A paint manufacturer made a modification to a paint to speed up its drying time. Independent simple random samples of 11 cans of type A (the original paint) and 9 cans of type B (the modified paint) were selected and applied to similar surfaces. The drying times, in hours, were recorded.
The summary statistics are as follows.
Type A Type B
x1 = 76.3 hrs x2 = 65.1 hrs
s1 = 4.5 hrs s2 = 5.1 hrs
n1 = 11 n2 = 9
The following 98% confidence interval was obtained for μ1 - μ2, the difference between the mean drying time for paint cans of type A and the mean drying time for paint cans of type B:
4.90 hrs < μ1 - μ2 < 17.50 hrs
What does the confidence interval suggest about the population means?

Answer:

y=n*pi, where n is an integer.

Step-by-step explanation:

We have the equation:

[tex]y'(t)=3sin(y)[/tex]

Which is autonomous because the independent variable does not appear explicitly.

Now, we are asked about all the equilibrium solutions for such autonomous equation. Let's remember that the solution equilibrium y=y0 when F(y0)=0.

Then, matching the given equation to zero, we have:

[tex]y'(t)=3sin(y)=0[/tex]

Which is fulfilled when the sine function has a value of zero. The sine function is worth zero in y=n*pi where n is an integer.

Answer:

Correct answer is (a). An online research company puts out a survey asking people two questions. First, it asks whether they buy groceries online. Second, it asks whether they own a car or not. The data are collected and recorded in a contingency table. The company wants to determine if there is a relationship between buying groceries online and car ownership

Step-by-step explanation:

The chi-square distribution can be used to perform the goodness-of-fit test, Closeness of observed data to expected data of the model.

Answer:

0.6915 is the probability that it will take more than 4 hours to process the orders of 100 people.

Step-by-step explanation:

We are given the following information in the question:

Mean, μ = 2.5 minutes

Standard Deviation, σ = 2 minutes

Since the sample size is large, by central limit theorem, the distribution of sample means is approximately normal.

[tex]P(\sum x_{i} > 4)\\P(\sum x_i > 4\times 60\text{ minutes})\\\\P(\displaystyle\frac{1}{100}\sum x_i > \frac{4\times 60}{100}\text{ minutes})\\\\P(\bar{x} > 2.4)[/tex]

Formula:  

[tex]z_{score} = \displaystyle\frac{x-\mu}{\frac{\sigma}{\sqrt{n}}}[/tex]  

P(it will take more than 4 hours to process the orders of 100 people)  

P(x > 2.4)  

[tex]P( x > 2.4) = P( z > \displaystyle\frac{2.4-2.5}{\frac{2}{\sqrt{100}}}) = P(z > -0.5)[/tex]  

Calculation the value from standard normal z table, we have,  [tex]P(x > 2.4) = 1 - 0.3085 = 0.6915= 69.15\%[/tex]

0.6915 is the probability that it will take more than 4 hours to process the orders of 100 people.

Final answer:

Using the Central Limit Theorem, the mean and standard deviation for 100 customers were calculated. The z-score was computed to find the probability from the standard normal distribution. The probability of taking more than 240 minutes to serve 100 customers is less than 0.5.

Explanation:

To solve this problem, we need to use the Central Limit Theorem which states that the sum or the average of a large number of independent and identically distributed random variables will be approximately normally distributed, irrespective of the original distribution of the variables.

Step by Step Solution:

First, we calculate the mean total time to process orders for 100 people. Mean total time = mean time per customer * number of customers = 2.5 minutes * 100 = 250 minutes.

Next, we calculate the standard deviation of the total time for 100 customers. Since the customer service times are independent, we can use the formula for the standard deviation of the sum of independent random variables: Standard deviation of the total time = standard deviation per customer * sqrt(number of customers) = 2 minutes * sqrt(100) = 20 minutes.

To find out if it will take more than 4 hours (which is 240 minutes), we find the z-score: z = (Total minutes to consider - Mean total time) / Standard deviation of total time = (240 - 250) / 20 = -0.5.

The probability of service taking more than 4 hours is the same as the probability of the sum being greater than 240 minutes. We use the z-score to find this probability from the standard normal distribution table or using a calculator with normal distribution functions.

Since a z-score of -0.5 corresponds to a probability higher than 0.5 (due to the symmetry of the normal distribution), the probability of taking less than 240 minutes is above 0.5. Therefore, the probability of taking more than 240 minutes is less than 0.5.

Based on the given data,  cannot conclude that the financial advisor's belief is accurate. Since the test statistic (Z-score) of 0.4483 is less than the critical value of 1.645, fail to reject the null hypothesis

To formulate a one-sample hypothesis test for a proportion, we can use the following steps:

Step 1: State the null hypothesis (H0) and the alternative hypothesis (H1).

