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kogti [31]
3 years ago
14

The row reduction algorithm applies only to augmented matrices for a linear system.Is this statement true or​ false?

Mathematics
1 answer:
Rudiy273 years ago
7 0

Answer:

afffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff

Step-by-step explanation:

afafafafafafafafafafafafafafafafafafafaf

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If tim spends 1/3 each weekday sleeping and about 7/24 each weekday in school what fraction of a weekday does Tim spend either s
Ganezh [65]
Tim spends 1/3 each weekday sleeping and 7/24 in school. We can write 1/3 as 8/24 so we have a common denominator. Now we can see that Tim sleeps for 1/24 time of a weekday more then he spends in school.

I hope that's what you meant.
3 0
3 years ago
Can somebody please explain and answer this for me? I really don't get it.
Tcecarenko [31]

Answer:

Results can be (-\frac{1}{3} )^5

Step-by-step explanation:

-The solution for this question:

(-\frac{1}{3} )^5 is equal to  (-\frac{1}{3}) × (-\frac{1}{3}) × (-\frac{1}{3}) × (-\frac{1}{3}) × (-\frac{1}{3})

-The calculation for (-\frac{1}{3} )^5:

(-\frac{1}{3} )^5 = -\frac{1}{243}

3 0
3 years ago
Please help!!! Find the value of the variable and the measures of the angles.
e-lub [12.9K]

Answer:

x= 38°

<P = 57°

<R = 38

<Q = 85°

5 0
1 year ago
Identify the slope by converting standard form into slope-intercept form.<br> 12x - 9y = -9
Novosadov [1.4K]

Answer:

4/3

Step-by-step explanation:

12x-9y=-9

9y=12x-(-9)

9y=12x+9

y=12/9x+9/9

y=4/3x+1

y=mx+b where m=slope and b=y-intercept,

so the slope is 4/3

8 0
3 years ago
Recall the Akaike information Criterion (AIC) as well as the related Bayesian Information Criterion (BIC) from our study of Vari
irga5000 [103]

Step-by-step explanation:

To check out how efficient or accurate a model is, we use the akaike information criterion or the Bayesian. If the AIC or BIC are lower, then this model would be better. They are also used to control for model complexity

Akaike information criterion = 2k-2ln where k is the number of parameter. A higher k gives a higher AIC.

In the real world complex models are discouraged and avoided since

1. They cause data to be over fitted and can capture noise and information from this data.

2. They are complex and therefore difficult to interpret

3. They consume a lot of time and computing them has several inefficiencies.

Using these two as measure of performance, we can select optimal choice of independent variable.

With forward/backward regression, we are able to put new variables in the model or remove from it. The best is the one with lowest AIC.

5 0
3 years ago
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