The records of two jet liners were inspected to determine the delay times on the tarmac. the following data sets were collected. Jet Linear A Jet Liner B 57 67 96 70 93 81 63 108 70 64 64 84 69 54 63 57 100 102 98 78 89 86 103 80 62 33 76 43 72 99 62 80 104 119 109 85 80 Jet liner B was fined for long delay time. At a significance level 10%, was the jet liner B more at fault than the jet liner A?

Apple COVID-19 Mobility Trends website is one of the examples of Big Data. The aspects of Big Data that are covered by this website are given below:

Big Data refers to the massive and diverse volume of structured and unstructured data that is generated at an unprecedented speed. It comprises three main aspects that are Volume, Velocity, and Variety. The website Apple COVID-19 Mobility Trends is an example of Big Data that covers all three of these aspects. The Volume of data includes the total amount of data that is generated daily. In the case of the COVID-19 Mobility Trends website, it includes the data on mobility trends of people around the world.

This data is collected daily and is used to track the mobility of people. The website provides data on the number of requests made to Apple Maps for directions. It also shows the walking and driving trends of people in different countries. Hence, this data contributes to the Volume aspect of Big Data.Velocity aspect of Big Data refers to the speed at which data is generated, stored, and processed. The COVID-19 pandemic has affected the entire world, and as a result, the mobility of people has been disrupted. To address this issue, Apple has developed a website to track the mobility trends of people worldwide. This website provides data in real-time and is updated daily. Thus, the Velocity aspect of Big Data is also covered by the Apple COVID-19 Mobility Trends website.The third aspect of Big Data is Variety. This refers to the different types of data that are generated daily. The data generated by the COVID-19 Mobility Trends website is of various types, including location data, mobility trends data, and geographic data. The website also shows data on different modes of transport, including walking, driving, and public transport. Therefore, the website covers the Variety aspect of Big Data as well. In conclusion, the Apple COVID-19 Mobility Trends website is an example of Big Data that covers the three main aspects of Volume, Velocity, and Variety. The COVID-19 Mobility Trends website covers the three primary aspects of Big Data, i.e., Volume, Velocity, and Variety.

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If A and B are independent events, then A and B are also independent events.

Given that A and B are independent events, we want to show that A and B are also independent events.

If A and B are independent events, then:

P(A and B) = P(A) × P(B)Where P(A) denotes the probability of the event A, P(B) denotes the probability of the event B, and P(A and B) denotes the probability that both events A and B occur simultaneously.

Now, we want to show that A and B are also independent events.

Let's consider the probability that both events A and B occur simultaneously: P(A and B).

Since A and B are independent events, the probability that both events occur simultaneously is:

P(A and B) = P(A) × P(B)

We already know that A and B are independent events, and by definition of independence of events:

If A and B are independent events, then the occurrence of A has no effect on the probability of B, and the occurrence of B has no effect on the probability of A.

Therefore, we can conclude that if A and B are independent events, then A and B are also independent events.

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To express the expression 1 log(cx) + 3 log(cy) - 5 log(cx) as a single logarithm, we can use the properties of logarithms. Specifically, we can use the properties of addition and subtraction of logarithms.

The properties are as follows:

log(a) + log(b) = log(ab)

log(a) - log(b) = log(a/b)

Applying these properties to the given expression, we have:

1 log(cx) + 3 log(cy) - 5 log(cx)

Using property 1, we can combine the first two terms:

= log(cx) + log(cy^3) - 5 log(cx)

Now, using property 2, we can combine the last two terms:

= log(cx) + log(cy^3/cx^5)

Finally, using property 1 again, we can combine the two logarithms:

= log(cx * (cy^3/cx^5))

Simplifying the expression inside the logarithm:

= log(c * cy^3 / cx^4)

Therefore, the expression 1 log(cx) + 3 log(cy) - 5 log(cx) can be simplified as log(c * cy^3 / cx^4).

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The probability that a randomly chosen buyer is an adult or purchased a clock scented like cotton candy is 1, which is equivalent to 100%.

To find the probability that a randomly chosen buyer is an adult or purchased a clock scented like cotton candy, we can use the concept of probability union.

Let A be the event that a buyer is an adult and C be the event that a buyer purchased a clock scented like cotton candy.

