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LuckyWell [14K]

2 years ago

13

After eating at a restaurant, Mr. Jackson’s bill before tax is $42.50 The sales tax rate is 8%. Mr. Jackson decides to leave a 2

0% tip for the waiter based on the pre-tax amount. Part A. How much is the tax?

1 answer:

USPshnik [31]2 years ago

6 0

Answer:

$3.40

Step-by-step explanation:

Look at image above...

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-2b^2-5b+12 factor the trinomial

Bad White [126]

Answer: −(b+4)(2b−3)

Step-by-step explanation:

1. Factor out the negative sign.

−(2b^2+5b−12)

2. Split the second term in 2b^2+5b−12 into two terms.

−(2b^2+8b−3b−12)

3.Factor out common terms in the first two terms, then in the last two terms.

−(2b(b+4)−3(b+4))

4. Factor out the common term b+4b+4b+4.

−(b+4)(2b−3)

5 0

3 years ago

RST has vertices R(0,0), S(6,3), and 7(3,-3). R'S'T is the image of RST after adilation with center (0,0) and scale factor 1/3.

Snowcat [4.5K]

Answer:

That's why I love... Nestlé Crunch, AAAHHHHH

Step-by-step explanation:

4 0

2 years ago

Please help...<br> i rlly need help on this

melamori03 [73]

Answer:

3

Step-by-step explanation:

Since

3 : 2 = X : 2

Then we know

3/2 = X/2

Multiplying both sides by 2 cancels on the right

2 × (3/2) = (X/2) × 2

2 × (3/2) = X

Then solving for X

X = 2 × (3/2)

X = 3

Therefore

3 : 2 = 3 : 2

5 0

2 years ago

A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and

mr Goodwill [35]

Answer:

Bias for the estimator = -0.56

Mean Square Error for the estimator = 6.6311

Step-by-step explanation:

Given - A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and X2. The mean is estimated using the formula (3X1 + 4X2)/8.

To find - Determine the bias and the mean squared error for this estimator of the mean.

Proof -

Let us denote

X be a random variable such that X ~ N(mean = 4.5, SD = 7.6)

Now,

An estimate of mean, μ is suggested as

$\mu = \frac{3X_{1} + 4X_{2} }{8}$

Now

Bias for the estimator = E(μ bar) - μ

= $E( \frac{3X_{1} + 4X_{2} }{8}) - 4.5$

= $\frac{3E(X_{1}) + 4E(X_{2})}{8} - 4.5$

= $\frac{3(4.5) + 4(4.5)}{8} - 4.5$

= $\frac{13.5 + 18}{8} - 4.5$

= $\frac{31.5}{8} - 4.5$

= 3.9375 - 4.5

= - 0.5625 ≈ -0.56

∴ we get

Bias for the estimator = -0.56

Now,

Mean Square Error for the estimator = E[(μ bar - μ)²]

= Var(μ bar) + [Bias(μ bar, μ)]²

= $Var( \frac{3X_{1} + 4X_{2} }{8}) + 0.3136$

= $\frac{1}{64} Var( {3X_{1} + 4X_{2} }) + 0.3136$

= $\frac{1}{64} ( [{3Var(X_{1}) + 4Var(X_{2})] }) + 0.3136$

= $\frac{1}{64} [{3(57.76) + 4(57.76)}] } + 0.3136$

= $\frac{1}{64} [7(57.76)}] } + 0.3136$

= $\frac{1}{64} [404.32] } + 0.3136$

= $6.3175 + 0.3136$

= 6.6311

∴ we get

Mean Square Error for the estimator = 6.6311

6 0

3 years ago

What is scarerude of 90

lora16 [44]

Assuming you meant square root of 90 it would be

90 equals 3 times the square root of 10
√90 = 3√10

4 0

2 years ago