The arrival time of an elevator in a 12-story dormitory is equally likely at any time range during the next 4.6 minutes. a. Calculate the expected arrival time. (Round your answer to 2 decimal place.) Expected arrival time b. What is the probability that an elevator arrives in less than 3.5 minutes? (Round intermediate calculations to at least 4 decimal places and final answer to 3 decimal places.) Probability c. What is the probability that the wait for an elevator is more than 3.5 minutes? (Round intermediate calculations to at least 4 decimal places and final answer to 3 decimal places.) Probability

The number of games that Chris sell is g/8 - 1/7

From the question, we have the following parameters that can be used in our computation:

Using the above as a guide, we have the following:

Average = g/8

So, we have

Chris = g/8 - 1/7

Hence, the expression is g/8 - 1/7

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The probability of a combination lock consisting of only even numbers is 1/12.

There are 5 even numbers from 0 to 8: 0, 2, 4, 6, and 8. Since the combination has no repeated numbers, we can choose the first number in 5 ways, the second number in 4 ways (since we can't repeat the first number), and the third number in 3 ways. Therefore, there are 5 x 4 x 3 = 60 possible combinations of even numbers.

The total number of possible combinations is the number of ways we can choose 3 numbers out of 10, which is 10 x 9 x 8 = 720.

Therefore, the probability of the combination consisting only of even numbers is 60/720, which simplifies to 1/12.

So the probability of a combination lock consisting of only even numbers is 1/12.

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Answer:

6 million

Step-by-step explanation:

50% = 1/2

1/2 + 1/3 = 5/6

5/6 people watched or listened, which means 1/6 people did not do either of them.

1/6 of 36 (million) = 6 (million)

So, the answer is 6 million.

To modify the definition of SNN similarity to take into account the weights of the edges connecting the two points to their shared neighbors, we can use a weighted SNN similarity algorithm. This algorithm would involve assigning weights to the edges connecting the points and their shared neighbors, and using these weights to calculate the SNN similarity.

To calculate the weighted SNN similarity, we would first calculate the SNN distance as usual, but instead of just counting the number of shared neighbors between two points, we would also consider the weights of the edges connecting them to their shared neighbors. This could be done by multiplying the number of shared neighbors by the average weight of the edges connecting the points and their shared neighbors.

The advantages of this modification include more accurately capturing the similarity between points based on the strength of their connections to shared neighbors. This could be particularly useful in applications where the strength of connections is important, such as social network analysis or recommendation systems.

However, there are also potential disadvantages to this modification. For example, it could be more computationally intensive to calculate the weighted SNN similarity compared to the original algorithm. Additionally, assigning weights to edges could be subjective or difficult to determine, which could affect the accuracy of the similarity calculations.

Overall, while the weighted SNN similarity algorithm has the potential to improve the accuracy of similarity calculations in certain applications, it should be carefully evaluated for its practicality and effectiveness.

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A random variable is a variable that takes on values that are uncertain or probabilistic in nature.

Therefore, the correct option is a) A variable that takes on values that are uncertain.

Random variables can be discrete, meaning they can only take on specific values, or continuous, meaning they can take on any value within a certain range.

These variables are commonly used in statistical analyses and probability theory to model various phenomena, such as the outcome of a dice roll or the height of individuals in a population.

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The boat's speed in miles per minute is 9.3855 and its speed in miles per hour is 563.13.

Let's call the speed of the boat in still water "b" and the speed of the current "c".

When the boat is traveling downstream with the current, its effective speed is increased by the speed of the current, so its speed is (b + c) miles per minute.

Using the formula distance = rate x time, we can set up an equation for the downstream trip:

15 = (b + c) x 75/60

Simplifying this equation, we get:

15 = (b + c) x 5/4

12 = b + c

Similarly, when the boat is traveling upstream against the current, its effective speed is decreased by the speed of the current, so its speed is (b - c) miles per minute.

Using the same formula, we can set up an equation for the upstream trip:

15 = (b - c) x 133/60

Simplifying this equation, we get:

15 = (b - c) x 2.2167

6.771 = b - c

Now we have two equations with two unknowns (b and c). We can solve for b by adding the two equations:

12 + 6.771 = 2b

18.771 = 2b

b = 9.3855 miles per minute

To convert this to miles per hour, we can multiply by 60:

9.3855 x 60 = 563.13 miles per hour (rounded to two decimal places)

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60% of on-campus students in the sample participate in at least one extracurricular activity, 50% of off-campus students in the sample participate in at least one extracurricular activity.

