Patrick wants to determine the amount of wrapping paper he will need to cover a rectangular shirt box. The box is 10 in. wide, 17 in. long, and 1.5 in. tall. How much wrapping paper will Patrick need?
A 255 in.2
B. 370 in.2
c. 391 in.2
D. 421 in.2

Answer:

PEMDAS is an acronym that stands for Parentheses, Exponents, Multiplication and Division (from left to right), and Addition and Subtraction (from left to right). It's a helpful mnemonic device to remember the order of operations in arithmetic and algebraic equations. When solving a mathematical expression or equation, it's important to follow PEMDAS to ensure that the solution is accurate and consistent.

Answer:

by Paige Faber, ACDC Peer Advisor. Remember in seventh grade when you were discussing the order of operations in math class and the teacher told you the catchy acronym, “PEMDAS” (parenthesis, exponents, multiplication, division, addition, subtraction) to help you remember?

Step-by-step explanation:

HI

The calculated list price of the computer is $480

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

Total price = $513

Sales tax = 6.875%

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

Total price = List price * (1 + tax)

Substitute the known values in the above equation, so, we have the following representation

List price * (1 + 6.875%)  = 513

Evaluate

List price  = 480

Hence, the List price  = 480

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To determine which vitamin leads to weight gain, a hypothesis test can be conducted using SPSS with a level of significance of 5%. The group with the significantly higher mean weight gain would indicate which vitamin leads to weight gain.

To determine which vitamin leads to weight gain at a level of significance of 5%, a hypothesis test needs to be conducted. The null hypothesis would be that there is no significant difference in weight gain between the three groups, and the alternative hypothesis would be that there is a significant difference.

To conduct the test in SPSS, the first step would be to input the data for each group and calculate the mean weight gain for each group. Then, a one-way ANOVA test can be conducted to determine if there is a significant difference between the means. The level of significance is set at 5%.

If the p-value is less than 0.05, the null hypothesis can be rejected, indicating that there is a significant difference between the means. Further post-hoc tests can then be conducted to determine which specific groups differ significantly.

In conclusion, to determine which vitamin leads to weight gain, a hypothesis test can be conducted using SPSS with a level of significance of 5%. The group with the significantly higher mean weight gain would indicate which vitamin leads to weight gain.

To determine which vitamin leads to weight gain in the specific population, you can perform an analysis using SPSS at a 5% level of significance. First, input the weight gain data for the three vitamin groups into SPSS. Then, conduct an ANOVA (Analysis of Variance) test to compare the means of the three groups. If the p-value obtained from the test is less than the level of significance (0.05), it indicates a significant difference between the groups. Further post-hoc tests (such as Tukey's HSD) can then be conducted to identify which vitamin group leads to a significant weight gain compared to the others.

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b) To find the joint density of the minimum and maximum of the U_i's, we can use the following approach:

Let M = min(U_1, U_2, U_3, U_4, U_5) and let X = max(U_1, U_2, U_3, U_4, U_5). Then we have:

P(M > m, X < x) = P(U_1 > m, U_2 > m, U_3 > m, U_4 > m, U_5 > m, U_1 < x, U_2 < x, U_3 < x, U_4 < x, U_5 < x)

Since the U_i's are independent and uniformly distributed on (0,1), we have:

P(U_i > m) = 1 - m, for 0 < m < 1

P(U_i < x) = x, for 0 < x < 1

Substituting these expressions, we get:

P(M > m, X < x) = (1 - m)^5 * x^5

Therefore, the joint density of M and X is:

f(M,X) = d^2/dm dx (1-m)^5 * x^5 = 30(1-m)^4 * x^4, for 0 < m < x < 1.

c) To find P(R > 0.5), we need to find the probability that the distance between the minimum and maximum of the U_i's is greater than 0.5. We can use the following approach:

P(R > 0.5) = 1 - P(R <= 0.5)

Now, R <= 0.5 if and only if the difference between the maximum and minimum of the U_i's is less than or equal to 0.5. Therefore, we have:

P(R <= 0.5) = P(X - M <= 0.5)

To find this probability, we can integrate the joint density of M and X over the region where X - M <= 0.5:

P(R <= 0.5) = ∫∫_{x-m<=0.5} f(M,X) dm dx

The region of integration is the triangle with vertices (0,0), (0.5,0.5), and (1,1). We can split this triangle into two regions: the rectangle with vertices (0,0), (0.5,0), (0.5,0.5), and (0,0.5), and the triangle with vertices (0.5,0.5), (1,0.5), and (1,1). Therefore, we have:

P(R <= 0.5) = ∫_{0}^{0.5} ∫_{0}^{m+0.5} 30(1-m)^4 * x^4 dx dm + ∫_{0.5}^{1} ∫_{x-0.5}^{x} 30(1-m)^4 * x^4 dm dx

Evaluating these integrals, we get:

P(R <= 0.5) ≈ 0.5798

Therefore,

P(R > 0.5) = 1 - P(R <= 0.5) ≈ 0.4202.

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The correct option is the first one:

The theoretical probability, P(gold), is 25%, and the experimental probability is 27.5%.

The experimental probability is equal to the quotient between the number of times that a gold block was taken and the total number of trials, so it is:

E = 11/40 = 0.275

Multiply this by 100% to get the percentage:

0.275*100% = 27.5%

For the theoretical probability, take the quotient between the number of gold blocks and the total number:

T = 10/40 = 0.25

And multiply it by 100%

100%*0.25 = 25%

Then the correct option is The theoretical probability, P(gold), is 25%, and the experimental probability is 27.5%.

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Answer:

I'm pretty sure the answer is 80.

Step-by-step explanation:

5 × 8 × 2 = 80

The correct statement regarding the domain and the range of the exponential function is given as follows:

An exponential function has the definition presented as follows:

[tex]y = ab^x[/tex]

In which the parameters are given as follows:

The function is in the standard format, meaning that the horizontal asymptote is y = 0, and thus the multiplication of a by zero does not change the horizontal asymptote, and the range stays the same.

The domain also remains the same, as an exponential function is defined for all real values.

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The -scores that bound the middle of the area under the standard normal curve are -0.99 and 0.99.

To find the -scores that bound the middle of the area under the standard normal curve, we can use the normalcy function on the TI-84 Plus calculator.

1. Press the "2nd" button, then the "Vars" button (which is the "DISTR" button).

2. Scroll down to "2:normalcdf(", and press "Enter".

3. Type "-99", ",99", "0", and "1" (without quotes), and press "Enter" after each number.

4. The calculator will display the area under the standard normal curve between -99 and 99, which is 1.00 (since the standard normal curve is infinite and covers the entire -infinity to infinity range).

5. To find the -scores that bound the middle of the area, we need to find the -scores that bound the area between -1 and 1, which is approximately 0.68 (or 68% of the total area).

6. Press the "2nd" button, then the "Vars" button (which is the "DISTR" button) again.

7. Scroll down to "3:invNorm(", and press "Enter".  

8. Type "0.16", "0.84", "0", and "1" (without quotes), and press "Enter" after each number.

9. The calculator will display the -scores that bound the middle 68% of the area under the standard normal curve, which are approximately -0.99 and 0.99 (rounded to two decimal places and in ascending order).

Therefore, the -scores that bound the middle of the area under the standard normal curve are -0.99 and 0.99.

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The coordinates of the image point A after the dilation is (-1/4, -1/4)

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

ABC with vertices at A(-1, -1), B(1, 1), C(0, 1).

The scale factor is given as

Scale factor  = 1/4

So, we have

Image = A * Scale factor  

Substitute the known values in the above equation, so, we have the following representation

Image = (-1, -1)/4

Evaluate

Image = (-1/4, -1/4)

Hence, the image = (-1/4, -1/4)

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It is true that an altitude of a triangle is a line segment drawn from a vertex of the triangle perpendicular to the opposite side (or to the line containing the opposite side).

It forms a right angle with the line containing the opposite side. This altitude splits the triangle into two smaller triangles, and it can also be used to find the area of the triangle. The altitude of a triangle is useful in geometry because it can be used to find the area of the triangle. The area of a triangle can be calculated as half the product of the base (the side to which the altitude is drawn) and the length of the altitude. By drawing an altitude from each vertex of the triangle and calculating the area of each smaller triangle, we can find the total area of the larger triangle. Altitudes also have other important properties in geometry, such as being concurrent at a single point called the orthocenter of the triangle, and being related to the sides of the triangle through the Pythagorean theorem.