Step 2: Determine the significance level (α) for the hypothesis test.

The significance level represents the probability of rejecting the null hypothesis when it is true.

Let's assume a significance level of α = 0.05, which is a commonly used value.

Step 3: Collect the data and calculate the test statistic.

[tex]\bar{p}= 21 / 33[/tex]

On dividing gives:

[tex]\bar{p}\approx 0.6364[/tex]

Standard Error (SE) [tex]= \sqrt{(\bar{p} * (1 -\bar{p})) / n}[/tex]

plugging given data gives:

[tex]= \sqrt{(0.6364 * (1 - 0.6364)) / 33}[/tex]

On simplifying gives:

≈ 0.0811

Step 4: Determine the critical value or the p-value.

To determine the critical value,  to use the Z-table . For a significance level of α = 0.05 (one-tailed),

the critical value corresponds to a z-score of approximately 1.645.

Step 5: Make a decision and interpret the results.

Calculate the test statistic (Z-score):

[tex]Z = (\bar{p} - p) / SE[/tex]

Plugging the given values gives:

[tex]= (0.6364 - 0.6) / 0.0811[/tex]

On simplifying gives:

[tex]\approx 0.4483[/tex]

Since the test statistic (Z-score) of 0.4483 is less than the critical value of 1.645, fail to reject the null hypothesis. There is not enough evidence to support the claim that the proportion of risk-averse investors is greater than 0.6 at a significance level of 0.05.

Therefore, based on the given data, we cannot conclude that the financial advisor's belief is accurate.

Learn more about null hypothesis here:

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Final answer:

The expectation of the time at which the plumber completes the project is 4:30 PM, and the variance is 901.33 [tex]minutes^2[/tex]

Explanation:

To find the expectation and variance of the time at which the plumber completes the project, we need to use the properties of the uniform and exponential distributions.

1. The arrival time of the plumber is uniformly distributed between 1 PM and 7 PM, which means it has a continuous uniform distribution. The expectation of a continuous uniform distribution is calculated as the average of the lower and upper bounds, so the expectation of the arrival time is (1 + 7) / 2 = 4 PM. The variance of a continuous uniform distribution is calculated as [tex](upper bound - lower bound)^2 / 12,[/tex] so the variance of the arrival time is [tex](7 - 1)^2 / 12 = 1.33.[/tex]

2. The time it takes to fix the broken faucet is exponentially distributed with a mean of 30 minutes, which means it has an exponential distribution. The expectation of an exponential distribution is equal to its mean, so the expectation of the fixing time is 30 minutes. The variance of an exponential distribution is equal to the square of its mean, so the variance of the fixing time is [tex]30^2 = 900 minutes^2[/tex].

Since the arrival time and fixing time are independent, the total time at which the plumber completes the project is the sum of the arrival time and fixing time. The expectation of the total time is the sum of the expectations, which is 4 PM + 30 minutes = 4:30 PM. The variance of the total time is the sum of the variances, which is 1.33 + 900 = 901.33 [tex]minutes^2.[/tex]

Answer: 10 guests, $250

Step-by-step explanation:

Use the equation y=mx+b

Plug in what you are given.

For caterer A the cost per guest is $15. This is the m value. The b value is the additional fee for setting up the tables, $100. Your equation should be y=15x+100.

Caterer B charges $20 per guest and a $50 fee to set up tables. The equation will be y=20x+50.

To find the number of guests that will result in an equal cost, set the equations equal to one another. Solve for x.

15x+100=20x+50

Do this by getting the x’s all on one side (I subtracted 15x on both sides). Then subtract the 50 from both sides. This results in the equation 50=5x. Dividing by 5 will make x equal to 10.

To find the cost at this number of guests plug 10 into either equation y=15x+100 or y=20x+50. This results in a cost of $250.

Answer: 10 guests, $250

Step-by-step explanation:

Use the equation y=mx+b

Plug in what you are given.

For caterer A the cost per guest is $15. This is the m value. The b value is the additional fee for setting up the tables, $100. Your equation should be y=15x+100.

Caterer B charges $20 per guest and a $50 fee to set up tables. The equation will be y=20x+50.

To find the number of guests that will result in an equal cost, set the equations equal to one another. Solve for x.

15x+100=20x+50

Do this by getting the x’s all on one side (I subtracted 15x on both sides). Then subtract the 50 from both sides. This results in the equation 50=5x. Dividing by 5 will make x equal to 10.