The proportion of on-campus students in the sample who participate in at least one extracurricular activity, we need to divide the number of on-campus students who participate in at least one extracurricular activity by the total number of on-campus students in the sample.

Let's assume that our sample contains 100 on-campus students, and 60 of them participate in at least one extracurricular activity.

Then, the proportion of on-campus students who participate in at least one extracurricular activity is:

proportion = number of on-campus students who participate in at least one extracurricular activity / total number of on-campus students in the sample

proportion = 60/100

proportion = 0.6 or 60%

To calculate the proportion of off-campus students in the sample who participate in at least one extracurricular activity, we follow the same process.

Let's assume that our sample contains 80 off-campus students, and 40 of them participate in at least one extracurricular activity.

Then, the proportion of off-campus students who participate in at least one extracurricular activity is:

Proportion = number of off-campus students who participate in at least one extracurricular activity / total number of off-campus students in the sample

proportion = 40/80

proportion = 0.5 or 50%

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Since each packet received is independent of any other packet, we can model X as a binomial distribution with parameters n=10 and p=0.05 (the probability of receiving a packet with errors is 1-0.95=0.05).

The probability mass function (PMF) of X is given by:

P(X=k) = (10 choose k) * 0.05^k * 0.95^(10-k), for k = 0, 1, 2, ..., 10

where (10 choose k) is the binomial coefficient, representing the number of ways to choose k packets out of 10.

So, for example, the probability of receiving exactly 2 packets with errors is:

P(X=2) = (10 choose 2) * 0.05^2 * 0.95^8
= 45 * 0.0025 * 0.4305
= 0.0463

Similarly, we can calculate the probabilities for other values of k.
Hi, I'd be happy to help you with your question. Let's find the probability mass function (PMF) of X, where X represents the number of packets received with errors out of 10 packets, and the packets are independent with a 0.95 probability of being error-free.

1. Define the probability of success (error-free) and failure (with errors).
  Success (error-free): p = 0.95
  Failure (with errors): q = 1 - p = 0.05

2. Since the packets are independent, we can use the binomial distribution to model the problem. The PMF of a binomial distribution is given by:
  P(X = k) = C(n, k) * p^k * q^(n-k)

3. In our case, n = 10 (number of packets received), and we need to find P(X = k) for k = 0, 1, 2, ..., 10.

So, the PMF of X, the number of packets received with errors out of 10 independent packets, is:

P(X = k) = C(10, k) * (0.95)^k * (0.05)^(10-k), for k = 0, 1, 2, ..., 10.

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In the context of measurement, accuracy and precision refer to two related but distinct concepts. Accuracy is the degree to which a measurement is close to the true value of what is being measured, while precision is the degree to which repeated measurements of the same quantity are close to each other.

The uncertainty of a measurement refers to the degree of doubt or lack of confidence in the result obtained from a measuring instrument. It is typically represented by an interval around the measured value that indicates the range within which the true value is likely to lie.

The accuracy of a measuring device is related to its ability to provide measurements that are close to the true value. If a measuring device is highly accurate, then its measurements will be close to the true value, and the uncertainty associated with those measurements will be relatively small. On the other hand, if a measuring device is not very accurate, then its measurements may be far from the true value, and the uncertainty associated with those measurements will be relatively large.

The precision of a measuring device is related to its ability to provide measurements that are close to each other when measuring the same quantity repeatedly. A measuring device that is highly precise will give measurements that are very close to each other, and the uncertainty associated with those measurements will be relatively small. Conversely, a measuring device that is not very precise will give measurements that are far apart, and the uncertainty associated with those measurements will be relatively large.