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To find the 35th percentile for the standard normal distribution, we need to use a z-table or a calculator. The z-score corresponding to the 35th percentile is -0.385.
Therefore, the answer is -0.385 (rounded to the third decimal place).

The 35th percentile for the standard normal distribution. Here are the steps to find it:

1. Determine the percentile you're looking for, which is the 35th percentile in this case.

2. Since we're working with a standard normal distribution, the mean (μ) is 0 and the standard deviation (σ) is 1.

3. To find the z-score that corresponds to the 35th percentile, you can use a z-table or a calculator with a built-in function for finding percentiles (such as the inverse cumulative distribution function, often labeled as "inv Norm" or "norm").

4. Using a z-table or calculator, look up the z-score that corresponds to the 35th percentile, which is approximately -0.385.

5. Round the z-score to the third decimal place, which is -0.385.

So, the 35th percentile for the standard normal distribution is a z-score of -0.385.

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According to the figure the value of B is

The value of B is solved using the principles of segments of chords secants and tangents

The principle is such that

10^2 = 5 * (5 + b)

100 = 25 + 5b

gathering like terms

5b = 100 - 25

5b = 75

isolating b

b = 75 / 5

b = 15

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The z-score corresponding to a mass of 148.3 kg is approximately -0.91.

Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score.

A population has a mean of 159.8 kg and a standard deviation of 12.6 kg. To find the z-score corresponding to a mass of 148.3 kg, use the formula: z = (X - μ) / σ, where X is the value (148.3 kg), μ is the mean (159.8 kg), and σ is the standard deviation (12.6 kg).

z = (148.3 - 159.8) / 12.6
z = -11.5 / 12.6
z ≈ -0.91

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A simple random sample of steel canisters has been taken, and you have the mean wall thickness and standard deviation in millimeters. To determine if it is appropriate to perform a hypothesis test about the population mean, consider the following factors:

1. Randomness: The sample has been taken using a simple random sampling method, which helps ensure that each canister has an equal chance of being selected. This is a crucial factor for conducting a hypothesis test.

2. Sample size: Although the sample size is not mentioned, a large sample size (usually 30 or more) is preferred for hypothesis testing. The larger the sample size, the more accurate the results of the test will be in representing the entire population.

3. Normality: Hypothesis tests about the population mean often rely on the assumption that the data follows a normal distribution. With a large sample size, the Central Limit Theorem suggests that the sampling distribution of the mean will be approximately normally distributed, even if the population itself is not.

4. Known standard deviation: You have the standard deviation for the sample, which is necessary for conducting the hypothesis test.

Given the information provided, it seems appropriate to perform a hypothesis test about the population mean for the wall thickness of steel canisters, provided the sample size is large enough.

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Answer: 777 Meters

Step-by-step explanation:asa

We cannot determine the p-value from the given information. The confidence interval only tells us the range of values that we are 95% confident contains the true population mean weight change.

The p-value would need to be calculated from the sample data and test statistics to determine the statistical significance of the weight loss program's effectiveness.
A consumer group testing the weight change of participants in a weight loss program. They computed a 95% confidence interval of the result (-4.977, -2.177) and you want to know what we can infer about the p-value for the test.
Since the 95% confidence interval does not include 0 (meaning that the weight change is significantly different from no change), we can conclude that the p-value for this test would be less than 0.05.
The p-value for the test would be less than 0.05.

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When oversampling a minority class, the positive rate increases, meaning that the model is more likely to correctly identify instances of that class. Under-sampling the majority class may decrease the false positive rate, as the model is less likely to incorrectly classify instances from the majority class as belonging to the minority class.

This may also decrease the positive rate, as there are fewer examples of the minority class to learn from. In general, both oversampling and under-sampling can have trade-offs and it is important to carefully consider the specific dataset and problem at hand before deciding which approach to use.

Oversampling, in which examples from the minority class are duplicated, can have the following effects:
1. False positive rate: Oversampling may lead to an increase in false positives, as the classifier becomes more sensitive to the minority class, causing it to potentially misclassify some majority class instances as minority class instances.
2. False negative rate: On the other hand, oversampling tends to reduce the false negative rate, since the classifier becomes better at identifying the minority class instances.