We are given:

P(A) = 0.9 (probability that a buyer is an adult)

P(C) = 0.9 (probability that a buyer purchased a clock scented like cotton candy)

P(A and C) = 0.8 (probability that a buyer is an adult and purchased a clock scented like cotton candy)

The probability of the union of two events A and C is given by:

P(A or C) = P(A) + P(C) - P(A and C)

Substituting the given values:

P(A or C) = 0.9 + 0.9 - 0.8

P(A or C) = 1.8 - 0.8

P(A or C) = 1

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The function f(x) = 3.4e^x represents an exponential growth curve. The graph will be an increasing curve that approaches a horizontal asymptote as x approaches negative infinity.

The function has a y-intercept at (0, 3.4), and the curve will rise steeply at first and then flatten out as x increases. The exponential function f(x) = 3.4e^x can be graphed by plotting several points and observing its behavior. The scale and intercepts can be labeled to provide a clear representation of the graph.

To start, we can calculate a few key points to plot on the graph. For example, when x = -1, the value of f(x) is approximately 3.4e^(-1) ≈ 1.184. When x = 0, f(x) = 3.4e^0 = 3.4. As x increases, the value of f(x) will continue to grow rapidly. Next, we can label the x and y axes on graph paper or a template. The x-axis represents the horizontal axis, while the y-axis represents the vertical axis. The scale can be determined based on the range of values for x and y that we are interested in displaying on the graph.

Plotting the points calculated earlier, we can observe that the graph starts at the y-intercept (0, 3.4) and rises steeply as x increases. As x approaches negative infinity, the graph gets closer and closer to a horizontal asymptote located at y = 0. This represents the saturation or leveling off of the exponential growth. To ensure accuracy, it is recommended to label the key points, intercepts, and asymptotes on the graph. This will provide a clear visual representation of the function f(x) = 3.4e^x and its characteristics. Finally, a photograph or screenshot of the graph can be taken and submitted to complete the task.

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The equation of the circle with center (4, 9) and radius 4 is (x - 4)^2 + (y - 9)^2 = 16.

The general equation of a circle with center (h, k) and radius r is given by:

(x - h)^2 + (y - k)^2 = r^2

In this case, the given center is (4, 9) and the radius is 4. Plugging these values into the equation, we have:

(x - 4)^2 + (y - 9)^2 = 4^2

Simplifying, we get:

(x - 4)^2 + (y - 9)^2 = 16

Therefore, the equation of the circle with center (4, 9) and radius 4 is (x - 4)^2 + (y - 9)^2 = 16.

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To complete the steps mentioned, you can follow the code below assuming you have loaded the tidyverse library and have access to the mpg dataset:

```R

# Load the tidyverse library

library(tidyverse)

# Step 1: Create a histogram for the cty column with 10 bins

ggplot(mpg, aes(x = cty)) +

 geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +

 labs(x = "City MPG", y = "Frequency") +

 ggtitle("Histogram of City MPG") +

 theme_minimal()

# Step 2: Evaluate whether the mpg variable looks normal

# We can visually assess the normality by examining the histogram from Step 1.

# If the histogram shows a symmetric bell-shaped distribution, it suggests normality.

# However, it's important to note that a histogram alone cannot definitively determine normality.

# You can also use statistical tests like the Shapiro-Wilk test for a formal assessment of normality.

# Step 3: Calculate the mean and standard deviation for the cty column

mean_cty <- mean(mpg$cty)

sd_cty <- sd(mpg$cty)

# Step 4: Calculate probabilities using the theoretical mpg with the same mean and standard deviation as cty

# a. The probability that a car model has an mpg of 20

prob_20 <- dnorm(20, mean = mean_cty, sd = sd_cty)

# b. The probability that a car model has an mpg of less than 14

prob_less_than_14 <- pnorm(14, mean = mean_cty, sd = sd_cty)

# c. The probability that a car model has an mpg between 14 and 20

prob_between_14_20 <- pnorm(20, mean = mean_cty, sd = sd_cty) - pnorm(14, mean = mean_cty, sd = sd_cty)

# d. The mpg for which 90% of the cars are below it

mpg_90_percentile <- qnorm(0.9, mean = mean_cty, sd = sd_cty)

# Print the results

cat("a. Probability of an mpg of 20:", prob_20, "\n")

cat("b. Probability of an mpg less than 14:", prob_less_than_14, "\n")

cat("c. Probability of an mpg between 14 and 20:", prob_between_14_20, "\n")

cat("d. MPG for which 90% of cars are below it:", mpg_90_percentile, "\n")

```

Please note that the code assumes you have the `mpg` dataset available. If you don't have it, you can load it by running `data(mpg)` before executing the code.