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The final equation of the logarithmic equation is x³ - 12x² + 27x - 27 = 0

We have,

To solve the equation [tex]\log x_{3} (x-9) + \log x_{3} (x-3) = 2[/tex],

We can use the logarithmic rule that states:

㏒a (x) + ㏒a (y) = ㏒a (xy)

Using this rule, we can simplify the equation as follows:

[tex]\log x_{3} [(x-9)(x-3)] = 2[/tex]

Now, we can use the definition of logarithms, which states:

㏒a (x) = b if and only if a^b = x

Using this definition, we can rewrite the above equation as:

[tex]x^2_{3} [(x-9)(x-3)] = 3^2[/tex]

Expanding the brackets and simplifying.

x³ - 12x² + 27x - 27 = 0

Thus,

The final equation of the logarithmic equation is x³ - 12x² + 27x - 27 = 0

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Answer:

Bond

Step-by-step explanation:

A promissory note or a promise to repay a certain amount of money at some point in the future is basically a bond.

A bond is a debt security that represents a loan made by an investor to a borrower, which is usually a corporation or government agency. It is a fixed-income investment, meaning that the borrower promises to pay a specific amount of interest over a set period of time and return the principal amount of the loan on the date of maturity. Bonds are issued for various purposes, such as raising capital, funding new projects, or refinancing debt.

CD (Certificate of Deposit) is a savings instrument issued by a bank or credit union that generally pays a fixed rate of interest over a set term. Mutual fund is an investment vehicle that pools money from multiple investors to purchase a portfolio of securities, such as stocks, bonds, or both. Stock is an ownership share in a company that represents a claim on part of the company's assets and earnings.

Answer:

The awnser to this equation is 120 cents

The cost of using a 200-watt television for 30 days, turned on for 2 hours per day, would be $1.20.  

To calculate the cost of using a 200-watt television for 30 days with 2 hours of daily usage at 10 cents per kilowatt-hour, we need to find the total energy consumption and then multiply it by the cost per kilowatt-hour.

First, let's find the total energy consumption:

1. Daily energy usage: 200 watts * 2 hours = 400 watt-hours
2. Monthly energy usage: 400 watt-hours * 30 days = 12,000 watt-hours

Now, we need to convert watt-hours to kilowatt-hours:
3. Monthly energy usage in kilowatt-hours: 12,000 watt-hours / 1,000 = 12 kWh

Finally, let's calculate the cost:
4. Cost of using the television for 30 days: 12 kWh * 10 cents per kWh = 120 cents

So, the cost of using a 200-watt television for 30 days with 2 hours of daily usage at 10 cents per kilowatt-hour is 120 cents.

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Answer:

(3/7)x + (1/3)(x + 2) = 38

9x + 7(x + 2) = 798

9x + 7x + 14 = 798

16x = 784

x = 49, so x + 2 = 51

The numbers are 49 and 51.

The two consecutive odd numbers are 49 and 51, and their sum of three-sevenths of the first number and one-third of the second number equals thirty-eight.

To find two consecutive odd numbers such that the sum of three-sevenths of the first number and one-third of the second number is equal to thirty-eight, follow these steps:

1. Let the first odd number be x, and the second odd number be x + 2 (since they are consecutive odd numbers).
2. The sum of three-sevenths of the first number and one-third of the second number is equal to thirty-eight, so we can write the equation as (3/7)x + (1/3)(x + 2) = 38.
3. To solve for x, first find the common denominator for the fractions, which is 21. Multiply each term by 21: 9x + 7(x + 2) = 798.
4. Simplify and solve for x: 9x + 7x + 14 = 798. Combine like terms: 16x + 14 = 798.
5. Subtract 14 from both sides: 16x = 784.
6. Divide both sides by 16: x = 49.
7. So, the first odd number is 49, and the second odd number is 49 + 2 = 51.

The two consecutive odd numbers are 49 and 51, and their sum of three-sevenths of the first number and one-third of the second number equals thirty-eight.

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The remainder when the original number is divided by 130 is 0 using Chinese Remainder Theorem.

To solve this problem, we need to use the Chinese Remainder Theorem. Since the number has a remainder of 3 when divided by 10 and a remainder of 3 when divided by 13, we can write it as:

x ≡ 3 (mod 10)
x ≡ 3 (mod 13)

To find the solution, we can use the following steps:

Step 1: Find a solution to each congruence.

For the first congruence, we can see that x = 13k + 3 is a solution, where k is an integer. This is because any number of the form 13k + 3 will leave a remainder of 3 when divided by 10.

