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4 years ago

Solve the absolute value equation. |9x-3|=12

Mathematics

2 answers:

Alex4 years ago

7 0

Answer:

9 ×3 = 27 .......

<h2>Why ?</h2><h2>9 +9 +9 = 27. if you draw 3 big circles and 9 circles inside each one and count it u will get 27 BTW TRY IT BYEEEEEREEEEREERRRRR SKSKSKSK</h2>

Virty [35]4 years ago

5 0

Answer: x = 5/3 OR x = -1

Step-by-step explanation: to solve this, we can rewrite the absolute value as two inequalities (one equal to the positive value, one equal to l the negative value) and solve each:

| 9x - 3 | = 12 becomes:

9x - 3 = 12

9x = 12 + 3

9x = 15

x = 15/9

x = 5/3 OR 1.66 as decimal

and now the other:

9x - 3 = - 12

9x = -12 + 3

9x = - 9

x = -9/9

x = -1

Because an equality is given, when we solve for x, there are technically just two possible values that would meet the conditions. Either 5/3 or - 1

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Read 2 more answers

The data set below shows the temperature, in degrees Fahrenheit, of a mug of tea compared tothe time in minutes after being pour

Verizon [17]

Given the model of the Exponential Regression:

$y=174.4(0.987)^x$

By definition:

$Residual=Observed\text{ }y\text{ }value-Predicted\text{ }y\text{ }value$

You can see in the table the observed y-values (the temperature in Fahrenheit)

In order to find the Predicted y-values, you need to substitute all the x-values given in the table (the time in minutes) into the equation and then evaluate. You get:

$y=174.4(0.987)^5\approx163.35$ $y=174.4(0.987)^8\approx157.07$ $y=174.4(0.987)^{11}\approx151.02$ $y=174.4(0.987)^{15}\approx143.32$ $y=174.4(0.987)^{18}\approx137.80$ $y=174.4(0.987)^{22}\approx130.77$ $y=174.4(0.987)^{25}\approx125.74$ $y=174.4(0.987)^{29}\approx119.33$ $y=174.4(0.987)^{32}\approx114.73$ $y=174.4(0.987)^{35}\approx110.32$

Now you have these points:

$(5,163.35),(8,157.07),(11,151.02),(15.143.32)(18,137.800),(22,130.77),(25,125.74),(29,119.33),(32,114.73),(35,110.32)$

Therefore, you can plot them on the Coordinate Plane:

By definition, when the residual plot shows a pattern, a non-linear regression model is appropriate for the data. Therefore, the Exponential Regression Model is a good fit.

Hence, the answer is:

- Residual Plot:

- First option.