In 1999 the population of Austria was one-third the population of Nepal. At that time the number of people living in Austria was 8,100,000. How many people were living in Nepal

Answer:

X=4

for the graph, the point would be (4,0)

Answer:

3/4

Step-by-step explanation:

[tex]\dfrac{1}{4}+\dfrac{1}{2}=\dfrac{1}{4}+\dfrac{2}{4}=\dfrac{3}{4}[/tex]. Hope this helps!

Answer:3/4

Step-by-step explanation:

Here are my reason why 1/2 is greater than 2/6 or 1/6. And it won’t be 2/4 because then it would be 1/2 + 0 or 2/4 + 0.

So 1/4 + 1/2

= 1/4 + 2/4

= 3/4

Find the attachments for step by step solutions

Note: The .01 significance level is used

The management at Food Town Inc. is investigating potential correlations between error rates in price checking between regular priced and specially priced items. This relates to business and statistics. A detailed look at the data, and potentially a formal correlation test, can provide insights.

The subject of the question is related to statistics and business with a focus on quality control in a grocery chain. The quality control department at Food Town Inc. conducts a check comparing scanned prices to posted prices. In essence, management is interested in understanding any possible relationship between error rates for regularly priced and specially priced items. A simple way to understand this is through the concept of a correlation.

Correlation is a term in statistics used to indicate the degree to which two or more attributes or measurements on the same group of elements show a tendency to vary together. By looking at the data, especially the error rates (overcharge and undercharge) on regular and special priced items, one could check if there's a negative or positive correlation between these variables.

It seems at first glance that regularly priced items incur fewer errors overall than specially priced items, especially in overcharging. A statistical correlation test, such as the Pearson correlation, can further confirm or refute this observation.

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To estimate the true mean weight of the cream filling within 0.25 grams with 99% confidence, the quality control inspectors should sample approximately 384 donuts. This is calculated using statistical techniques for sample size determination.

In order to find out how many donuts the quality control inspectors should sample, we need to utilize the formula for sample size in statistics, which is n = (Z^2 * σ^2 * N) / E^2 . In this case, 'Z' is the Z-score which corresponds to the desired confidence level, σ is the standard deviation of the sample data, E is the desired margin of error, and N is the population size.

For a 99% confidence level, the Z-score is 2.58. Given the standard deviation(σ) is 1.5 grams, and the desired margin of error(E) is 0.25 grams, we can substitute these values into the equation to calculate the sample size(n).

Substituting these values: n = (2.58^2 * 1.5^2) / 0.25^2. n becomes approximately 384.

Therefore, the quality control inspectors should sample approximately 384 donuts to be 99% confident that their estimate of the true mean weight of cream filling is within 0.25 grams of the true mean.

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We have been given that the legs on a right triangle are 15.25 inches and 14.1 inches. We are asked to find the hypotenuse of the right triangle.

We will use Pythagoras theorem to solve our given problem.

[tex]\text{Hypotenuse}^2=\text{Leg}^2+\text{Leg}^2[/tex]

[tex]\text{Hypotenuse}^2=15.25^2+14.1^2[/tex]

[tex]\text{Hypotenuse}^2=232.5625+198.81[/tex]

[tex]\text{Hypotenuse}^2=431.3725[/tex]

Now we will take positive square root on both sides.

[tex]\sqrt{\text{Hypotenuse}^2}=\sqrt{431.3725}[/tex]

[tex]\text{Hypotenuse}=20.7695089012715946[/tex]

Upon rounding to nearest hundredth, we will get:

[tex]\text{Hypotenuse}\approx 20.77[/tex]

Therefore, the length of the hypotenuse is approximately 20.77 inches.

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A model relating a CEO's annual salary to firm's sales, market value, and profits is proposed. The equation takes a logarithmic form of sales and market value for the elasticity. Profits cannot take a logarithmic form due to the possibility of negative values.

The objective of this question is to build a model relating CEO's annual salary to firm sales, market value, and profits, using the data from CEOSAL2.RAW. The model would be a constant elasticity variety for the sales and market variables. The standard form of a constant elasticity model is ln(Y) = a + b1*ln(X1) + b2*ln(X2) + u, where Y represents the dependent variable (CEO's annual salary), X1 and X2 represent independent variables (firm sales and market value).

Now adding profits to the model, your equation would be: ln(Y) = a + b1*ln(X1) + b2*ln(X2) + b3*X3 + u, where X3 denotes the firm's profits. It is not in logarithmic form because profits can be negative, and the natural log of a negative number is undefined in real numbers.