For the second congruence, we can use the same method and find that x = 10m + 3 is a solution, where m is an integer.

Step 2: Combine the solutions using the Chinese Remainder Theorem.

To combine the solutions, we need to find a number that satisfies both congruences. One way to do this is to use the equation:

x = aM(y)(b) + bM(x)(a)

where a = 10, b = 13, M(a) = 13, and M(b) = 10. Plugging in these values, we get:

x = 10(13)(y) + 13(10)(x)

Simplifying this equation, we get:

x = 130y + 130x - 130x + 130y

x = 260y

So any number of the form 260y will satisfy both congruences.

Step 3: Find the remainder when divided by 130.

To find the remainder when divided by 130, we can simply take the remainder of 260y when divided by 130. Since 260 is a multiple of 130, we know that the remainder will be 0. Therefore, the remainder when the original number is divided by 130 is 0.

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When there is only one type of treatment or grouping factor with more than two levels, a one-way ANOVA is used. This type of ANOVA compares the means of multiple groups to determine if there is a significant difference between them. The factor, or independent variable, is the single grouping factor with multiple levels. The dependent variable is the measure of interest that is being compared across the groups. The one-way ANOVA is useful when trying to determine if there is a significant difference in the means of multiple groups, but it does not provide information on which specific groups are significantly different from each other. Pairwise comparisons or post-hoc tests can be used to further analyze the differences between groups.
Hi! The type of ANOVA used when there is only one type of treatment or grouping factor with more than two levels is called One-way ANOVA. One-way ANOVA is a statistical method used to test the null hypothesis that there are no differences between the means of the different groups, considering only one independent variable, the grouping factor. It helps in determining if the factor has a significant effect on the dependent variable by comparing the variance within groups and the variance between groups.

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The average score on the test is 82" is an example of a measure of central tendency known as the mean.

The statement "The average score on the test is 82" is an example of a measure of central tendency known as the mean. In this case, the mean is being used to summarize the performance of a group of 8th graders on a test of basic concepts of conflict resolution at the end of a 6 week psychoeducation group on the topic.

The mean is a common measure of central tendency that represents the arithmetic average of a set of values. It is calculated by adding up all of the values in the set and dividing by the number of values.

In this case, the mean score of 82 indicates that the average performance on the test was relatively good, though other measures of central tendency, such as the median or mode, could provide additional insights into the distribution of scores among the students.

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If the p-value is less than 0.05, we can reject the null hypothesis and conclude that there is evidence to suggest that the population mean CO2 level has increased. If the p-value is greater than 0.05.

Null hypothesis (H0): The population means [tex]CO_2[/tex] level is equal to 418 ppm.

Alternative hypothesis (Ha): The population means [tex]CO_2[/tex] level is greater than 418 ppm.

A p-value, or probability value, is a statistical measure that helps to determine the significance of results obtained from a hypothesis test. It is the probability of observing a test statistic as extreme as the one computed, assuming that the null hypothesis is true. The null hypothesis is a statement that there is no significant difference between two populations or that there is no effect of an intervention or treatment.

The p-value is used to decide whether or not to reject the null hypothesis based on a pre-determined significance level, typically 0.05 or 0.01. If the p-value is less than the significance level, the null hypothesis is rejected, indicating that the observed results are unlikely to have occurred by chance alone, and that the alternative hypothesis is likely true. Conversely, if the p-value is greater than the significance level, the null hypothesis is not rejected, indicating that the observed results are consistent with the null hypothesis.

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Answer:

the possible outcomes contain the same number of heads and tails are 70.

tell me if i am right

For each coin flip, there are 2 possible outcomes (heads or tails), so for 8 flips, there are [tex]2^8[/tex] = 256 possible outcomes.

When flipping a coin, there are two possible outcomes: heads or tails. So, for one flip, there are two possibilities. For two flips, there are two possibilities for the first flip and two possibilities for the second flip, making a total of 2x2=4 possible outcomes.

For three flips, there are two possibilities for the first flip, two for the second flip, and two for the third flip, making a total of 2x2x2=8 possible outcomes.

Similarly, for four flips, there are 2x2x2x2=16 possible outcomes, and for five flips, there are 2x2x2x2x2=32 possible outcomes.