Answer:

14

Step-by-step explanation:

Given that a package delivery company purchased 14 trucks at the same time. Five trucks were purchased from manufacturer A, four from manufacturer B, and five from manufacturer C. The cost of maintaining each truck was recorded.

No of companies  = 3

No of items in total = 14

Total df = 17-1=16

degrees of freedom between groups = 3-1 =2

Hence degrees of freedom for denominator

=[tex]16-2 =14[/tex]

Answer:

[tex]H_{0}: \mu \leq 40,000\text{ miles}\\H_A: \mu > 40,000\text{ miles}[/tex]

Step-by-step explanation:

We are given the following in the question:

Population mean, μ = 40,000 miles

Management believes that due to a new production process, the life expectancy of their tires has increased.

We design the null and the alternate hypothesis in the following manner:

[tex]H_{0}: \mu \leq 40,000\text{ miles}\\H_A: \mu > 40,000\text{ miles}[/tex]

The null hypothesis states before the new production process life expectancy of tires is equal to or less than 40,000 miles, while the alternate hypothesis states that after the new production process life expectancy of tires is greater than 40,000 miles.

Option D)

[tex]H_{0}: \mu \leq 40,000\text{ miles}\\H_A: \mu > 40,000\text{ miles}[/tex]

Final answer:

The correct hypotheses for checking if the average life expectancy of tires has increased are : μ ≤ 40,000 and : μ > 40,000, where μ represents the mean mileage for the population of tires. Option d) is the correct answer.

Explanation:

The correct set of hypotheses for testing whether the average life expectancy of tires produced by Whitney Tire Company has increased due to a new production process is:

μ ≤ 40,000 (null hypothesis)

μ > 40,000 (alternative hypothesis)

This hypothesis testing is set up to check if there is evidence to support the claim that the average life expectancy of the tires is greater than 40,000 miles. Thus, option d. : μ ≤ 40,000 : μ > 40,000 is correct. Here, μ stands for the mean mileage for the population of tires. The null hypothesis always contains an equality claim (≤, =, ≥), and the alternative hypothesis contains the opposite inequality, representing the claim we are trying to find evidence for.

Step-by-step explanation:

For an alternating series ∑a(n), the error bound of the sum of the first k terms is:

E < | a(k+1) |

Here, k = 10:

E < | a(11) |

E < | (-1)¹¹⁺¹ × 1 / (4(11) + 3) |

E < | 1/47 |

E < 1/47

Answer:

Step-by-step explanation:

Given

Economic system Q is given by

[tex]Q=cL^aK^b[/tex]

also [tex]a+b=1[/tex]

if [tex]Q=12,200[/tex]

[tex]a=\frac{1}{6}[/tex]

[tex]b=\frac{5}{6}[/tex]

[tex]c=42[/tex]

substitute these values

[tex]12,200=42\times (L)^{\frac{1}{6}}K^{\frac{5}{6}}[/tex]

[tex](L)^{\frac{1}{6}}K^{\frac{5}{6}}=\frac{12,200}{42}[/tex]

[tex]K^{\frac{5}{6}}=\frac{12,200}{42(L)^{\frac{1}{6}}}[/tex]

[tex]K=(\frac{12,200}{42})^{\frac{6}{5}}\times \frac{1}{L^{5}}[/tex]

differentiate w.r.t to L to get [tex]\frac{dK}{dL}[/tex]

[tex]\frac{dK}{dL}=(\frac{12,200}{42})^{\frac{6}{5}}\times (-5)\times L^{-6}[/tex]

[tex]\frac{dK}{dL}=-5(\frac{12,200}{42})^{\frac{6}{5}}\times \frac{1}{L^6}[/tex]

[tex]\frac{dK}{dL}=-\frac{4515.466}{L^6}[/tex]

To test if mothers spend more time driving kids than fathers, use t-distribution due to normality assumption and potentially unequal variances.

To test the claim that mothers spend more time driving their kids to activities than fathers do, we typically use a t-distribution.

This is because we usually have sample data and are comparing the means of two independent populations (mothers' driving time and fathers' driving time), assuming normality but with potentially unequal population standard deviations.

Therefore, the correct choice is option B. t-distribution.

Answer:

False

Step-by-step explanation:

Midrange is the average of the difference between the maximum and minimum

Final answer:

The midrange, being the average of the maximum and minimum, is not robust to outliers, as it can be significantly affected by extreme values in the data. Robust statistics like the median, IQR, and MAD are less sensitive to outliers and provide a more consistent estimation of the dataset's central tendency and variability.

Explanation:

The statement that the midrange is defined as the average of the maximum and minimum is correct. However, the claim that this statistic is robust to outliers is false. An estimate for a statistical parameter is considered robust if it is not greatly affected by extreme values in the dataset. Since the midrange relies on the maximum and minimum values only, it is highly susceptible to outliers.