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Answer:

Miranda may have made the mistake of mismatching

Step-by-step explanation:

See attached a rough sketch of Cross sections of rectangular and triangular pyramids

Miranda made the mistake of not properly identifying the cross section of a rectangular pyramid which has a cross section of a rectangle.

A triangular pyramid has a triangular cross section

Answer:

4:5

Step-by-step explanation:

reduce the 8 and 10 by dividing both by 2.

Answer:

largest= 23,049

smallest= 22,950

Answer:

(1/3)^2  m^2

Step-by-step explanation:

Because Area of square = A = x*x

here x = 1/3 m.

so A = (1/3 m)*(1/3 m) = (1/3)^2 * m^2

Answer:

c).b

Step-by-step explanation:

e2020

The result of the product [tex]\sqrt b \times \sqrt b =b[/tex] is b

The product is represented as:-

[tex]\sqrt b \times \sqrt b[/tex]

Multiply the roots

[tex]\sqrt b \times \sqrt b =\sqrt{b^2}[/tex]

Rewrite the above expression as:

[tex]\sqrt b \times \sqrt b =b^{\frac 12 \times 2}[/tex]

Multiply 1/2 and 2

[tex]\sqrt b \times \sqrt b =b^{ 1}[/tex]

Express b^1 as b

[tex]\sqrt b \times \sqrt b =b[/tex]

Hence, the result of product [tex]\sqrt b \times \sqrt b =b[/tex] is b

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Answer:

p value = 0.0000 < 0.05

We reject H0

Hence the claim is significant

Step-by-step explanation:

See attached image

Answer:

y = 4 cosine (x) + 3

Step-by-step explanation:

A curve crosses the y-axis at (0, 7)

This means that when x = 0, y = 7

We have that:

sin(0) = 0, cos(0) = 1.

Let's see which of the functions respect this condition:

y = 8 cosine (x) + 3

y(0) = 8cos(0) + 3 = 8 + 3 = 11. Incorrect.

y = 4 cosine (x) + 3

y(0) = 4cos(0) + 3 = 4 + 3 = 7. Correct.

y = 4 sine (x) + 3

y(0) = 4sin(0) + 3 = 0 + 3 = 3. Incorrect.

y = 8 sine (x) + 3

y(0) = 8sin(0) + 3 = 0 + 3 = 3. Incorrect.

Decreases to negative 1, and then increases again to 7.

This means that the amplitude is 7-(-1) = 8, which means that the term which multiplies the function is 8/2 = 4. From above, we have already seen that the answer is y = 4 cosine (x) + 3.

Answer:

Pretty sure its' b (y=4Cos(x)+3)

Step-by-step explanation:

Take the max minus the min, equals 8 then times 1/2 equals 4.

Since it starts at the max, that is a characteristic of cosine NOT sine.

P.s I know this is late, but for other people

Answer:

The car can go 25 miles on 1 gallon.

Step-by-step explanation:

300 divided by 12 equals 25. Therefore, the car can go 25 miles on 1 gallon.

Answer:

25 miles

Step-by-step explanation:

Let distance = x

Gallons Miles

12. 300

1. x

12x = 300

x = 300/12

x = 25 miles

A car can go 25 miles on 1 gallon.

The best approach to determine the average improvement in SAT scores for PrepIt! students is to calculate a confidence interval for the difference in two population means using the sample mean differences, option A.

To determine the average improvement in SAT scores for students who take the PrepIt! class, the best approach is to use the difference in sample means to calculate a confidence interval for a difference in two population means (or treatment effect), option A.

This involves taking the mean SAT score before the PrepIt! class and the mean SAT score after the class and computing the confidence interval for this difference. Doing this calculation will provide an interval estimate that shows the range of values within which the true mean difference in SAT scores, due to the PrepIt! class, is likely to fall. For instance, if the mean SAT score improvement is 29 points, we would use this value and the appropriate standard error and confidence level to calculate the confidence interval.

Answer:

1.

%K = 31.904%

%Cl = 28.930%

%O = 39.166%

2.

Hg = 80.69/ 200.59 = 0.40/0.2 = 2

S = 6.436/ 32.07 = 0.20/0.2 = 1

O = 12.87/ 16.00 = 0.80/0.2 = 4

Hg2SO4 is the empirical formula

Step-by-step explanation:

Answer:

None of the above. The correct answer is 1.47%, 1.10%.

