Can I get some help with this question?

Sh363gdbzej3yve truly urban urge Heydyeh hdye

Answer:

c = 4√2

Step-by-step explanation:

From the question given above, the following data were obtained:

Angle θ = 30

Opposite = 2√2

Hypothenus = c =?

We can obtain the value of c by using the sine ratio as illustrated below:

Sine θ = Opposite / Hypothenus

Sine 30 = 2√2 / c

½ = 2√2 / c

Cross multiply

c = 2 × 2√2

c = 4√2

Therefore, the value of c is 4√2.

Answer:

2:3 is equal to 8:12

Step-by-step explanation:

2:3

To get the first number to 8

8/2 = 4

Multiply by all terms 4

2*3 : 3*4

8:12

2:3 is equal to 8:12

8:12 = 8/12

= 2/3

= 2:3

Therefore 2:3 and 8:12 are equalent to each other.

Answered by Gauthmath must click thanks and mark brainliest

Answer:

x = 7[tex]\sqrt{2}[/tex]

Step-by-step explanation:

Pytago:

[tex]7^2 + 7^2 = x^2\\x = \sqrt{7^2 + 7^2} \\x = 7\sqrt{2}[/tex]

Step-by-step explanation:

In a right triangle, you can find the leg of the triangle by using the Pythagorean theorem.

[tex]a^2+b^2=c^2[/tex]

In this case, we have [tex]7^2+7^2=c^2[/tex], or

[tex]c^2=98[/tex]

[tex]\sqrt{98}[/tex]≅[tex]9.9[/tex]

Answer:

a) 0.5768 = 57.68% probability that the shop sells at least 3 in a week.

b) 0.988 = 98.8% probability that the shop sells at most 7 in a week.

c) 0.0104 = 1.04% probability that the shop sells more than 20 in a month.

Step-by-step explanation:

For questions a and b, the Poisson distribution is used, while for question c, the normal approximation is used.

Poisson distribution:

In a Poisson distribution, the probability that X represents the number of successes of a random variable is given by the following formula:

[tex]P(X = x) = \frac{e^{-\lambda}*\lambda^{x}}{(x)!}[/tex]

In which

x is the number of successes

e = 2.71828 is the Euler number

[tex]\lambda[/tex] is the mean in the given interval.

Normal Probability Distribution

Problems of normal distributions can be solved using the z-score formula.

In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the z-score of a measure X is given by:

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

The Poisson distribution can be approximated to the normal with [tex]\mu = \lambda, \sigma = \sqrt{\lambda}[/tex], if [tex]\lambda>10[/tex].

Poisson variable with the mean 3

This means that [tex]\lambda= 3[/tex].

(a) At least 3 in a week.

This is [tex]P(X \geq 3)[/tex]. So

[tex]P(X \geq 3) = 1 - P(X < 3)[/tex]

In which:

[tex]P(X < 3) = P(X = 0) + P(X = 1) + P(X = 2)[/tex]

Then

[tex]P(X = x) = \frac{e^{-\lambda}*\lambda^{x}}{(x)!}[/tex]

[tex]P(X = 0) = \frac{e^{-3}*3^{0}}{(0)!} = 0.0498[/tex]

[tex]P(X = 1) = \frac{e^{-3}*3^{1}}{(1)!} = 0.1494[/tex]

[tex]P(X = 2) = \frac{e^{-3}*3^{2}}{(2)!} = 0.2240[/tex]

So

[tex]P(X < 3) = P(X = 0) + P(X = 1) + P(X = 2) = 0.0498 + 0.1494 + 0.2240 = 0.4232[/tex]

[tex]P(X < 3) = P(X = 0) + P(X = 1) + P(X = 2) = 1 - 0.4232 = 0.5768[/tex]

0.5768 = 57.68% probability that the shop sells at least 3 in a week.

(b) At most 7 in a week.

