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Gnom [1K]
4 years ago
12

169a2-52a-65b-196b2​

Mathematics
1 answer:
Kobotan [32]4 years ago
3 0

Answer:

13\left(13a^{2}-4a-5b-15b^{2}\right)

Step-by-step explanation:

factored 13

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The table shows values for functions f(x) and g(x). x f(x)=2x−3 g(x)=73x−2 −1 −52 −133 0 −2 −2 1 −1 13 2 1 83 3 5 5 4 13 223 5 2
Verizon [17]

0 and 2

f(x) = g(x) will be the input or x value at which f and g have the same output or y value. Look in the table where two numbers repeat right next to each other.

−1 −7/2 −9/2

0 −3 −3

1 −2 −3/2

2 0 0

3 4 3/2

4 12 3

5 28 9/2

There are two solutions to f(x) = g(x) which are x=0 and x=2.

6 0
3 years ago
Jim dan and David share some sweets in the ratio 5:4:5. Jim gets 65 sweets. How many did dan get?
Elodia [21]

Answer:

52

Step-by-step explanation:

I'm pretty sure the answer is 52.

In Jim and Dave each got 5 and David got 4 then :

65/5 = 13

65 - 13 = 52

or

5 4 5

10 8 10

15 12 15

20 16 20

25 20 25

30 24 30

35 28 35

40 32 40

45 36 45

50 40 50

55 44 55

60 48 60

65 52 65

8 0
2 years ago
GIVING BRAINLIEST!!!!
pochemuha

Answer:

It is a function

Step-by-step explanation:

7 0
3 years ago
X = The set of months in a year?
Mariulka [41]

there are 12 set of months in a year

7 0
3 years ago
If, for a random sample of 16 days, the sample mean ends up being exactly one standard deviation of x above the population mean,
Rudik [331]

Answer:

The answer to the question is

μx + (σx/4)

Step-by-step explanation:

The sample mean that is one standard deviations above the population mean  is given by

value = μx + (Number of standard deviations)

(σx/√n)

value = μx + 1 (σx/√16)=

=  μx + (σx/4) =

Where

μx = Population mean

σx = Population standard deviation

n = Sample size

The standard error of the mean is

σ/√n =σ/√16 = σ/4

The standard of error is an indication of the expected error in the mean of a sample from the mean of the population.

The above statements is based on the central limit theorem, which states that, in particular instances the normalized sum of independent random variables becomes closer and closer to those of  normal distribution regardless of the variation in the sample of the variables

8 0
3 years ago
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