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Vikentia [17]
2 years ago
13

Which equation has roots -3 and 4

Mathematics
1 answer:
RSB [31]2 years ago
4 0

Answer:

x^2 - x - 12    (this is the second answer choice)

Step-by-step explanation:

Multiply (x + 3)(x - 4) and simplify the result:

x^2 - 4x + 3x - 12, or x^2 - x - 12    (this is the second answer choice)

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-3(2b-7) in stander from asap
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Answer:

<h3>- 6b + 21</h3>

Step-by-step explanation:

= > - 3 ( 2b - 7 )

= > - 6b + 21

<h3>Hope it helps you!</h3>
6 0
2 years ago
ben earns a base salary of $400.00 every week with an additional 11% commission on everything he sells. if ben sold $6050.00 wor
RUDIKE [14]
If you would like to know what was his total pay last week, you can calculate this using the following steps:

a base salary ... $400.00
an additional 11% commission on everything Ben sells
11% of $6050.00 = 11% * 6050 = 11/100 * 6050 = $665.5

$400 + $665.5 = $1065.5

Result: His total pay was $1065.5.
4 0
3 years ago
Read 2 more answers
A radio station is having a T-shirt design contest. The first prize is four times the second prize. The second price is four tim
hammer [34]
Answer:
The first prize is $800.

Explanation:
Third price = $50
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6 0
3 years ago
Point A’ is the image of Point A when point A is rotated about the origin B. What angle in the counterclockwise direction is A r
liberstina [14]

Answer:

90 degrees

Step-by-step explanation:

8 0
2 years ago
.For small training sets variance may contribute more to the overall error than bias. Sometimes this is handled by reducing the
Ymorist [56]

Answer:

Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.

3 0
2 years ago
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