This is:

[tex]P(X \leq 7) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6) + P(X = 7)[/tex]

In which

[tex]P(X = x) = \frac{e^{-\lambda}*\lambda^{x}}{(x)!}[/tex]

[tex]P(X = 0) = \frac{e^{-3}*3^{0}}{(0)!} = 0.0498[/tex]

[tex]P(X = 1) = \frac{e^{-3}*3^{1}}{(1)!} = 0.1494[/tex]

[tex]P(X = 2) = \frac{e^{-3}*3^{2}}{(2)!} = 0.2240[/tex]

[tex]P(X = 3) = \frac{e^{-3}*3^{3}}{(3)!} = 0.2240[/tex]

[tex]P(X = 4) = \frac{e^{-3}*3^{4}}{(4)!} = 0.1680[/tex]

[tex]P(X = 5) = \frac{e^{-3}*3^{5}}{(5)!} = 0.1008[/tex]

[tex]P(X = 6) = \frac{e^{-3}*3^{6}}{(6)!} = 0.0504[/tex]

[tex]P(X = 7) = \frac{e^{-3}*3^{7}}{(7)!} = 0.0216[/tex]

Then

[tex]P(X \leq 7) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6) + P(X = 7) = 0.0498 + 0.1494 + 0.2240 + 0.2240 + 0.1680 + 0.1008 + 0.0504 + 0.0216 = 0.988[/tex]

0.988 = 98.8% probability that the shop sells at most 7 in a week.

(c) More than 20 in a month (4 weeks).

4 weeks, so:

[tex]\mu = \lambda = 4(3) = 12[/tex]

[tex]\sigma = \sqrt{\lambda} = \sqrt{12}[/tex]

The probability, using continuity correction, is P(X > 20 + 0.5) = P(X > 20.5), which is 1 subtracted by the p-value of Z when X = 20.5.

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

[tex]Z = \frac{20 - 12}{\sqrt{12}}[/tex]

[tex]Z = 2.31[/tex]

[tex]Z = 2.31[/tex] has a p-value of 0.9896.

1 - 0.9896 = 0.0104

0.0104 = 1.04% probability that the shop sells more than 20 in a month.

The probability of the selling the video recorders for considered cases are:

Let X ~ Pois(λ)

Then we have:

E(X) = λ = Var(X)

Since standard deviation is square root (positive) of variance,

Thus,

Standard deviation of X = [tex]\sqrt{\lambda}[/tex]

Its probability function is given by

f(k; λ) = Pr(X = k) = [tex]\dfrac{\lambda^{k}e^{-\lambda}}{k!}[/tex]

For this case, let we have:

X = the number of weekly demand of video recorder for the considered shop.

Then, by the given data, we have:

X ~ Pois(λ=3)

Evaluating each event's probability:

Case 1: At least 3 in a week.

[tex]P(X > 3) = 1- P(X \leq 2) = \sum_{i=0}^{2}P(X=i) = \sum_{i=0}^{2} \dfrac{3^ie^{-3}}{i!}\\\\P(X > 3) = 1 - e^{-3} \times \left( 1 + 3 + 9/2\right) \approx 1 - 0.4232 = 0.5768[/tex]

Case 2: At most 7 in a week.

[tex]P(X \leq 7) = \sum_{i=0}^{7}P(X=i) = \sum_{i=0}^{7} \dfrac{3^ie^{-3}}{i!}\\\\P(X \leq 7) = e^{-3} \times \left( 1 + 3 + 9/2 + 27/6 + 81/24 + 243/120 + 729/720 + 2187/5040\right)\\\\P(X \leq 7) \approx 0.9881[/tex]

Case 3: More than 20 in a month(4 weeks)

That means more than 5 in a week on average.