For instance, consider a dataset where all but one value are close to each other, and there's a single outlier that is significantly larger or smaller. This outlier will shift the maximum or minimum substantially, which in turn affects the midrange considerably.

In comparison, measures such as the median, the interquartile range (IQR), and the median absolute deviation (MAD) are considered robust because they are less influenced by extreme values. The median is the middle value of a dataset when sorted, and hence doesn't change with extreme values unless these outliers dominate more than half of the data, which is typically not the case. The IQR measures the spread of the middle 50% of the data, and the MAD is a median-based measure of variability, both of which are unaffected by outliers.

Answer:

P-value = 0.1515

Step-by-step explanation:

We are given the following in the question:

Population mean, μ = 2.2

Sample mean, [tex]\bar{x}[/tex] = 2

Sample size, n = 52

Alpha, α = 0.05

Population standard deviation, σ = 1.4

First, we design the null and the alternate hypothesis

[tex]H_{0}: \mu =2.2\\H_A: \mu < 2.2[/tex]

We use one-tailed(left) z test to perform this hypothesis.

Formula:

[tex]z_{stat} = \displaystyle\frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}} }[/tex]

Putting all the values, we have

[tex]z_{stat} = \displaystyle\frac{2 - 2.2}{\frac{1.4}{\sqrt{52}} } = -1.03[/tex]

Now, we calculate the p-value from the normal standard table.

P-value = 0.1515

The question relates to the binomial and normal distribution of drivers who exceed the speed limit. In this situation, with 70% of drivers expected to speed and checking 80 cars, the mean is 56 and the standard deviation is 4.1. Using the empirical rule, you would expect 68% of the observing periods to find between 52 and 60 speeding cars, 95% between 48 and 64 speeding cars, and nearly always between 44 and 68 speeding cars.

The subject of this question is probability and it's about the binomial distribution, particularly under the umbrella of normal approximation. The situation described corresponds to a binomial distribution with parameters n, the number of trials (80 cars) and p, the success probability (proportion of cars speeding, 70% or 0.7).

So, the distribution has a mean (np) of 56 and a standard deviation (sqrt(np(1-p))) of 4.1. Using the 68-95-99.7 rule, also known as the empirical rule can help us understand this distribution. This rule states that in a normal distribution:

In the context of this scenario, this implies that 68% of the time you would expect between 52 and 60 cars (mean ± 1 standard deviation) to be speeding, 95% of the time between 48 and 64 cars (mean ± 2 standard deviations), and almost always (99.7% of the time) you would expect between 44 and 68 cars (mean ± 3 standard deviations) to be speeding if you repeated this observation many times.

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Answer:

Given definite  integral as a limit of Riemann sums is:

[tex] \lim_{n \to \infty} \sum^{n} _{i=1}3[\frac{9}{n^{3}}i^{3}+\frac{36}{n^{2}}i^{2}+\frac{97}{2n}i+22][/tex]

Step-by-step explanation:

Given definite integral is:

[tex]\int\limits^7_4 {\frac{x}{2}+x^{3}} \, dx \\f(x)=\frac{x}{2}+x^{3}---(1)\\\Delta x=\frac{b-a}{n}\\\\\Delta x=\frac{7-4}{n}=\frac{3}{n}\\\\x_{i}=a+\Delta xi\\a= Lower Limit=4\\\implies x_{i}=4+\frac{3}{n}i---(2)\\\\then\\f(x_{i})=\frac{x_{i}}{2}+x_{i}^{3}[/tex]

Substituting (2) in above

[tex]f(x_{i})=\frac{1}{2}(4+\frac{3}{n}i)+(4+\frac{3}{n}i)^{3}\\\\f(x_{i})=(2+\frac{3}{2n}i)+(64+\frac{27}{n^{3}}i^{3}+3(16)\frac{3}{n}i+3(4)\frac{9}{n^{2}}i^{2})\\\\f(x_{i})=\frac{27}{n^{3}}i^{3}+\frac{108}{n^{2}}i^{2}+\frac{3}{2n}i+\frac{144}{n}i+66\\\\f(x_{i})=\frac{27}{n^{3}}i^{3}+\frac{108}{n^{2}}i^{2}+\frac{291}{2n}i+66\\\\f(x_{i})=3[\frac{9}{n^{3}}i^{3}+\frac{36}{n^{2}}i^{2}+\frac{97}{2n}i+22][/tex]

Riemann sum is:

[tex]= \lim_{n \to \infty} \sum^{n} _{i=1}3[\frac{9}{n^{3}}i^{3}+\frac{36}{n^{2}}i^{2}+\frac{97}{2n}i+22][/tex]

To express the integral ∫7 to 4 x / 2 + x^3 dx as a limit of Riemann sums using right endpoints, divide the interval [4, 7] into n subintervals of equal width. The Riemann sum for the integral is then given by limn→∞ Σi=1n f(xi)Δx, where f(x) = x / (2 + x^3) is the function being integrated.