Step-by-step explanation:

The first thing to do is to calculate the Expected return of Stock A and Stock B.

For A;

Probability. Return.

0.1.= 0.1 × 10% = 1.00%

0.2= 0.2 × 13% = 2.60%

0.2= 0.2× 12% = 2.40%.

0.3= 0.3 × 14% = 4.20%

0.2= 0.2 × 15% = 3.00%

Total = 13.20%.

For B;

Probability. Return.

0.1= 0.1 × 8% = 0.80%

0.2= 0.2 × 7% = 1.40%

0.2.= 0.2 × 6% = 1.20%

0.3.= 0.3 × 9% =2.70%

0.2.= 0.2 × 8%= 1.60%

Total = 7.70%.

Hence, the Expected return of Stock A and B = 13.20% and 7.70%. respectively.

Now, let us find the Standard deviation of Stock A and the Standard deviation of Stock B.

For A

For individual value of A, we use the following formula;

(A - Expected return of Stock A)^2 × probability.

For instance,

(10% - 13.20%)^2 × 0.1 =0.000102.

(13% - 13.20%)^2 × 0.2= 0.000001.

(12% - 13.20%)^2 × 0.2 = 0.000029.

(14% - 13.20%)^2 × 0.3 =0.000019.

(15% - 13.20%)^2 × 0.2 = 0.000065.

Which gives us the following values;

0.000102, 0.000001, 0.000029, 0.000019, 0.000065.

The next thing to do is to find the variance (that is the addition of all the values above) and the value for the square root of variance which is the standard deviation.

√( 0.000102 + 0.000001 + 0.000029 + 0.000019 + 0.000065) = 0.000216.

Thus, variance = 0.000216, square root of variance= 0.014697(1.47%).

For B;

We follow as the one above.

(B - Expected return of Stock A)^2 × probability.

We have values as;

0.000001, 0.000010, 0.000058, 0.000051, 0.000002.

√ ( 0.000001 + 0.000010 + 0.000058 +0.000051 + 0.000002).

= 0.011( 1.10%).

To find the standard deviations of stocks A and B, calculate the expected returns and then use the variance formula to determine the standard deviations by taking the square root of the variance for each stock.

The standard deviations of stocks A and B can be calculated using the probabilities and the returns given in the probability distribution. First, we calculate the expected return for each stock by multiplying the return by the probability of each state and summing it up. Then we use the formula for standard deviation, which is the square root of the expected value of the squared deviations from the mean.

For Stock A, we calculate the expected return (μA) and the variance (σA2) before finding the standard deviation (σA). The same steps are taken for Stock B to find its expected return (μB) and standard deviation (σB). Without the detailed calculations, we are unable to provide the exact values for the standard deviations, but by following these methods, you can determine which of the given options is correct for both stocks.

Answer:

Check the explanation

Step-by-step explanation:

Kindly check the attached image below to see the step by step explanation to the question above.

Answer:

d

Step-by-step explanation:

i did that already and got it right so i hope its not wrong for you

Answer:

A (52,2%, 55,8%)

Step-by-step explanation:

To get the confidence interval, we have to add the margin of error to the point estimator.

The middle of the interval is 54%, and the ME is 1.8%. Don't forget that this means ±1.8%!

54 - 1.8 = 52.2%

54 + 1.8 = 55.8%

So our confidence interval is (52,2%, 55,8%) and the answer is A.

The margin of error of 1.8% in the study indicates that the true percentage of patients with flu-like symptoms is likely between 52.2% and 55.8%.

The margin of error in the study from Florida is 1.8%.

This means that we can be confident that the true percentage of patients experiencing flu-like symptoms is within 1.8% of the observed 54%, above and below this value.

To calculate the confidence interval, we add and subtract the margin of error from the observed percentage. Therefore, the confidence interval would be from 54% - 1.8% to 54% + 1.8%, which is between 52.2% and 55.8%.

Answer:

Step-by-step explanation:

V=WHL

Answer:

I think V= 256

Step-by-step explanation:

Answer:

c

Step-by-step explanation:

Answer:

c

Step-by-step explanation:

Answer:

The answer is 2 i think

Step-by-step explanation:

I think thats where the gap is

Answer:

The probability that an elite athlete has a maximum oxygen uptake of at least 50 ml/kg is 0.9970.

Step-by-step explanation:

The random variable X can be defined as the amount of oxygen an athlete takes in.

The mean maximum oxygen uptake for elite athletes is, μ = 65 ml/kg.