[tex]P(X > 5) = 1- P(X \leq 5) =\sum_{i=0}^{5}P(X=i) = \sum_{i=0}^{5} \dfrac{3^ie^{-3}}{i!}\\\\P(X > 5) = 1- e^{-3}( 1 + 3 + 9/2 + 27/6 + 81/24 + 243/120)\\\\P(X > 5) \approx 1 - 0.9161 \\ P(X > 5) \approx 0.0839[/tex]

Thus, the probability of the selling the video recorders for considered cases are:

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Answer: x=52.6°

Step-by-step explanation:

To find the value of x, we have to use our SOHCAHTOA. We can eliminate sine and cosine because both uses hypotenuse, which is not labelled. Therefore, we use tangent.

[tex]tan(x)=\frac{17}{13}[/tex]

To find x, we want to use inverse tangent.

[tex]x=tan^{-1}(\frac{17}{13} )[/tex]      [plug into calculator]

[tex]x=52.6[/tex]

Now, we know that x=52.6°.

Step-by-step explanation:

well, what does the word "constant" tell you ?

e.g. "this is a constant reminder of ..."

a constant is steady and unchanging. always the same.

so, what could be the constant part/term in the expression ?

-3xy ? is that always the same value ? no matter what values you assign to x, y (and whatever other variables there might be in the system)?

or

10 ? is that always the same value, no matter what values are assigned to x, y, ... ?

there are no other parts/terms I can see here.

so, please use your common sense and pick the right one. you can do that !

this is so simple. to outright write the answer to this feels like an offense. also against your own intelligence.

Answer:

Kindly check explanation

Step-by-step explanation:

Given the data :

Car 1 Car 2

214 220

245 221

239 244

224 225

220 258

295 259

Ordered data:

Car 1 : 214, 220, 224, 239, 245, 295

Sample mean = ΣX/ n ; n = sample size = 6

Sample mean = 1437 / 6 = 239.5

Median = 1/2(n+1)th term = 1/2(7) = 3.5th term

Median = (3rd + 4th) /2 = (224 + 239) /2 = 231.5

Sample standard deviation; √(Σ(x - xbar)²/n-1 ) = 29.60 (using calculator)

Car 2 : 220, 221, 225, 244, 258, 259

Sample mean = ΣX/ n ; n = sample size = 6

Sample mean = 1427 / 6 = 237.833

Median = 1/2(n+1)th term = 1/2(7) = 3.5th term

Median = (3rd + 4th) /2 = (225 + 244) /2 = 234.5

Sample standard deviation; √(Σ(x - xbar)²/n-1 ) = 18.21 (using calculator)

she saved $162

hope it helps

Answer:

11.75

Step-by-step explanation:

The required triangle is attached below :

The triangle AFE has it's by the mid segment as BD ;as B is the mid-point of line EA ; and D is the mid-point of line FA ;

HENCE, The Length of the midsegment BD = 1/2FE

Hence, BD =. 1/2 * 23.5

BD = 23.5 / 2 = 11.75

Answer:

73.5 Percent ...........

Answer:

The closest percentage of shots you made is 75%. Please mark brainliest.

I believe the choices are:

60%

70%

75%

80%

Therefore the answer 75%

Step-by-step explanation:

59/80 = 0.7375

Rounded up is 0.75

0.75 x 100 = 75%

Hope this helps.

Have a nice day amazing person there.

MAY GOD RICHLY BLESS YOU!!

Answer:

0.0193 = 1.93% probability that there is equipment damage to the payload of at least one of five independently dropped parachutes.

Step-by-step explanation:

For each parachute, there are only two possible outcomes. Either there is damage, or there is not. The probability of there being damage on a parachute is independent of any other parachute, which means that the binomial probability distribution is used to solve this question.

To find the probability of damage on a parachute, the normal distribution is used.

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

In which [tex]C_{n,x}[/tex] is the number of different combinations of x objects from a set of n elements, given by the following formula.

[tex]C_{n,x} = \frac{n!}{x!(n-x)!}[/tex]

And p is the probability of X happening.

Normal Probability Distribution

Problems of normal distributions can be solved using the z-score formula.

In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the z-score of a measure X is given by:

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Probability of a parachute having damage.

