Let X = (1, 2, 3, 4, 5, 6). Which of the following could be an equivalence class of an equivalence relation on X? a. (1 2)(3 4)(5 6) b. {(1, 2), (3, 4), (5, 6)} c. {1, 3, 5} d. {(1, 2), (3, 4), (5, 6)}

Answer:

To find the distance between parallel lines, you can use the formula:

distance = |(c2 - c1)| / sqrt(a^2 + b^2)

Where the lines are represented in the form ax + by + c1 = 0 and ax + by + c2 = 0.

For the given equations:

Line 1: y = 3x + 4

Line 2: y = 3x - 5

We can rewrite the equations in the standard form:

Line 1: 3x - y + 4 = 0

Line 2: 3x - y + 5 = 0

Comparing the coefficients, we have:

a = 3

b = -1

c1 = 4

c2 = 5

Now we can calculate the distance:

distance = |(c2 - c1)| / sqrt(a^2 + b^2)

= |(5 - 4)| / sqrt(3^2 + (-1)^2)

= 1 / sqrt(9 + 1)

= 1 / sqrt(10)

≈ 0.316227766

Rounding the answer to the nearest hundredth, the distance between the parallel lines y = 3x + 4 and y = 3x - 5 is approximately 0.32

I hope that helped!!

1. The probability that the high temperature will exceed 100 is  0.0228 or 2.28%.

2. To satisfy quality control requirements, the standard deviation (σ) of the battery production needs to be determined such that the probability of the voltage being between 1.45V and 1.52V is at least 99%.

1. To find the probability that the high temperature will exceed 100, we can standardize the variable using the z-score formula: z = (x - u) / a, where x is the value of interest, u is the mean, and a is the standard deviation. Then, we can calculate the probability using the standard normal distribution table or software. For this case, we need to find P(X > 100), where X is the high temperature. By standardizing, we get z = (100 - 87.2) / 6.4 ≈ 2.0. Consulting the standard normal distribution table, we find that the probability is approximately 0.0228 or 2.28%.

2. To determine the required standard deviation for the battery production, we need to find the z-scores corresponding to the lower and upper voltage limits (1.45V and 1.52V) using the formula z = (x - V) / σ, where V is the mean voltage and σ is the standard deviation. We want the probability of the voltage falling within this range to be at least 99%, so we need to find the z-scores that give the cumulative probability of 0.99 and (1 - 0.99)/2 = 0.005 on each tail. Once we have the z-scores, we can rearrange the formula to solve for σ. This calculation requires the use of the standard normal distribution table or software.

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Answer:

it will take Tyrell and Hunter approximately 2.4 hours to mow the lawn together.

Step-by-step explanation:

To determine the number of hours it will take for Tyrell and Hunter to mow the lawn together, we can use the concept of work rates.

Tyrell mow 1/4 per hour

Hunter mow 1/6 per hour

When working together, their work rates are additive. Therefore, the combined work rate of Tyrell and Hunter is:

1/4 + 1/6 = 3/12 + 2/12 = 5/12 of the lawn per hour.

(5/12) * x = 1 (since they need to mow the entire lawn, which is equivalent to 1).

x = (1) * (12/5) = 12/5 = 2.4 hours.

On November 1, Year 1, Shumate Company paid $1,200 in advance for an insurance policy that covered the company for six months. A debit to Insurance Expense for $400.

When an insurance policy is paid in advance, the amount paid is initially recorded as a prepaid expense. Over time, as the coverage period progresses, the prepaid expense needs to be adjusted to reflect the portion that has been used up or expired.

In this case, the insurance policy covers the company for six months, and two months have passed from November 1 to December 31. Therefore, four months of insurance coverage remain as of December 31.

To adjust the prepaid insurance account on December 31, Year 1, we need to recognize the portion that has been used up. Since two months have passed, which is one-third (2/6) of the coverage period, we need to adjust the prepaid insurance by one-third of the original amount of $1,200.

One-third of $1,200 is $400, so there should be a debit entry to Insurance Expense for $400 to recognize the portion of insurance coverage that has been used up or expired.

Hence, the correct option is "A debit to Insurance Expense for $400."