To express the integral ∫7 to 4 x / 2 + x^3 dx as a limit of Riemann sums using right endpoints, we divide the interval [4, 7] into n subintervals of equal width. The width of each subinterval is given by Δx = (b - a) / n, where a = 4 and b = 7. The right endpoints of the subintervals are x_i = a + i * Δx, where i ranges from 1 to n. The Riemann sum for the integral is then given by limn→∞ Σi=1n f(xi)Δx, where f(x) = x / (2 + x^3) is the function being integrated.

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Answer:

n=50

Step-by-step explanation:

1) Previous concepts

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".

The margin of error is the range of values below and above the sample statistic in a confidence interval.

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

[tex]\bar X=16.10[/tex] represent the sample mean for the sample  

[tex]\mu[/tex] population mean (variable of interest)

s=1.8 represent the sample standard deviation

n=25 represent the sample size  

2) Solution to the problem

The confidence interval for the mean is given by the following formula:

[tex]\bar X \pm t_{\alpha/2}\frac{s}{\sqrt{n}}[/tex]   (1)

We need to find the degrees of freedom given by:

[tex]df=n-1=25-1=24[/tex]

Since the Confidence is 0.95 or 95%, the value of [tex]\alpha=0.05[/tex] and [tex]\alpha/2 =0.025[/tex], and we can use excel, a calculator or a table to find the critical value. The excel command would be: "=-NORM.INV(0.025,0,1)".And we see that [tex]z_{\alpha/2}=\pm 1.96[/tex]

Since we assume that we are taking a bigger sample then we can replace the t distribution with the normal standard distribution, and we can assume that th population deviation is 1.8. The margin of error is given by this formula:

[tex] ME=z_{\alpha/2}\frac{\sigma}{\sqrt{n}}[/tex]    (a)

And on this case we have that ME =0.5 and we are interested in order to find the value of n, if we solve n from equation (a) we got:

[tex]n=(\frac{z_{\alpha/2} \sigma}{ME})^2[/tex]   (b)

Replacing into formula (b) we got:

[tex]n=(\frac{1.96(1.8)}{0.5})^2 =49.79 \approx 50[/tex]

So the answer for this case would be n=50 rounded up to the nearest integer

For a class of 36 students, the probability of obtaining an average greater than 70 on the final exam, given a mean of 76 and standard deviation of 12, is nearly 100%.

Given:

Mean[tex](\(\mu\))[/tex]of scores = 76

Standard deviation [tex](\(\sigma\))[/tex] of scores = 12

Sample size[tex](\(n\))[/tex] = 36

Sample mean[tex](\(\bar{x}\))[/tex] to find the probability for = 70

Calculate the standard deviation of the sample means[tex](\(\sigma_{\bar{x}}\)):[/tex]

[tex]\(\sigma_{\bar{x}} = \frac{\sigma}{\sqrt{n}} = \frac{12}{\sqrt{36}} = \frac{12}{6} = 2\)[/tex]

Compute the z-score for the sample mean of 70:

[tex]\[z = \frac{\bar{x} - \mu_{\bar{x}}}{\sigma_{\bar{x}}}\][/tex]

[tex]\[z = \frac{70 - 76}{2}\][/tex]

[tex]\[z = \frac{-6}{2}\][/tex]

[tex]\[z = -3\][/tex]

Find the probability using the z-score:

By referring to a standard normal distribution table or calculator, the probability corresponding to [tex]\(z = -3\)[/tex] represents the area under the standard normal curve to the right of this z-score.

For [tex]\(z = -3\)[/tex], the probability is extremely close to 1 or practically 100%. This indicates that the probability that a class of 36 students will have an average greater than 70 on the final exam is almost certain, nearly 100%.

complete the question

Professor Elderman has given the same multiple-choice final exam in his Principles of Microeconomics class for many years. After examining his records from the past 10 years, he finds that the scores have a mean of 76 and a standard deviation of 12.

What is the probability that a class of 36 students will have an average greater than 70 on Professor Elderman’s final exam?

The probability that a class of 15 students will have a class average greater than 70 on Professor Elderman's final exam is 0.9738. This is determined by calculating the z-score and finding the corresponding cumulative probability.