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Answer:

A)

Step-by-step explanation:

The correct answer is A. "a" is the initial value and "b" is the growth factor.

In an exponential function of the form y = ab^x, the initial value, represented by "a," determines the y-value when x = 0. It is the starting point or the y-intercept of the graph.

The growth or decay factor, represented by "b," determines the rate at which the function grows or decays as x increases. If the graph of the exponential function is increasing, it means that the values of y are getting larger as x increases. This can only happen if the growth factor "b" is greater than 1.

Therefore, option A correctly identifies that "a" is the initial value, and "b" is the growth factor, indicating that as x increases, the function's values grow exponentially.

The simplified expression becomes 3(x⁻⁴)/(4y²) / (27x-4y³)¹/³, where all exponents are positive. To simplify the expression (3x⁻⁴4y⁻²) / (27x-4y³)¹/³, we can start by simplifying the numerator and denominator separately.

By applying exponent rules and simplifying the terms, we can then combine the simplified numerator and denominator to obtain the final simplified form of the expression.

Let's simplify the numerator and denominator separately. In the numerator, we have 3x⁻⁴4y⁻². To simplify this expression, we can apply the exponent rule for division, which states that xⁿ / xᵐ = xⁿ⁻ᵐ. Applying this rule, we can rewrite the numerator as 3(x⁻⁴)/(4y²).

Next, let's simplify the denominator, which is (27x-4y³)¹/³. We can rewrite this expression as the cube root of (27x-4y³).

Now, combining the simplified numerator and denominator, we have (3(x⁻⁴)/(4y²)) / (cube root of (27x-4y³)). To simplify further, we can apply the exponent rule for cube roots, which states that (aⁿ)¹/ᵐ = aⁿ/ᵐ. In our case, the cube root of (27x-4y³) can be written as (27x-4y³)¹/³.

Therefore, the simplified expression becomes 3(x⁻⁴)/(4y²) / (27x-4y³)¹/³, where all exponents are positive.

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The given Cauchy problem involves a wave equation with a source term F(x, t). The initial conditions are u(x, 0) = f(x) and u₁(x, 0) = g(x).

The given Cauchy problem is a second-order partial differential equation (PDE) known as the wave equation. It is given by the equation Utt - 4uxx = F(x, t), where u represents an unknown function of two variables x and t.

The initial conditions are u(x, 0) = f(x), which specifies the initial displacement, and u₁(x, 0) = g(x), which represents the initial velocity. Here, f(x) = 3 - x and g(x) = x².

The term F(x, t) = -4e^(-nt) represents the source term that affects the wave equation.

To solve this Cauchy problem, various techniques can be employed, such as the method of characteristics or separation of variables. These techniques involve transforming the PDE into a system of ordinary differential equations and applying appropriate boundary conditions to obtain a solution that satisfies the given initial conditions.

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The proof that set  F ⊆ K[x]\{0} is "Grobner-System" if only if there exists  polynomial f ∈ F​​​​​​​ which divides any polynomial in F is shown below.

If "set-F" is a Grobner system, it means that there is a polynomial in "F" that can divide every other polynomial in F. In simpler terms, if we have a collection of polynomials and there is one particular polynomial in that collection that can evenly divide all the other polynomials, then that collection is a Grobner system.

On the other hand, if there is a polynomial in the collection that can divide every other polynomial in the collection, then the collection is also a Grobner-system.

Therefore, a set of polynomials is a Grobner-system if and only if there exists a polynomial in that set that can divide all the other polynomials in the set.

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The given question is incomplete, the complete question is

Prove that the set F ⊆ K[x]\{0}  is a Grobner system if and only if there exists a polynomial f ∈ F​​​​​​​ that divides any polynomial in F.