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To construct a confidence interval for the proportion of all customers who experienced an interruption, we can use the formula:

Confidence Interval = Point Estimate ± (Critical Value) * (Standard Error)

First, let's calculate the point estimate:

Point Estimate = Number of customers who experienced an interruption / Total sample size

Point Estimate = 75 / 550 ≈ 0.136 (rounded to three decimal places)

Next, we need to determine the critical value corresponding to a 98% confidence level. Since the sample size is large (n = 550) and the data are assumed to be approximately normally distributed, we can use the z-table.

The critical value for a 98% confidence level can be found by finding the z-score that corresponds to an area of (1 - 0.98) / 2 = 0.01 in the upper tail of the standard normal distribution. Using the z-table, this critical value is approximately 2.33 (rounded to two decimal places).

Next, we need to calculate the standard error:

Standard Error = sqrt((Point Estimate * (1 - Point Estimate)) / Sample Size)

Standard Error = sqrt((0.136 * (1 - 0.136)) / 550) ≈ 0.016 (rounded to three decimal places)

Now we can construct the confidence interval:

Confidence Interval = 0.136 ± (2.33 * 0.016)

Confidence Interval ≈ 0.136 ± 0.037 (rounded to three decimal places)

So the 98% confidence interval for the proportion of all customers who experienced an interruption is approximately 0.099 < p < 0.173.

c. The company's quality control manager claims that no less than 10% of its customers experienced an interruption. We can check if this claim is contradicted by examining the confidence interval.

In the confidence interval, the lower bound is 0.099, which is greater than 0.1. Therefore, the confidence interval does not contradict the claim. It is possible that at least 10% of the customers experienced an interruption based on the provided data.

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Answer:

Center: (-4,1)

Radius: √2

Step-by-step explanation:

Complete the square for both variables

[tex]x^2+y^2+8x-2y+15=0\\x^2+8x+y^2-2y+15=0\\x^2+8x(+16)+y^2-2y+15(-14)=0+16-14\\x^2+8x+16+y^2-2y+1=2\\(x+4)^2+(y-1)^2=2[/tex]

Comparing our result with [tex](x-h)^2+(y-k)^2=r^2[/tex], then the center of the circle is [tex](h,k)=(-4,1)[/tex] and our radius is [tex]r=\sqrt{2}[/tex]

This means that we can estimate, with 95% confidence, that the average time spent at all Taco Bell drive-throughs is between 166.1 seconds (173 - 6.9) and 179.9 seconds (173 + 6.9).

To estimate the average time spent at all Taco Bell drive-throughs with 95% confidence, we can use a confidence interval. The formula for the confidence interval is:

CI = x ± Z * (σ / √n)

Where:

x is the sample mean (173 seconds)

Z is the Z-score corresponding to the desired confidence level (95% confidence corresponds to a Z-score of approximately 1.96)

σ is the population standard deviation (35 seconds)

n is the sample size (100 cars)

Plugging in the values, we get:

CI = 173 ± 1.96 * (35 / √100)

Calculating the expression inside the parentheses, we have:

CI = 173 ± 1.96 * (35 / 10)

Simplifying further, we get:

CI = 173 ± 1.96 * 3.5

CI = 173 ± 6.86

Rounding to one decimal place, the confidence interval is:

CI = 173 ± 6.9 seconds

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To evaluate the probabilities of type I and type II errors in this scenario, we need to use the binomial distribution and consider the given hypotheses.

a) Type I Error:In this case, the null hypothesis is that the new stain remover product removes no more than 70% of stains, which means p = 0.7. If we reject the null hypothesis when it is actually true, it would be a type I error. We are testing if fewer than 11 out of 12 stains are removed. The probability of making a type I error can be calculated by summing up the probabilities of observing 0 to 10 successful outcomes (spots removed) in 12 trials, given a success probability of p = 0.7. Using a binomial distribution:P(Type I Error) = P(X ≤ 10), where X follows a binomial distribution with n = 12 and p = 0.7. Calculating this probability depends on the specific software or calculator used. However, you can use binomial probability tables, Excel, or statistical software to find the cumulative probability for X ≤ 10 with n = 12 and p = 0.7. This cumulative probability represents the probability of observing 10 or fewer successful outcomes (spots removed) in 12 trials. Subtracting this value from 1 will give you the probability of making a type I error.b) Type II Error:

In this case, the alternative hypothesis is that the new stain remover product removes more than 70% of stains, meaning p = 0.9. If we fail to reject the null hypothesis (claiming p ≤ 0.7) when the alternative hypothesis is true (p > 0.7), it would be a type II error.We want to calculate the probability of committing a type II error when p = 0.9. This probability is given by: P(Type II Error) = P(X ≥ 11), where X follows a binomial distribution with n = 12 and p = 0.9. Similar to the previous case, you can calculate this probability using binomial probability tables, Excel, or statistical software. The cumulative probability for X ≥ 11 represents the probability of observing 11 or more successful outcomes (spots removed) in 12 trials when p = 0.9.