Probability Calculation

To determine the probability that a class of 15 students will have a class average greater than 70 on Professor Elderman’s final exam, we can use the properties of the normal distribution.

Step-by-Step Explanation

Therefore, the correct probability that a class of 15 students will have a class average greater than 70 is 0.9738.

Answer:

Cost of the bonds      = 4600 $

Total annual interest = 350 $

Yield                            = 7.6 %

Step-by-step explanation:

Fred purchased the bonds for 1000 $ at 92. 1000 $ is the face value of the bond. That means the actual value of the bond = 92 % of 1000.

⇒ Actual value of the bond = [tex]$ \frac{92}{100} \times 1000 $[/tex]

= 920 $

Since, Fred purchased five bonds, the total cost of the bonds = 920 X 5

= 4600 $

Actual cost of the bond = 4600 $

Total interest on the bond = Rate on the bond X Face value of the bond

= [tex]$ \frac{7}{100} \times 1000 $[/tex] = 70 $

Interest on all the 5 bonds = 70 X 5 = 350 $

Total interest on the bond = 350 $

Yield is the amount got as returns on the bond.

We have the following formula to calculate yield.

Yield = [tex]$ \frac{Interest \hspace{2mm} earned}{Total \hspace{2mm} amount \hspace{2mm} paid} $[/tex]

∴ Yield = [tex]$ \frac{350}{4600} \times 100 = 0. 076 \times 100 = 7.6\% $[/tex]

Therefore, Yield = 7.6%

Answer:

4600 $

350 $

7.6 %

Step-by-step explanation:

The 99% confidence interval for the difference between the population mean total cholesterol levels for vegans and omnivores is calculated to be -0.450 ± 0.445, indicating we can be 99% confident that the true total cholesterol level difference between the two populations lies within this interval.

To calculate a 99% confidence interval (CI) for the difference between the population mean total cholesterol level for vegans and omnivores, we apply the formula for the confidence interval for the difference between two means. The formula is: (x1 - x2) ± Z(α/2) * sqrt [ (s1^2/n1) + (s2^2/n2)], where x1 and x2 are the sample means, s1 and s2 are the standard deviations, and n1 and n2 are the sample sizes.

Here, we have for vegans (labelled as 1): x1 = 5.10, s1 = 1.05, n1 = 85. For omnivores (labelled as 2), we have: x2 = 5.55, s2 = 1.20, n2 = 96. The value of Z(α/2) for a 99% confidence interval is approximately 2.576.

Substituting these values into the formula, we find the 99% CI for the difference between the population means is: (5.10 - 5.55) ± 2.576 * sqrt [ (1.05^2/85) + (1.20^2/96)]. After calculation, the population mean total cholesterol level difference is -0.45 ± 0.445, which rounded to three decimal places is -0.450 ± 0.445. This means that we can be 99% confident that the true total cholesterol level difference between the two populations lies within this interval.

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Answer:

a)0.1714 b)0.504 c)0.5 d)0.2

Step-by-step explanation:

Total numbers between 1000 and 9999 (both inclusive)= 9000.

Probability=[tex]\frac{No.OfFavourableOutcomes}{TotalNo.OfOutcomes}[/tex]

a) Divisible by 5 and not by 7 means, that the number is divisible by 5 but not by 35.

Total numbers between 1000 and 9999 divisible by 5= 1800

Total numbers between 1000 and 9999 divisible by 35=257

Total numbers between 1000 and 9999 divisible by 5 but not by 35=         1800 - 257= 1543

Probability=[tex]\frac{1543}{9000}[/tex]=0.1714

b) Finding the number of numbers with distinct digits,

We can find this by the method that how many digits can be placed in each place,

In the thousands place=9( 0 cannot be placed)

In the hundreds place=9( The digit inserted in the thousands place, cannot be put but 0 can be put now)

In the tens place=8( Numbers placed in hundreds and thousands place cannot be put)

In the ones place=7(The rest 3 numbers can't be put)

Total Favorable Outcomes=9 x 9 x 8 x 7 =4536

Probability=[tex]\frac{4536}{9000}[/tex]=[tex]\frac{63}{125}[/tex]=0.504

c) Total number of even numbers between 1000 and 9999= 4500

Probability=[tex]\frac{4500}{9000}[/tex]=[tex]\frac{1}{2}[/tex]=0.5

d) Total numbers between 1000 and 9999 divisible by 5= 1800

Probability= [tex]\frac{1800}{9000}[/tex]=[tex]\frac{1}{5}[/tex]=0.2

Answer:

0

Step-by-step explanation:

8+3=11.

11-11=0

So the answer is zero.

Hope that helps!