Undersampling, in which examples from the majority class are deleted, can have these effects:
1. False positive rate: Undersampling may lead to a decrease in false positives, as the classifier is less likely to misclassify majority class instances as minority class instances due to the reduced majority class representation.
2. False negative rate: However, undersampling can cause an increase in false negatives, as the classifier may not be as sensitive to the minority class instances and may misclassify them as majority class instances.

In summary, oversampling generally increases false positives and reduces false negatives, while undersampling tends to decrease false positives and increase false negatives. When choosing between these methods, it's important to consider the specific problem and the desired balance between false positive and false negative rates.

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A Type II error occurs when we fail to reject a null hypothesis that is actually false. In this case, the null hypothesis is that the proportion of students who were quarantined at some point during the Fall Semester of 2020 is equal to or less than 0.65.

The alternative hypothesis is that the proportion is greater than 0.65. If we make a Type II error, we fail to reject the null hypothesis when it is actually false, meaning we do not conclude that the proportion is higher than 0.65 even though it actually is higher.

Therefore, the correct explanation for a Type II error, in this case, we would be: "Did not conclude the percent was higher than 65%, but it was higher."

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The description of the motion of the ball across the floor after being rolled by Vic, based on the distances shown on the table and the average speed are as follows;

1. The function is a quadratic function

2. a. 12 ft/s

b. 10 ft/s

c. 8 ft/s

d. 6 ft/s

Average speed is the ratio of the distance traveled in a period to the time it takes to travel during the period.

The values in the table in the indicates that we get;

The first differences are;

6 - 0 = 6

11 - 6 = 5

15 - 11 = 4

18 - 15 = 3

The second difference are;

5 - 6 = -1

4 - 5 = -1

3 - 4 = -1

Whereby the first difference are not constant and the second difference, which is the differences between the consecutive first difference are constant, we get;

2. a. The average speed between 0 and 0.5 seconds = (6 - 0)/(0.5 - 0) = 12

The average speed between 0 and 0.5 seconds is 12 feet per second

b. The interval between 0.5 and 1 indicates;

Average speed = (11 - 6)/(1 - 0.5) = 10

The average speed between 0.5 and 1 second is 10 feet per second

c. The interval between 1 and 1.5 seconds indicates;

Average speed = (15 - 11)/(1.5 - 1) = 8

The average speed between 1 and 1.5 second is 8 feet per second

d. The interval between 1.5 and 2 seconds indicates;

Average speed = (18 - 15)/(2 - 1.5) = 6

The average speed between 1.5 and 2 second is 6 feet per second

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The ratio of family practice physicians to pediatricians is 5:1.

The ratio of family practice physicians to pediatricians can be found by dividing the number of family practice physicians by the number of pediatricians:

Ratio = Number of family practice physicians / Number of pediatricians

Ratio = 10 / 2

Ratio = 5

Therefore, the ratio of family practice physicians to pediatricians is 5:1.

The clinic administrator can use this ratio to assess the balance of the clinic's medical staff. If the ratio is not ideal, the administrator may need to take steps to hire more pediatricians or family practice physicians to better meet the needs of the clinic's patients.

Additionally, the ratio can be used to allocate resources, such as staff time and medical equipment, more effectively based on the patient population the clinic serves.

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The probability is 9/10.

Sample Space: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

Successful Outcomes:

Numbers greater than 2: {3, 4, 5, 6, 7, 8, 9, 10}

Odd numbers: {1, 3, 5, 7, 9}

However, the numbers 3, 5, 7, and 9 are included in both the "greater than 2" and "odd numbers" sets. So, we only count them once in our combined set of successful outcomes.

Combined Successful Outcomes: {1, 3, 4, 5, 6, 7, 8, 9, 10}

There are a total of 9 successful outcomes out of the 10 possible outcomes in the sample space.

The probability that the number will be more than 2 or odd is:

Probability = (Number of successful outcomes) / (Total number of outcomes) = 9 / 10

So, the probability is 9/10.

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a. H: δ>100 is a legitimate statistical hypothesis because it is a statement about a population parameter (δ) and it is testable using statistical methods.
b. H: x=45 is a legitimate statistical hypothesis because it is a statement about a population parameter (x) and it is testable using statistical methods.
c. H: s<.20 is a legitimate statistical hypothesis because it is a statement about a population parameter (s) and it is testable using statistical methods.
d. H: δ1/δ2 <1 is a legitimate statistical hypothesis because it is a statement about a population parameter (δ1 and δ2) and it is testable using statistical methods.
e. H: X-Y=5 is a legitimate statistical hypothesis because it is a statement about a population parameter (X and Y) and it is testable using statistical methods.
f. H: גּ<.01 is a legitimate statistical hypothesis because it is a statement about a population parameter (λ) and it is testable using statistical methods, with the parameter being the rate parameter of an exponential distribution used to model component lifetime.