Continuing this pattern, for eight flips, there are 2x2x2x2x2x2x2x2 = 256 possible outcomes.

This can also be calculated using the formula for combinations, which is [tex]n! / (r!(n-r)!)[/tex] where n is the number of total flips (in this case, 8) and r is the number of heads that we want to get.

For example, to find the number of outcomes where we get exactly 3 heads and 5 tails, we would use the formula:

[tex]8! / (3!5!) = 56[/tex]

So, there are 56 possible outcomes where we get exactly 3 heads and 5 tails.

In summary, for each coin flip, there are 2 possible outcomes (heads or tails), so for 8 flips, there are [tex]2^8 = 256[/tex] possible outcomes.

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Answer: 2.6

Step-by-step explanation:

Hope this helps! :)

There is not sufficient evidence at the 0.10 level to support the marketing executive's claim that the percentage of readers owning a laptop is different from the reported percentage of 54T%.

To test whether the sample proportion of 50P% is significantly different from the reported proportion of 54T%, we can use a one-sample z-test.

The null hypothesis is that the true proportion is equal to 54T%, and the alternative hypothesis is that the true proportion is different from 54T%.

The test statistic is calculated as:

z = (p - p₀) / √(p₀(1-p₀)/n)

where p is the sample proportion, p₀ is the hypothesized proportion, and n is the sample size.

Plugging in the values, we get:

z = (0.50 - 0.54) / √(0.54(1-0.54)/110) ≈ -1.38

The critical value for a two-tailed test at the 0.10 level with a sample size of 110 is ±1.645. Since the calculated test statistic (-1.38) does not exceed the critical value (-1.645), we fail to reject the null hypothesis.

Therefore, there is not sufficient evidence at the 0.10 level to support the marketing executive's claim that the percentage of readers owning a laptop is different from the reported percentage of 54T%.

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Step-by-step explanation:

the ONLY roll that is two or less when rolling 2 dice is   1 - 1

 out of 36 possible rolls

     so 35 are greater than two     or     35/36

Three treatment conditions were compared in the study.

The question is: A researcher reports an F-ratio with df 5 2, 27 from an independent-measures research study. How many treatment conditions were compared in the study?

To find the number of treatment conditions, we need to look at the first number in the degrees of freedom (df) pair, which is 2.

The first df value (numerator) represents the degrees of freedom associated with the between-groups or treatment variability, while the second df value (denominator) represents the degrees of freedom associated with the within-groups or error variability.

The formula to find the number of treatment conditions is:

Number of treatment conditions = df between groups + 1

In this case, df between groups is 2. So, using the formula:

Number of treatment conditions = 2 + 1 = 3

Therefore, three treatment conditions were compared in the study.

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The probability of getting a poker hand with two jacks or two fives is approximately 0.009 or 0.9%.

To calculate the probability of getting a poker hand with two jacks or two fives, we need to know the total number of possible poker hands and the number of poker hands with two jacks or two fives.
There are a total of 52 cards in a standard deck of playing cards. To get a poker hand with two cards, we need to choose two cards out of 52. The number of ways to choose two cards out of 52 is given by the combination formula, which is:
C(52,2) = 52! / (2! * (52-2)!) = 1326
Therefore, there are 1326 possible poker hands that we can get.
Now, we need to find the number of poker hands that consist of two jacks or two fives. There are 4 jacks and 4 fives in a standard deck of cards, so there are 4C2 = 6 ways to choose two jacks or two fives. Therefore, there are a total of 12 possible poker hands with two jacks or two fives.
The probability of getting a poker hand with two jacks or two fives is given by:
P(two jacks or two fives) = number of poker hands with two jacks or two fives / total number of possible poker hands
= 12 / 1326
= 0.009
Therefore, the probability of getting a poker hand with two jacks or two fives is approximately 0.009 or 0.9%.

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To find the appropriate value for c, we need to use the cumulative distribution function (CDF) of the bolt width. Let X be the width of a bolt and F(x) be the CDF of X. Then, we have: P(X < c) = F(c) = 0.8438.

Using a standard normal distribution table, we can find the z-score corresponding to a cumulative probability of 0.8438, which is 1.03 (rounded to two decimal places). Therefore, we have: z = (c - μ) / σ = 1.03, where μ and σ are the mean and standard deviation of the bolt width, respectively. Rearranging this equation, we get: c = μ + z * σ.