To evaluate the definite integral ∫[2 to 4] (54y^2 / (4 - 6y)) dy, we first need to perform partial fraction decomposition on the integrand.

The integrand can be expressed as: 54y^2 / (4 - 6y) = A / (4 - 6y)

To find the value of A, we can multiply both sides of the equation by the denominator (4 - 6y): 54y^2 = A(4 - 6y)

Expanding the right side: 54y^2 = 4A - 6Ay

Now, let's equate the coefficients of y on both sides: 0y = -6Ay --> A = 0

Therefore, the partial fraction decomposition of the integrand is: 54y^2 / (4 - 6y) = 0 / (4 - 6y) = 0

Now, using the Trapezoidal rule with n = 4 steps, we can approximate the definite integral.

The Trapezoidal rule formula for approximating an integral is given by:

∫[a to b] f(x) dx ≈ h/2 * [f(a) + 2 * (f(x₁) + f(x₂) + ... + f(xₙ-1)) + f(b)]

where h = (b - a) / n is the step size, n is the number of steps, and x₁, x₂, ..., xₙ-1 are the intermediate points between a and b.

In this case, a = 2, b = 4, and n = 4. h = (4 - 2) / 4 = 2 / 4 = 1/2

Using the formula, the approximation of the definite integral is:

∫[2 to 4] (54y^2 / (4 - 6y)) dy ≈ (1/2) * [0 + 2 * (0 + 0 + 0) + 0]

Simplifying further:

∫[2 to 4] (54y^2 / (4 - 6y)) dy ≈ 0

Therefore, the approximate value of the definite integral using the Trapezoidal rule with n = 4 steps is 0.

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Answer:

x = 8

m<a = 30

Step-by-step explanation:

6x - 18 + 14x + 38 = 180

20x +20 = 180

20x +20 -20 = 180- 20

20x =160

20x / 20 = 160/ 20

x = 8

m<a = 6x-18

        => 6*8 -18 = 48-18 = 30

m<a = 30

To show that for sufficiently large m, X/m follows an approximate normal distribution with mean 1 and variance 2/m, we can make use of the Central Limit Theorem.

The Central Limit Theorem states that the sum or average of a large number of independent and identically distributed random variables, regardless of their individual distribution, tends to follow a normal distribution.

Let's consider X as the sum of m independent X² random variables, each with a mean of 1 and a variance of 2:

X = X₁ + X₂ + ... + Xₘ,

where each Xᵢ has a mean of 1 and a variance of 2.

Now, divide X by m:

X/m = (X₁ + X₂ + ... + Xₘ) / m.

Since X₁, X₂, ..., Xₘ are independent, we can apply the properties of means and variances to X/m:

Mean of X/m:

E(X/m) = E(X₁/m + X₂/m + ... + Xₘ/m) = (E(X₁) + E(X₂) + ... + E(Xₘ)) / m = (1 + 1 + ... + 1) / m = m/m = 1.

Variance of X/m:

Var(X/m) = Var(X₁/m + X₂/m + ... + Xₘ/m) = (Var(X₁) + Var(X₂) + ... + Var(Xₘ)) / m² = (2 + 2 + ... + 2) / m² = (2m) / m² = 2/m.

By the Central Limit Theorem, when m is sufficiently large, the distribution of X/m will approach a normal distribution with mean 1 and variance 2/m. Therefore, we can say that for sufficiently large m, X/m ~ approximately normal (1, 2/m).

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The correct answer is: C. (0.0422, 0.1958).

To determine whether the population variance is different from 0.05, we can perform a hypothesis test. The null hypothesis (H0) is that the population variance is equal to 0.05, while the alternative hypothesis (Ha) is that the population variance is different from 0.05.

Using a significance level of 5%, we can calculate the test statistic and compare it to the critical value from the F-distribution. The test statistic is calculated as (n-1) * sample_variance / null_hypothesis_variance, where n is the sample size.

In this case, the sample variance is calculated to be 0.1241, and the null hypothesis variance is 0.05. The test statistic is then (15-1) * 0.1241 / 0.05 = 2.976.