The opening altitude actually has a normal distribution with mean value 185 and standard deviation 32 m, which means that [tex]\mu = 185, \sigma = 32[/tex]

Equipment damage will occur if the parachute opens at an altitude of less than 100 m, which means that the probability of damage is the p-value of  Z when X = 100. So

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

[tex]Z = \frac{100 - 185}{32}[/tex]

[tex]Z = -2.66[/tex]

[tex]Z = -2.66[/tex] has a p-value of 0.0039.

What is the probability that there is equipment damage to the payload of at least one of five independently dropped parachutes?

0.0039 probability of a parachute having damage, which means that [tex]p = 0.0039[/tex]

5 parachutes, which means that [tex]n = 5[/tex]

This probability is:

[tex]P(X \geq 1) = 1 - P(X = 0)[/tex]

In which

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

[tex]P(X = 0) = C_{5,0}.(0.0039)^{0}.(0.9961)^{5} = 0.9807[/tex]

Then

[tex]P(X \geq 1) = 1 - P(X = 0) = 1 - 0.9807 = 0.0193[/tex]

0.0193 = 1.93% probability that there is equipment damage to the payload of at least one of five independently dropped parachutes.

Answer:

a) 0.1287 = 12.87% probability the sample will contain exactly 8 defective drives

b) 0.2092 = 20.92% probability the sample will contain more than 8 defective drives.

c) 0.6621 = 66.21% probability the sample will contain less than 8 defective drives.

d) The expected number of defective drives in the sample is 6.6

Step-by-step explanation:

For each DVD, there are only two possible outcomes. Either it is defective, or it is not. The probability of a DVD being defective is independent of any other DVD, which means that the binomial probability distribution is used to solve this question.

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

In which [tex]C_{n,x}[/tex] is the number of different combinations of x objects from a set of n elements, given by the following formula.

[tex]C_{n,x} = \frac{n!}{x!(n-x)!}[/tex]

And p is the probability of X happening.

A company that produces DVD drives has a 12% defective rate.

This means that [tex]p = 0.12[/tex]

Let X represent the number of defectives in a random sample of 55 of their drives.

This means that [tex]n = 55[/tex]

a. What is the probability the sample will contain exactly 8 defective drives?

This is [tex]P(X = 8)[/tex]. So

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

[tex]P(X = 8) = C_{55,8}.(0.12)^{8}.(0.88)^{47} = 0.1287[/tex]

0.1287 = 12.87% probability the sample will contain exactly 8 defective drives.

b. What is the probability the sample will contain more than 8 defective drives?

This is:

[tex]P(X > 8) = 1 - P(X \leq 8)[/tex]

In which:

[tex]P(X \leq 8) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6) + P(X = 7) + P(X = 8)[/tex]

Then

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

[tex]P(X = 0) = C_{55,0}.(0.12)^{0}.(0.88)^{55} = 0.0009[/tex]

[tex]P(X = 1) = C_{55,1}.(0.12)^{1}.(0.88)^{54} = 0.0066[/tex]

[tex]P(X = 2) = C_{55,2}.(0.12)^{2}.(0.88)^{53} = 0.0244[/tex]

[tex]P(X = 3) = C_{55,3}.(0.12)^{3}.(0.88)^{52} = 0.0588[/tex]

[tex]P(X = 4) = C_{55,4}.(0.12)^{4}.(0.88)^{51} = 0.1043[/tex]

[tex]P(X = 5) = C_{55,5}.(0.12)^{5}.(0.88)^{50} = 0.1450[/tex]

[tex]P(X = 6) = C_{55,8}.(0.12)^{6}.(0.88)^{49} = 0.1648[/tex]

[tex]P(X = 7) = C_{55,7}.(0.12)^{7}.(0.88)^{48} = 0.1573[/tex]

[tex]P(X = 8) = C_{55,8}.(0.12)^{8}.(0.88)^{47} = 0.1287[/tex]

So

[tex]P(X \leq 8) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6) + P(X = 7) + P(X = 8) = 0.0009 + 0.0066 + 0.0244 + 0.0588 + 0.1043 + 0.1450 + 0.1648 + 0.1573 + 0.1287 = 0.7908[/tex]

[tex]P(X > 8) = 1 - P(X \leq 8) = 1 - 0.7908 = 0.2092[/tex]

0.2092 = 20.92% probability the sample will contain more than 8 defective drives.

c. What is the probability the sample will contain less than 8 defective drives?