Here are the answers for each assertion:

a. H: δ>100 - This is a legitimate statistical hypothesis because it states a specific direction for the population parameter (delta).

b. H: x=45 - This is not a legitimate statistical hypothesis because it refers to a sample statistic (x) rather than a population parameter.

c. H: s<.20 - This is not a legitimate statistical hypothesis because it refers to a sample statistic (s, sample standard deviation) instead of a population parameter.

d. H: δ1/δ2 <1 - This is a legitimate statistical hypothesis because it specifies a relationship between two population parameters (delta1 and delta2).

e. H: X-Y=5 - This is not a legitimate statistical hypothesis because it refers to sample statistics (X and Y) rather than population parameters.

f. H: λ<.01 - This is a legitimate statistical hypothesis because it states a specific direction for the population parameter (lambda) related to the exponential distribution for component lifetime.

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If events A and B are independent, then P(A and B) = P(A) * P(B). Also, from Bayes' theorem we have P(A | B) = P(A and B) / P(B).

Given that P(A | B) = 0.35 and P(B) = 0.5, we can solve for P(A and B) as follows:

P(A and B) = P(A | B) * P(B) = 0.35 * 0.5 = 0.175

Since events A and B are independent, we have P(A and B) = P(A) * P(B). Solving for P(A), we get:

P(A) = P(A and B) / P(B) = 0.175 / 0.5 = 0.35

Therefore, P(A) = 0.35.

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The power series representation for f(x) = 1/(1-x) centered at x=0 is:

1 + x + x^2 + x^3 + ...

To find a power series representation for a function, we can use the formula:

f(x) = ∑(n=0 to infinity) [an(x-a)^n]

where a is the center of the series and an is the coefficient of the (x-a)^n term.

For example, let's find a power series representation for the function f(x) = 1/(1-x) centered at x=0:

Using the formula, we have:

f(x) = ∑(n=0 to infinity) [an(x-0)^n]

To find the coefficients an, we can use the formula for the geometric series:

1/(1-x) = 1 + x + x^2 + x^3 + ...

So, we have:

an = [x^n]/n!

Substituting this into the power series formula, we get:

f(x) = ∑(n=0 to infinity) [(x^n)/(n!)](x-0)^n

Simplifying, we get:

f(x) = ∑(n=0 to infinity) [(x^n)/(n!)]

Therefore, the power series representation for f(x) = 1/(1-x) centered at x=0 is:

1 + x + x^2 + x^3 + ...

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Answer: 0.333333333->

Step-by-step explanation:

It goes on forever

The correct answer is: z-test statistic = 3.41, p-value = 0.0003. To find the standardized test statistic (z-test statistic) and corresponding p-value, we will use the following formula: z = (sample mean - hypothesized mean) / (standard deviation / sqrt(sample size))


To find the standardized test statistic, we use the formula:

z = (sample mean - hypothesized mean) / (standard deviation / sqrt(sample size))

Plugging in the values given, we get:

z = (78 - 75) / (5.9 / sqrt(45))
z = 3.41

To find the corresponding p-value, we can use a standard normal distribution table or calculator. The p-value represents the probability of getting a z-score as extreme or more extreme than the one we calculated. Since our z-score is positive, we are interested in the area to the right of it on the standard normal distribution. This gives us a p-value of 0.0003.

Therefore, the correct answer is: z-test statistic = 3.41, p-value = 0.0003.
To find the standardized test statistic (z-test statistic) and corresponding p-value, we will use the following formula:

z = (sample mean - hypothesized mean) / (standard deviation / sqrt(sample size))

Plugging in the given values:

z = (78 - 75) / (5.9 / sqrt(45))

z = 3 / (5.9 / 6.708)

z = 3 / 0.880

z ≈ 3.41

Using a standard normal distribution table or a calculator, we find the area to the right of the z-score (since we're testing if the braking distance is greater than the hypothesized mean):

p-value ≈ 0.0003

Therefore, the correct answer is:

z-test statistic = 3.41, p-value = 0.0003

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Answer:

Step-by-step explanation: Since ABCD is a rhombus, all sides are equal in length, and opposite angles are equal. Let's denote the length of each side as "s". We know that the area of a rhombus can be calculated as (diagonal1 * diagonal2)/2.