The standard deviation is, σ = 5.3 ml/kg.

The random variable X is approximately normally distributed.

Now to compute probabilities of a normally distributed random variable, we first need to convert the value of X to a z-score.

[tex]z=\frac{x-\mu}{\sigma}[/tex]

The distribution of these z-scores is known as a Standard normal distribution, i.e. [tex]Z\sim N(0, 1)[/tex].

A normal distribution is a continuous distribution. So, the probability at a point on a normal curve is 0. To compute the exact probabilities we need to apply continuity correction.

Compute the probability that an elite athlete has a maximum oxygen uptake of at least 50 ml/kg as follows:

P (X ≥ 50) = P (X > 50 + 0.50)

                = P (X > 50.50)

                [tex]=P(\frac{x-\mu}{\sigma}>\frac{50.50-65}{5.3})[/tex]

                [tex]=P(Z>-2.74)\\=P(Z<2.74)\\=0.99693\\\approx 0.9970[/tex]

Thus, the probability that an elite athlete has a maximum oxygen uptake of at least 50 ml/kg is 0.9970.

Answer:15

Step-by-step explanation:

%change=(201.59-171.33)/201.59 x 100

%change=30.26/201.59 x 100

%change=0.15 x 100

%change=15

Answer:

9 bottles

Step-by-step explanation:

If Luther drinks 80% of the water bottles, then he has 100% - 80% = 20% of his bottles left. Therefore, we find that he has 45 * 0.20 = 9 bottles left.

Answer:

9

Step-by-step explanation:

45-(45x0.80)=9

Answer:

792 dollars

Step-by-step explanation:

20% of 990= 0.2*990=198

Subtracting this from the total price of the original ticket, you get 990-198=$792. Hope this helps!

Answer:

Step-by-step explanation:

Givens

Notice that the chain represents the radius of the circle trajectory, because the dog is running pulling the leash as far as it can go.

So, the radius of the circle is 18.6 feet long.

Now, the length of the circumference is

[tex]L=2 \pi r[/tex]

Replacing the radius, we have

[tex]L=2(3.14)(18.6)\\L=116.81 ft[/tex]

So, one lap is equal to 116.81 feet. But the dog ran 10 times, which means it ran a total distance of

[tex]d=10(116.81ft)=1168.1 \ ft[/tex]

Therefore, the dog ran 1,168.1 feet, approximately.

Answer:

[tex]z=\frac{28.5-35.2}{\sqrt{\frac{7.2^2}{40}+\frac{9.1^2}{40}}}}=-3.652[/tex]  

Now we can calculate the p value since we are conducting a bilateral test the p value would be:

[tex]p_v =2*P(z<-3.652)=0.00026[/tex]

Since the p value is very low we have enough evidence to reject the null hypothesis that the true means are equal at 5% of significance.

Step-by-step explanation:

Information provided

[tex]\bar X_{1}=28.5[/tex] represent the mean for Miami

[tex]\bar X_{2}=35.2[/tex] represent the mean for Baltimore

[tex]\sigma_{1}=7.2[/tex] represent the population standard deviation for Miami

[tex]\sigma_{2}=9.1[/tex] represent the population standard deviation for Baltimore

[tex]n_{1}=40[/tex] sample size selected for Miami

[tex]n_{2}=10[/tex] sample size selected for Baltimore

[tex]\alpha=0.05[/tex] represent the significance level

z would represent the statistic (variable of interest)

[tex]p_v[/tex] represent the p value for the test

Hypothesis to analyze

We want to check if the commiting times are different in the winter for the two cities, so then the system of hypothesis are:

Null hypothesis:[tex]\mu_{1} = \mu_{2}[/tex]

Alternative hypothesis:[tex]\mu_{1} \neq \mu_{2}[/tex]

Since we know the population deviations the statistic for this case is given by:

[tex]z=\frac{\bar X_{1}-\bar X_{2}}{\sqrt{\frac{\sigma^2_{1}}{n_{1}}+\frac{\sigma^2_{2}}{n_{2}}}}[/tex] (1)

Replacing the info provided we got:

[tex]z=\frac{28.5-35.2}{\sqrt{\frac{7.2^2}{40}+\frac{9.1^2}{40}}}}=-3.652[/tex]  

Now we can calculate the p value since we are conducting a bilateral test the p value would be:

[tex]p_v =2*P(z<-3.652)=0.00026[/tex]

Since the p value is very low we have enough evidence to reject the null hypothesis that the true means are equal at 5% of significance.