This is:

[tex]P(X < 8) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6) + P(X = 7)[/tex]

With the values we found in b.

[tex]P(X < 8) = 0.0009 + 0.0066 + 0.0244 + 0.0588 + 0.1043 + 0.1450 + 0.1648 + 0.1573 = 0.6621[/tex]

0.6621 = 66.21% probability the sample will contain less than 8 defective drives.

d. What is the expected number of defective drives in the sample?

The expected value of the binomial distribution is:

[tex]E(X) = np[/tex]

In this question:

[tex]E(X) = 55(0.12) = 6.6[/tex]

The expected number of defective drives in the sample is 6.6

Answer:

279+x

Step-by-step explanation:

Emily + Yani + Joyce=3209 stickers

how many stickers does Emily have than Joyce:

(279+2x)-(x)

279+2x-x

=279+x

The pair of functions that are inverses of each other is B. f(x) = 2x - 9 and g(x) = x + 9/2.

To determine if two functions are inverses of each other, we need to check if the composition of the functions results in the identity function, which is f(g(x)) = x and g(f(x)) = x.

Let's analyze the given options:

A. f(x) = 5 + x and g(x) = 5 - x

To check if they are inverses, we compute f(g(x)) = f(5 - x) = 5 + (5 - x) = 10 - x, which is not equal to x. Similarly, g(f(x)) = g(5 + x) = 5 - (5 + x) = -x, which is also not equal to x. Therefore, these functions are not inverses.

B. f(x) = 2x - 9 and g(x) = x + 9/2

By calculating f(g(x)) and g(f(x)), we find that f(g(x)) = x and g(f(x)) = x, which means these functions are inverses of each other.

C. f(x) = 3 - 6 and g(x) = x + 6/2

Similar to option A, we compute f(g(x)) and g(f(x)), and find that they are not equal to x. Hence, these functions are not inverses.

D. f(x) = x/3 + 4 and g(x) = 3x - 4

After evaluating f(g(x)) and g(f(x)), we see that f(g(x)) = x and g(f(x)) = x. Therefore, these functions are inverses of each other.

In summary, the pair of functions that are inverses of each other is B. f(x) = 2x - 9 and g(x) = x + 9/2.

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Answer:

( 1/2 ; 1/2 ) and ( 1 ; 2 )

Step-by-step explanation:

y = 2x².............1

y = 3x-1............2

2x²=3x-1

2x²-3x+1 = 0

(2x-1)(x-1) = 0

x = 1/2 or x = 1

y = 1/2 or y = 2

Answer:

a. These four vectors are dependent because there are columns of 3 by 4 matrix with one free variable.

b. If one is a multiple of other

c. c1v1 + c20 = 0 has nontrivial solution.

Step-by-step explanation:

Any set of 4 or more vectors must be linearly dependent. The non trivial combination of vector may produce zero as the set is linearly dependent. The vector v1 and v2 will be dependent if one is the multiple of the other.

Step-by-step explanation:

simple formula application :

a1 = 8 + 3×(1-1) = 8

a2 = 8 + 3×(2-1) = 11

a3 = 8 + 3×(3-1) = 14

a4 = 8 + 3×(4-1) = 17

...