Comparing the test statistic to the critical value from the F-distribution with (n-1) and 1 degrees of freedom at a 5% significance level, we find that the test statistic falls within the range of (0.0422, 0.1958).

Therefore, we fail to reject the null hypothesis and conclude that there is not sufficient evidence to indicate that the population variance is different from 0.05.

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Answer:

Step-by-step explanation:

1) Angle 1 = 95   Angle 2 = 95

2) Angle 1 = 108 Angle 2 = 72

3) Angle 1 = 58  Angle 2 = 58

4) Angle 1 = 40  Angle 2 = 40

The expected value for your Amazon credit is $6.20. To calculate the expected value for your Amazon credit, we can use a tree diagram and the given probabilities and credits.

Let's denote the events as follows:

D1: First package arrives damaged

D2: Second package arrives damaged

U1: First package arrives undamaged

U2: Second package arrives undamaged

We are given the following probabilities:

P(D1) = 0.26

P(D2|U1) = 0.12

P(D2|D1) = 0.03

And the corresponding credits:

Credit(D1) = $10

Credit(D2) = $30

Now let's construct the tree diagram:

               D1 ($10)

               /   \

         D2 ($30) U2 ($0)

        /

 U1 ($0)

Based on the tree diagram, we can calculate the expected value by multiplying each credit by its corresponding probability and summing them up:

Expected Value = (P(D1) * Credit(D1)) + (P(D2|U1) * Credit(D2)) + (P(U2|U1) * Credit(U2))

Expected Value = (0.26 * $10) + (0.12 * $30) + (0.88 * $0) = $2.60 + $3.60 + $0 = $6.20

Therefore, the expected value for your Amazon credit is $6.20.

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To determine the probability that a person who supports the racetrack lives in the town, we need to calculate the conditional probability.

The conditional probability is the probability of an event occurring given that another event has already occurred. In this case, we want to find the probability that a person lives in the town given that they support the racetrack.

Let's denote the events as follows:

A: Person lives in the town

B: Person supports the racetrack

We are given the following information:

P(A ∩ B) = 3690 (number of people who support the racetrack and live in the town)

P(B) = (3690 + 2460) (total number of people who support the racetrack)

The probability that a person who supports the racetrack lives in the town can be calculated using the conditional probability formula:

P(A | B) = P(A ∩ B) / P(B)

Substituting the given values, we have:

P(A | B) = 3690 / (3690 + 2460)

Simplifying the expression:

P(A | B) = 3690 / 6150 ≈ 0.6

Therefore, the probability that a person who supports the racetrack lives in the town is approximately 0.6 or 60%.

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The probability that 3 of those vehicles will be trucks is 0.300.

In this problem, the number of trials n = 10 since 10 vehicles passed by. The probability of success p = 20% = 0.2 since that is the probability that any vehicle passing by is a truck.

The probability of observing 3 trucks in 10 vehicles is a binomial probability,

which is given by the formula:P(X = k) = {n\choose k}p^k(1-p)^{n-k} where X is the number of successes (in this case, trucks), k is the number of successes we want (3 in this case), n is the number of trials (10 in this case), and p is the probability of success (0.2 in this case).So we have: P(X = 3) = {10\choose 3}0.2^3(1-0.2)^{10-3}= 0.300

Therefore, the probability that 3 of those vehicles will be trucks is 0.300.

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Answer:

[tex]15x^3-38x^2+9x+18[/tex]

Step-by-step explanation:

[tex](5x-6)(3x^2-4x-3)\\(5x)(3x^2)+(5x)(-4x)+(5x)(-3)+(-6)(3x^2)+(-6)(-4x)+(-6)(-3)\\15x^3-20x^2-15x-18x^2+24x+18\\15x^3-38x^2+9x+18[/tex]

To calculate the t-value for an independent samples t-test, we need the means, sample sizes, and pooled variance.

Given:

For the group with pets:

Sample size (n1) = 10

Mean (M1) = 8

For the group without pets:

Sample size (n2) = 10

Mean (M2) = 3.5

Pooled variance (s^2p) = 45

Therefore, the t-value for the independent samples t-test is approximately 1.50.

