, is <u><em>zero standard deviations units from the mean.</em></u> <u><em>The subject   earned a score that equals the population mean.</em></u> Then, using [1]:

\\ z = \frac{x - \mu}{\sigma}

\\ z = \frac{155 - 155}{50}

\\ z = \frac{0}{50}

\\ z = 0

As we say before, the z-score "tells us" the distance from the population mean, and in this case this value equals zero:  

\\ x = \mu

Therefore

\\ z = 0

So, the value 155 is zero standard deviations <em>from the [population] mean</em>.

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