The value of y when x = 5 is 45

Given that y varies directly with x. This can be written mathematically as:

y  ∝  x

[tex]y = k \times x[/tex]  ---- eqn 1

Where "k" is the constant of propotionality

Given y = 27 when x = 3. Substitute these values in eqn 1

[tex]27 = k \times 3\\\\k = \frac{27}{3} = 9[/tex]

Substitute k = 9 in eqn 1

y = 9x ----- eqn 2

To find: y = ? and x = 5

Substitute x = 5 in eqn 2

[tex]y = 9 \times 5 = 45[/tex]

Thus the value of y when x = 5 is 45

Final answer:

To find the height of the car, we can rearrange the equation for potential energy and solve for h. Using the given values of 200 J for potential energy and 300 N for weight, the height of the car is approximately 0.683 meters.

Explanation:

To calculate the height of a car that weighs 300 Newtons and has 200 joules of gravitational potential energy, we can use the formula for potential energy, PE = mgh, where m is the mass of the car, g is the acceleration due to gravity, and h is the height. Since we know the potential energy and the weight (force) of the car, we can rearrange the formula to solve for h. Here's how:

Therefore, the height of the car is approximately 0.683 meters.

Answer:

[tex] 0.609 \leq \sigma \leq 0.772[/tex]  

And the best conclusion would be:

D. This conclusion is safe because 1.40 °F is outside the confidence interval.

Step-by-step explanation:

1) Data given and notation  

s=0.68 represent the sample standard deviation  

[tex]\bar x =98.90[/tex] represent the sample mean  

n=98 the sample size  

Confidence=90% or 0.90  

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population mean or variance lies between an upper and lower interval".  

The margin of error is the range of values below and above the sample statistic in a confidence interval.  

The Chi Square distribution is the distribution of the sum of squared standard normal deviates .  

2) Calculating the confidence interval  

The confidence interval for the population variance is given by the following formula:  

[tex]\frac{(n-1)s^2}{\chi^2_{\alpha/2}} \leq \sigma^2 \leq \frac{(n-1)s^2}{\chi^2_{1-\alpha/2}}[/tex]  

The next step would be calculate the critical values. First we need to calculate the degrees of freedom given by:  

[tex]df=n-1=98-1=97[/tex]  

Since the Confidence is 0.90 or 90%, the value of [tex]\alpha=0.1[/tex] and [tex]\alpha/2 =0.05[/tex], and we can use excel, a calculator or a table to find the critical values.  

The excel commands would be: "=CHISQ.INV(0.05,97)" "=CHISQ.INV(0.95,97)". so for this case the critical values are:  

[tex]\chi^2_{\alpha/2}=120.990[/tex]  

[tex]\chi^2_{1- \alpha/2}=75.282[/tex]  

And replacing into the formula for the interval we got:  

[tex]\frac{(97)(0.68)^2}{120.990} \leq \sigma \leq \frac{(97)(0.68)^2}{75.282}[/tex]  

[tex] 0.371 \leq \sigma^2 \leq 0.596[/tex]  

Now we just take square root on both sides of the interval and we got:  

[tex] 0.609 \leq \sigma \leq 0.772[/tex]  

And the best conclusion would be:

D. This conclusion is safe because 1.40 °F is outside the confidence interval.

Answer:

P-value = 0.4324

Step-by-step explanation:

We are given the following in the question:

Population mean, μ = 8 ounces

Sample mean, [tex]\bar{x}[/tex] = 7.983 ounces

Sample size, n = 48

Population standard deviation, σ = 0.15 ounces

First, we design the null and the alternate hypothesis

[tex]H_{0}: \mu = 8\text{ ounces}\\H_A: \mu \neq 8\text{ ounces}[/tex]

We use Two-tailed z test to perform this hypothesis.

Formula:

[tex]z_{stat} = \displaystyle\frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}} }[/tex]

Putting all the values, we have

[tex]z_{stat} = \displaystyle\frac{7.983 - 8}{\frac{0.15}{\sqrt{48}} } = -0.7851[/tex]

Now, we calculate the p-value with the help of standard normal z table.

P-value = 0.4324

Final answer:

To calculate the P-Value, we need to perform a hypothesis test. The test statistic is calculated by subtracting the hypothesized mean from the sample mean and dividing by the standard error. The P-Value is the probability of observing a test statistic as extreme as the calculated value or more extreme under the null hypothesis.

Explanation:

To calculate the P-Value, we need to perform a hypothesis test. The null hypothesis, H0, is that the mean amount dispensed is equal to 8 ounces, while the alternative hypothesis, Ha, is that the mean amount dispensed is different from 8 ounces.