To calculate Cohen's d as an effect size, we can use the formula:

d = (M1 - M2) / sqrt(spooled)

Substituting the given values:

d = (8 - 3.5) / sqrt(45)

d = 4.5 / sqrt(45)

d ≈ 0.67

Therefore, Cohen's d as an effect size is approximately 0.67.

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Answer:

x = 101

Step-by-step explanation:

[tex]x+(-21)=21-(-59)\\x-21=21+59\\x-21=80\\x=101[/tex]

The exercise consists of two parts. In the first part, a line integral is solved using Green's theorem. The requirements for this part include creating a drawing of the region in GeoGebra, parameterizing the curve with corresponding intervals, stating the line integral with positive orientation, and resolving the integral.

In the second part, an iterated double integral is solved using Green's theorem and applied to a region of type I or type II. The requirements for this part include creating a drawing in GeoGebra, highlighting the defined functions and intervals for the region, and correctly solving the double integral.

The exercise requires solving a line integral and an iterated double integral using Green's theorem. In the first part, GeoGebra is used to create a visual representation of the region, and the curve is parameterized with appropriate intervals. The line integral is then stated with positive orientation, and the integral is resolved.

In the second part, a drawing is made in GeoGebra to represent the region, emphasizing the parts where the functions are defined and the intervals used. Either a type I or type II region is chosen, and the corresponding functions and intervals are defined. Finally, the double integral is correctly solved using the chosen region and Green's theorem.

Both parts of the exercise require a combination of mathematical understanding and the use of GeoGebra to visualize and solve the given problems.

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False. Because the repeated-measures ANOVA removes variance caused by individual differences, it usually is more likely to detect a treatment effect than the independent-measures ANOVA is.

The statement is false. The repeated-measures ANOVA is more likely to detect a treatment effect compared to the independent-measures ANOVA due to its ability to control for individual differences. By measuring the same subjects under different conditions, the repeated-measures design reduces the influence of individual variability and increases the sensitivity to detect treatment effects. In contrast, the independent-measures ANOVA compares different groups of subjects, which may introduce additional variability and make it relatively harder to detect treatment effects.

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Among the given expressions, the one that could be the possible leading term for the polynomial function graphed below is -18x¹⁴.

The leading term of a polynomial function is the term containing the highest power of the variable. Among the given expressions, the one that could be the possible leading term for the polynomial function graphed below is -18x¹⁴.

The degree of a polynomial function is the highest degree of any of its terms.

If a polynomial has only one term, then the degree of that term is the degree of the polynomial and is also called a monomial.

For example, consider the given function:Now, observe the degree of the function, which is 14, as the highest exponent of the function is 14.

Thus, the term containing the highest power of the variable x is -18x¹⁴.

Therefore, among the given expressions, the one that could be the possible leading term for the polynomial function graphed below is -18x¹⁴.

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The function y₁ (t) = eat is a solution to the differential equation y''-y-72y = 0. On the other hand, the function y(t) = e-8 is not a solution to the differential equation.

To verify that the given functions are solutions to the differential equation y''-y-72

y = 0, we must substitute them into the differential equation and check if they satisfy it.

i) y₁ (t) = eat

We can find the first and second derivatives of y₁(t) as follows:

y₁(t) = eat

⇒ y₁'(t) = aeat

⇒ y₁''(t) = aeat

Thus, substituting these expressions into the differential equation, we get:

(aeat) - (eat) - 72(eat) = 0

⇒ (a-1-72)eat = 0

For the above equation to be true for all values of t, we must have:

a - 1 - 72 = 0

⇒ a = 73

Therefore, y₁(t) = eat is a solution to the differential equation,

provided a = 73.

ii) y(t) = e⁻⁸

Using the same method as above, we can find the first and second derivatives of y(t):

y(t) = e⁻⁸

⇒ y'(t) = -8e⁻⁸

⇒ y''(t) = 64e⁻⁸

Substituting these expressions into the differential equation, we get:

(64e⁻⁸) - (e⁻⁸) - 72(e⁻⁸) = 0

⇒ (-9e⁻⁸) = 0

The above equation is not true for all values of t.

Hence, y(t) = e⁻⁸ is not a solution to the differential equation.

Therefore, only y₁(t) = eat is a solution to the differential equation, provided a = 73.