We need to know the values of μ and σ to compute c. Let's assume that the bolt width follows a normal distribution with mean μ = 0.75 inches and standard deviation σ = 0.03 inches (these values are just examples). Then, we have: c = 0.75 + 1.03 * 0.03 = 0.78 inches.

Therefore, the appropriate value for c such that a randomly chosen bolt has a width less than c with probability 0.8438 is 0.78 inches (rounded to two decimal places). Since the probability distribution is not given, I will assume that you are referring to a standard normal distribution (z-score).

Using a z-score table or calculator, find the z-score that corresponds to the cumulative probability of 0.8438. The z-score is approximately 1.01. Now, we need to convert the z-score back to the original width scale. This can be done using the formula: Width = (z-score × standard deviation) + mean

However, since the standard deviation and mean are not provided, it is not possible to find the exact value for c. If you can provide the mean and standard deviation, I can help you find the appropriate value for c rounded to two decimal places.

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Answer:

648 (I think)

I hope this helps...

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a) The probabilities for X = 0, 1, 2, and 3, and then summing them up, we find that P(X > 3) ≈ 0.019. b) The probability of zero successes is approximately 0.430. c) The length of an interval of time is 1.306 hours.

a) The time between arrivals of small aircraft is exponentially distributed with a mean of one hour. To find the probability that more than three aircraft arrive within an hour, we will use the Poisson distribution, where λ (lambda) represents the average number of arrivals per hour, which is 1 in this case. The probability formula is P(X > 3) = 1 - P(X ≤ 3), where X is the number of arrivals. Using the Poisson formula, we get:

P(X > 3) = 1 - [P(X=0) + P(X=1) + P(X=2) + P(X=3)]

Calculating the probabilities for X = 0, 1, 2, and 3, and then summing them up, we find that P(X > 3) ≈ 0.019.

b) To find the probability that no interval contains more than three arrivals in 30 separate one-hour intervals, we can use the binomial distribution. The probability of success (an interval with more than three arrivals) is 0.019 from part a), and the probability of failure (an interval with three or fewer arrivals) is 1 - 0.019 = 0.981. Using the binomial formula with n = 30 (number of intervals) and p = 0.981, we find the probability of zero successes (i.e., no interval with more than three arrivals) is approximately 0.430.

c) To determine the length of an interval of time (in hours) such that the probability that no arrivals occur during the interval is 0.27, we use the exponential distribution formula:

P(T > t) = e^(-λt), where T is the waiting time between arrivals, t is the time interval, and λ is the average number of arrivals per hour (1 in this case).

We want to find the value of t such that P(T > t) = 0.27. So:

0.27 = e^(-1 * t)

Taking the natural logarithm of both sides, we get:

ln(0.27) = -t

Solving for t, we find that t ≈ 1.306 hours.

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The slope of the line of best fit in this context represents the rate of change in the number of hot cocoas sold for every unit increase in the day's high temperature.

In other words, it represents how much the sales of hot cocoa increase or decrease with respect to changes in the day's high temperature. If the slope is positive, it means that as the temperature increases, Justin sells more hot cocoas, and if the slope is negative, it means that as the temperature decreases, Justin sells more hot cocoas.

The magnitude of the slope indicates the degree to which the number of hot cocoas sold is affected by changes in the temperature.

The slope of the line of best fit in this context represents the rate of change in the number of hot cocoas sold for every unit increase in the day's high temperature.

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Answer:

THE ANSWER IS -7.5 .

Step-by-step explanation:

-6+5÷-2+1

-6-2.5+1

-6-1.5

-7.5

The answer is -9.5, as we will apply the rule of "BODMAS."

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To find the first employee who commutes by bus, you would need to sample at least 5 employees (since 20% of employees commute by bus, and 1/5 is equal to 20%).

However, if you wanted to increase the likelihood of finding the first employee who commutes by bus, you may need to sample more employees. To find the first employee who commutes by bus, you can use the concept of probability.

Since 20% of the business's employees commute by bus, there's a 1 in 5 chance that a randomly selected employee will be a bus commuter. On average, you would need to sample 5 employees in order to find the first employee who commutes by bus.

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