Using the given information, we can calculate the test statistic, which is the standardized value of the sample mean. First, we calculate the standard error of the sample mean, which is the standard deviation divided by the square root of the sample size. In this case, the standard error is 0.15 / sqrt(48) = 0.021.

The test statistic is then calculated by subtracting the hypothesized mean (8 ounces) from the sample mean (7.983 ounces) and dividing by the standard error. So, the test statistic is (7.983 - 8) / 0.021 = -0.619.

To calculate the P-Value, we need to find the probability of observing a test statistic as extreme as -0.619 (or more extreme) under the null hypothesis. We can use a standard normal distribution table or a calculator to find the corresponding area under the curve. From the table or calculator, we find that the area to the left of -0.619 is 0.269.

Since the alternative hypothesis is two-sided (the mean could be either greater or less than 8 ounces), we double the area to get the P-Value. So, the P-Value is 2 * 0.269 = 0.538.

Answer:

There is significant difference between the proportions

Step-by-step explanation:

Given that the two statistics classes were asked if A Christmas Story is the best holiday movie.

Class                I          II              Total

n                     40        50              90

x                     30        45               75

p=x/n              0.75     0.90            0.8333

H0: p1 =p2

H0: p1 ≠p2

(Two tailed test at 5% level)

p difference = -0.15

STd error for difference = [tex]\sqrt{pq/n} =\sqrt{0.8333*0.1667/90} \\=0.03727[/tex]

Z statistic = p difference/std error

=-4.0246

p <0.00001

Since p <0.05 we reject H0.  There is significant difference between the proportions

At 0.01 level also p is less than alpha So same conclusion

Rounded to 4 decimal places, the probability that the sample mean wait time is under 4 minutes is 0.0146.

Given that,

The population mean waiting time is 5 minutes and the standard deviation is 2.9 minutes,

And, A random sample of 40 wait times was selected.

Now, the standard deviation of the sample mean,

[tex]\text {Standard Error }= \dfrac{ \text {Population Standard Deviation}}{\sqrt{\text {Sample Size}}}[/tex]

[tex]\text {SE} = \dfrac{2.9}{\sqrt{40} }[/tex]

[tex]\text {SE} = 0.459[/tex]

Now, we can standardize the sample mean using the formula:

[tex]\text {Z}= \dfrac{ \text {(Sample mean - Population mean)}}{{\text {Standard error}}}[/tex]

[tex]\text {Z}= \dfrac{ \text {(4 - 5)}}{{\text {0.459}}}[/tex]

[tex]Z = - 2.177[/tex]

Hence, for the probability corresponding to this Z-score, we can refer to the standard normal distribution table or use a calculator.

The probability of getting a Z-score less than -2.177 is 0.0146.

To learn more about the probability visit:

https://brainly.com/question/13604758

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Answer: above

Step-by-step explanation:

ok so angle is 30 and we have adjacent and we have to figure out opposite so adjacent and opposite, we need tan so

let x be height of triangle

tan 30= x/81

x= (tan 30) *81

x= about 48ft

48 + 4 is 52 which is more than 50

[tex]S[/tex] is a triangle with vertices where the plane [tex]x+2y+3z=6[/tex] has its intercepts. These occur at the points (0,0,2), (0,3,0), and (6,0,0). Parameterize [tex]S[/tex] by

[tex]\vec s(u,v)=(1-v)((1-u)(0,0,2)+u(0,3,0))+v(6,0,0)[/tex]

[tex]\vec s(u,v)=(6v,3u(1-v),2(1-u)(1-v))[/tex]

with [tex]0\le u\le1[/tex] and [tex]0\le v\le1[/tex]. The surface element is

[tex]\mathrm d\sigma=\left\|\dfrac{\partial\vec s}{\partial u}\times\dfrac{\partial\vec s}{\partial u}\right\|\,\mathrm du\,\mathrm dv=6\sqrt{14}(1-v)\,\mathrm du\,\mathrm dv[/tex]

So the integral is

[tex]\displaystyle\iint_Sxyz\,\mathrm d\sigma=216\sqrt{14}\int_0^1\int_0^1uv(1-u)(1-v)^3\,\mathrm du\,\mathrm dv[/tex]

Answer:

[tex]C(x) = \$8 + \$0.05*x[/tex]

Step-by-step explanation:

Let 'x' be the usage in kWh. When usage is at or under 800 kWh, the cost function C(x) is given by the base charge of $8 added to the rate of $0.05/kWh multiplied by the consumption 'x', in kWh.

Therefore, for 0 ≤ x ≤ 800, the cost function is:

[tex]C(x) = \$8 + \$0.05*x[/tex]