9514 1404 393

Answer:

Youngest to oldest:

Step-by-step explanation:

At 9% interest per year, the present value of 1 at age 20 is ...

  p(a) = 1.09^(a-20)

Adding the present values for the different ages, we get a total of about 2.35528984846. Dividing the inheritance by that amount gives the multiplier for each of the present value numbers. The result is the list of shares shown above. At age 20, each brother will inherit about 636,864.29.

__

Additional comment

This is the sort of question that suggests the use of a graphing calculator or spreadsheet for doing the tedious number crunching.

(We assume the bank pays 9% per year, rather than charges 9% per year.)

Answer:

Step-by-step explanation:

Answer:

a) 0.2

b) 0.0009

c) 0.0298

d) 0.0465 = 4.65% probability that the sample proportion is less than 0.15.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution

Problems of normal distributions can be solved using the z-score formula.

In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the z-score of a measure X is given by:

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]

20% of customers entering her store make a purchase.

This means that [tex]p = 0.2[/tex]

180 people

This means that [tex]n = 180[/tex]

a. What is the mean of the distribution of the sample proportion of customers making a purchase?

By the Central Limit Theorem, [tex]\mu = p = 0.2[/tex].

b) What is the variance of the sample proportion?

The standard deviation is:

[tex]s = \sqrt{\frac{0.2*0.8}{180}} = 0.0298[/tex]

Variance is the square of the standard deviation, so:

[tex]s^2 = (0.0298)^2 = 0.0009[/tex]

c) What is the standard error of the sample proportion?

As found in the previous item, 0.0298.

d) What is the probability that the sample proportion is less than 0.15?

This is the p-value of Z when X = 0.15. So

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

By the Central Limit Theorem

[tex]Z = \frac{X - \mu}{s}[/tex]

[tex]Z = \frac{0.15 - 0.20}{0.0298}[/tex]

[tex]Z = -1.68[/tex]

[tex]Z = -1.68[/tex] has a p-value of 0.0465.

0.0465 = 4.65% probability that the sample proportion is less than 0.15.

Answer:

Hello,

Answer A : 11.2 ≤ X ≤ 29.2

Step-by-step explanation:

[tex]Z=\dfrac{X-20.2}{4.5} \\\\X=4.5*Z+20.2\\\\For\ Z=-2, \ X=4.5*(-2)+20.2=11.2\\For\ Z=2, \ X=4.5*2+20.2=29.2\\[/tex]

Answer:

17.3

Step-by-step explanation:

14.4 x 1.2

= 17.28

= 17.3 ( approximately )

Answer:

m <5 = 71 degrees.

m <8 = 109 degrees.

Answer:

(b) 0.30 atm

Step-by-step explanation:

Given data

Initial pressure= 1.2atm

Initial volume= 1.0L

Final volume= 4.0L

Final pressure= ???

Let us apply the gas formula to find the Final pressure

P1V1= P2V2

Substitute

1.2*1= x*4

Divide both sides by 4

1.2/4= x

x= 0.3atm

Hence the final pressure is 0.3 atm

9514 1404 393

Answer:

  a) 16A -4B +C = 1

  b) 4A -2B +C = 0.94

  c) C = 1

Step-by-step explanation:

Substitute the x- and y-values into the general form equation.

a. A(-4)² +B(-4) +C = 1

  16A -4B +C = 1

__

b. A(-2)² +B(-2) +C = 0.94

  4A -2B +C = 0.94

__

c. A(0)² +B(0) +C = 1

  C = 1

_____

Additional comment

Solving these equations gives A=0.015, B=0.06, C=1. The quadratic is ...

  0.015x² +0.06x +1 = y

Answer:

1. cube

2. square pyramid

4. cone

5. cube

Answer:

5/8

Step-by-step explanation:

There are 5 people when 3 more join for a total of 8 people

5 candy bars divided by 8 people

Take the candy bars and divide by the people

5/8

Answer:

3043 (base 6)

Step-by-step explanation:

216      36    6    1

3           0      4  3

216* 3 = 648

6*4 = 24

1*3 = 3

648+24+3 = 675