What volume of a 0.1 M NaOH solution must be combined with 25 mL of 0.2 M HNO3 to reach a pH of 11.5?

Consider the following reaction. 2 A + B −→ P A table of initial rates with initial concentrations of A and B is given below. [A]o (M) [B]o (M) rate M s 0.2 0.1 1.2 × 10−5 0.4 0.1 2.4 × 10−5 0.4 0.2 9.2 × 10−5 0.1 0.1 6.0 × 10−6 What is the rate expression? (a) r = k[A] [B]2 (b) r = k[A] [B] (c) r = k[A]2 [B] (d) r = k[A]2 [B]2

A stock's returns have the following distribution:

Assume the risk-free rate is 2%. Calculate the stock's expected return, standard deviation, coefficient of variation, and Sharpe ratio. Do not round intermediate calculations. Round your answers to two decimal places.

Stock's expected return:   %

Standard deviation:   %

Coefficient of variation:

Sharpe ratio:

Expected Return: Discrete Distribution

A stock's return has the following distribution:

Calculate the stock's expected return. Round your answer to two decimal places.
10.3 %

Calculate the standard deviation. Round your answer to two decimal places.

???? %

I was able to answer the first question but not the second. Could you help please?

I need to verify my answers are correct.

A large operator of timeshare complexes requires anyone interested in making a purchase to first visit the site of interest. Historical data indicates that 55% of all potential purchasers select a day visit, 25% choose a one-night visit, and 20% opt for a two-night visit. In addition, 10% of day visitors ultimately make a purchase, 25% of night visitors make a purchase, and 20% of those visiting for two nights make a purchase. Suppose a visitor is randomly selected

(a) What is the probability that the visitor makes a purchase?

(my answer was 0.1 * 0.55 + 0.25 * 0.25 + 0.2 * 0.2 = 0.1575)

(b) What is the probability that the visitor visited for two nights given that they made a purchase.

(my answer was found using bayes theorem: (0.25 * 0.2)/0.1575 = 0.31746

(c) What is the probability that the visitor visited for one night given that they did not make a purchase?

(my answer was: (1 - 0.25)(0.25)/(1 - 0.1575) = 0.22255

Thanks for your help

1. Amy tosses 12 biased coins. Each coin comes up heads with probability 0.2. What is the probability that fewer than 3 of the coins come up heads?

Answer: 0.5583

2. Amy shoots 27000 arrows at a target. Each arrow hits the target (independently) with probability 0.2. What is the probability that at most 2 of the first 15 arrows hit the target?

Answer: 0.398

3. Amy tosses 19 biased coins. Each coin comes up heads with probability 0.1. What is the probability that more than 1 of the coins come up heads?

Answer: 0.5797

4. Amy shoots 49000 arrows at a target. Each arrow hits the target (independently) with probability 0.2. What is the probability that fewer than 3 of the first 12 arrows hit the target?

Answer: 0.5583

5. Amy rolls 16 8-sided dice. What is the probability that fewer than 1 of the rolls are 1s?

Answer: 0.1181

A city has built a bridge over a river and it decides to charge a toll to everyone who crosses. For one year, the city charges a variety of different tolls and records information on how many drivers cross the bridge. The city thus gathers information about elasticity of demand. If the city wishes to raise as much revenue as possible from the tolls, where will the city decide to charge a toll: in the inelastic portion of the demand curve, the elastic portion of the demand curve, or the unit elastic portion? Explain using elasticity and total revenue diagram

Which of the following regarding the recognition of contingencies is not correct?

IFRS guidance is built around a balance sheet perspective.

Both IFRS and U.S. GAAP require recognition of a contingent liability when it is both probable and can be reasonably estimated.

U.S. GAAP relies on an income statement perspective.

Only U.S. GAAP requires recognition of a contingent liability, called a provision under IFRS—and the associated contingent loss—when it is both probable and can be reasonably estimated.

Suppose the average size of a new house built in a certain county in 2014 was 2,275 square feet. A random sample of 25 new homes built in this county was selected in 2018. The average square footage was 2,189​, with a sample standard deviation of 227 square feet. Complete parts a and b.

a. Using α=0.02​, does this sample provide enough evidence to conclude that the average house size of a new home in the county has changed since​ 2014?

Determine the null and alternative hypotheses.

H0​:μ ▼ greater than or equals ≥ not equals ≠ less than or equals ≤ equals =

H1​:μ ▼ equals = not equals ≠ less than < greater than > ​(Type integers or decimals. Do not​ round.)

Determine the appropriate critical value. Select the correct choice below and fill in the answer box within your choice. ​(Round to three decimal places as​ needed.)

A. tα=

B. −tα=

C. tα/2 equals =

Calculate the appropriate test statistic.

t-x= ​(Round to two decimal places as​ needed.)

State the conclusion.

(Reject/Do not reject) H0. There is/is not sufficient evidence to conclude that the average house size of a new home in the county has (stayed the same/decreased/changed/increased) since 2014.

b. Determine the precise​ p-value for this test using Excel.

The​ p-value is . ​(Round to three decimal places as​ needed.)

A company has just built a new factory to manufacture their widgets with a method they believe should be more efficient than their old method on a daily basis. To compare they recorded the number of widgets produced by the old factory for the last 9 days before it was shutdown and the first 8 days of the new factory's production. The company has reason to believe that daily production of widgets does not follow the normal distribution and have requested the use of the Wilcoxon rank sum test.

What would be the correct hypothesis test assuming that μ₁ is the mean number produced by the old factory and μ₂ is the mean number produced by the new factory?

H₀: μ₁ = μ₂ vs. H_a: μ₁ > μ₂
H₀: μ₁ > μ₂ vs. H_a: μ₁ = μ₂
H₀: μ₁ = μ₂ vs. H_a: μ₁ < μ₂
H₀: μ₁ = μ₂ vs. H_a: μ₁ ≠ μ₂

What rank should the value 236 from the old factory data have?

Calculate μ_W

Calculate the test statistic W

What is the approximate p value? (Hint: treat W as normally distributed)