Answer:

Thus, the function y₁ (t) = eat is a solution to the differential equation y''-y-72y = 0. On the other hand, the function y(t) = e-8 is not a solution to the differential equation.

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From the graph of 5 galaxies and using the values of the Hypothetical Galaxy (HC), the RV (Recession Velocity) is 3.1167 x 10^4 km/sec and D (Distance) is 415 Mpc (megaparsecs).

The relationship between Recession Velocity (RV) and Distance (D) in cosmology is described by Hubble's Law, which states that the recessional velocity of galaxies is directly proportional to their distance from us. This relationship is known as the Hubble constant, denoted as H, and is typically expressed in units of km/sec/Mpc.

In this case, the values of RV and D obtained from the graph indicate the observed recession velocity and distance of the Hypothetical Galaxy (HC) respectively. The RV value of 3.1167 x 10^4 km/sec represents the velocity at which the Hypothetical Galaxy is receding from us, while the D value of 415 Mpc corresponds to its distance from us.

Regarding the statement about the universe contracting, it is not possible to infer the contraction or expansion of the universe solely based on the RV and D values provided. The contraction or expansion of the universe is determined by studying the overall dynamics and behavior of galaxies on much larger scales.

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The given differential equation e(dy/dx) + 3y = x^2y is a non-homogeneous and non-separable equation. Therefore, option (d) is the correct answer.

To classify the given differential equation, we examine its form and properties. The equation e(dy/dx) + 3y = x^2y is a first-order linear differential equation, which can be written in the standard form as dy/dx + (3/e)y = x^2y/e. A homogeneous differential equation is one in which all terms involve either the dependent variable y or its derivatives dy/dx. A non-homogeneous equation contains additional terms involving the independent variable x.

A separable differential equation is one that can be expressed in the form g(y)dy = f(x)dx, where g(y) and f(x) are functions of y and x, respectively. In the given equation, we have terms involving both y and dy/dx, as well as a term involving x^2. Therefore, it is a non-homogeneous equation. Furthermore, the equation cannot be rearranged to the form g(y)dy = f(x)dx, indicating that it is non-separable. Hence, the given differential equation e(dy/dx) + 3y = x^2y is classified as a non-homogeneous and non-separable equation. Therefore, option (d) is the correct answer.

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The minimum selling price per television in negotiating a price for the special order should be $174.

To determine the minimum selling price per television for the special order, we need to consider the relevant costs associated with producing and selling the televisions.

The direct materials cost per television is $75, the direct labor cost is $55, and the manufacturing overhead cost is $44 (75% variable and 25% unavoidable fixed). These costs amount to $174 per television.

In addition to the production costs, there is a selling cost of $12 per television for shipping the special order. Therefore, the total cost per television for the special order is $174 + $12 = $186.

Since the regular selling price for the televisions is $220, Farnsworth should negotiate a minimum selling price per television of at least the total cost per television for the special order, which is $186.

Therefore, the minimum selling price per television should be $174.

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4.1.2. the claim that the median price is R120,000 is not valid. 4.1.3. The mean price is the best suited for the data in dealer 2

4.1.1 To arrange the prices of car dealer 1 in descending order:

R178,200

R175,000

R155,700

R125,800

R115,000

R110,900

R108,500

R108,500

R99,900

R99,900

R95,000

4.1.2 To verify whether the claim that the median price of car dealer 1 is R120,000 is valid, we need to find the median of the data set:

Arranging the prices in ascending order:

R95,000

R99,900

R99,900

R108,500

R108,500

R110,900

R115,000

R125,800

R155,700

R175,000

R178,200

The median is the middle value, so we can see that the median price is R115,000. Therefore, the claim that the median price is R120,000 is not valid.

4.1.3 To determine the mean price of dealer 2, we sum up all the prices and divide by the total number of prices:

R138,600 + R140,000 + R165,000 + R180,000 + R192,000 + R235,000 + R238,000 + R400,000 + R450,000 + R650,000 + R700,000 = R3,378,600

To find the mean, we divide the sum by the number of prices:

Mean = R3,378,600 / 11 = R307,145.45

The mean price is the best suited for the data in dealer 2 because it takes into account all the prices and provides a measure of central tendency that is influenced by each data point. It gives an overall average price for the vehicles at dealer 2.

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