Using the given information, we can create the following diagram:

css

Copy code

       A

      / \

     /   \

    /     \

   /       \

  /         \

 P-----------B

  \         /

   \       /

    \     /

     \   /

      \ /

       C

We know that AC and BD are diagonals of the rhombus, and their lengths are in a 4:3 ratio. Let's denote the length of AC as 4x and the length of BD as 3x.

We also know that the length of side PA is equal to the length of side PB, and the length of side PC is equal to the length of side PD. Therefore, we can use the Pythagorean theorem to calculate the value of s:

s^2 = PA^2 + AP^2 = (4x)^2 + (s/2)^2

s^2 = PB^2 + BP^2 = (3x)^2 + (s/2)^2

s^2 = PC^2 + CP^2 = (4x)^2 + (s/2)^2

s^2 = PD^2 + DP^2 = (3x)^2 + (s/2)^2

We can simplify these equations to:

16x^2 + s^2/4 = s^2

9x^2 + s^2/4 = s^2

16x^2 + s^2/4 = s^2

9x^2 + s^2/4 = s^2

Combining like terms, we get:

s^2 = 64x^2/3

s^2 = 36x^2/5

Setting these two expressions equal to each other and solving for x, we get:

64x^2/3 = 36x^2/5

320x^2 = 108x^2

x^2 = 0

This result indicates that our assumption of a rhombus with given side length and diagonal ratio is not valid. Therefore, there is no unique solution for the area of ABCD.

The differential equation describing the cell phone sales is [tex]\frac{{dy}}{{dt}} = 0.5357 \cdot y(t) \cdot \left(1 - \frac{{y(t)}}{{100}}\right)[/tex].

Based on the given information, the cell phone sales growth is logistic, with a carrying capacity of 100 thousand units. When 28 thousand cell phones have been sold, the rate of sales increase is 10 thousand units per month.

The logistic growth differential equation is given by:

[tex]\frac{dy}{dt} = k \cdot y(t) \cdot \left(1 - \frac{y(t)}{M}\right)[/tex]

where dy/dt is the rate of change in sales, y(t) is the number of cell phones sold after t months, k is the growth rate, and M is the carrying capacity.

In this case, y(t) = 28, dy/dt = 10, and M = 100. To find k, we can plug these values into the equation:

10 = [tex]$k \cdot 28 \cdot \left(1 - \frac{28}{100}\right)$[/tex]

Solving for k:

k ≈ 0.5357

Therefore, the differential equation describing the cell phone sales is:

[tex]\frac{{dy}}{{dt}} = 0.5357 \cdot y(t) \cdot \left(1 - \frac{{y(t)}}{{100}}\right)[/tex]

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(a) The sampling distribution of x is approximately normal with mean 82 and standard deviation 3. (b) P(x > 87.4) = 0.0708. (c) P(x ≤ 75.1) = 0.0990. (d) P(79.3 < x < 84.7) = 0.1675.

(a) The sampling distribution of x is approximately normal due to the central limit theorem, with a mean of μ = 82 and a standard deviation of σ/sqrt(n) = 27/sqrt(81) = 3.
(b) To find P(x > 87.4), we first standardize the value using the formula z = (x - μ) / (σ / sqrt(n)) = (87.4 - 82) / (27 / sqrt(81)) = 1.48. We then find the probability using a standard normal distribution table or calculator, which is approximately 0.0708 or 7.08%.
(c) To find P(x ≤ 75.1), we again standardize the value using the formula z = (x - μ) / (σ / sqrt(n)) = (75.1 - 82) / (27 / sqrt(81)) = -1.29. We then find the probability using a standard normal distribution table or calculator, which is approximately 0.0990 or 9.90%.
(d) To find P(79.3), we first standardize the value using the formula z = (x - μ) / (σ / sqrt(n)) = (79.3 - 82) / (27 / sqrt(81)) = -0.96. We then find the probability using a standard normal distribution table or calculator, which is approximately 0.1675 or 16.75%.

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