Please note that the exact calculations depend on the specific software or calculator you are using, but the general approach is as described above.

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Let us say two different inputs x₁ and x₂ so that f(x₁) = f(x₂).

f(x₁) = f(x₂) implies:

(e^x₁ - e^(-x₁)) / (e^x₁ + e^(-x₁)) = (e^x₂ - e^(-x₂)) / (e^x₂ + e^(-x₂))

(e^x₁ - e^(-x₁))(e^x₂ + e^(-x₂)) = (e^x₂ - e^(-x₂))(e^x₁ + e^(-x₁))

(e^x₁e^x₂ + e^x₁e^(-x₂) - e^(-x₁)e^x₂ - e^(-x₁)e^(-x₂)) = (e^x₂e^x₁ + e^x₂e^(-x₁) - e^(-x₂)e^x₁ - e^(-x₂)e^(-x₁))

e^x₁e^(-x₂) - e^(-x₁)e^x₂ = e^x₂e^(-x₁) - e^(-x₂)e^x₁

ln(e^x₁e^(-x₂) - e^(-x₁)e^x₂) = ln(e^x₂e^(-x₁) - e^(-x₂)e^x₁)

Using the properties of logarithms:

x₁ - x₂ = x₂ - x₁

This simplifies to:

0 = 0

The equation zero = zero is proper for any x₁ and x₂. This implies that the idea that f(x₁) = f(x₂) leads to a contradiction. Hence, the characteristic f(x) = (e^x - e^(-x)) / (e^x + e^(-x)) is injective or one-to-one.

Thus, as the feature is one-to-one, it means that it has an inverse.

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Your question seems incomplete, the probable complete question is:

Examine whether the function f(x)=e^x-e^-x/e^x+e^-x is inverse or not.

When T = 170° and P = 20 lb/in², the volume of the gas is approximately 119.0 in³ (option b).

The problem states that the volume V of a gas varies directly with the temperature T and inversely with the pressure P. Mathematically, this can be represented as V = k(T/P), where k is the constant of variation.

To solve the problem, we can use the given values of V, T, and P to find the value of k. We have V = 350.0 in³, T = 250°, and P = 10 lb/in². Plugging these values into the equation V = k(T/P), we can solve for k.

350.0 = k(250/10)

35 = k(25)

k = 35/25 = 1.4

Now that we have the value of k, we can find the volume V when T = 170° and P = 20 lb/in².

V = k(T/P) = 1.4(170/20) = 11.9 in³

Rounding to the nearest tenth, the volume of the gas is approximately 119.0 in³, which corresponds to option b.

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To determine whether (T + S)² is symmetric, skew-symmetric, or neither, we need to examine its properties.

Let's start by expanding (T + S)² using the binomial expansion:

(T + S)² = (T + S)(T + S)

Using the distributive property, we can expand this expression:

(T + S)(T + S) = T(T + S) + S(T + S)

Expanding further:

T(T + S) + S(T + S) = T² + TS + ST + S²

Now, let's analyze the individual terms in this expansion:

T²: This term is a symmetric matrix. The square of a symmetric matrix is also symmetric.

S²: This term is a symmetric matrix. The square of a symmetric matrix is also symmetric.

TS: This term represents the product of a symmetric matrix (T) and a matrix (S). The product of a symmetric matrix and any matrix may or may not be symmetric. Therefore, we cannot determine its symmetry without further information.

ST: This term represents the product of a matrix (S) and a symmetric matrix (T). Similar to the previous case, the product of a matrix and a symmetric matrix may or may not be symmetric. Again, we cannot determine its symmetry without further information.

In conclusion, (T + S)² can be symmetric if both TS and ST are symmetric matrices, but it is not guaranteed. Therefore, (T + S)² can be symmetric, skew-symmetric, or neither, depending on the specific matrices T and S.

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The expression arccos(cos(πº)) can be evaluated. The result is π radians. The expression arccos(cos(270º)) can be90º or π/2 radians. The expression cos(arccos(2)) cannot be evaluated.

The expression arccos(cos(πº)) can be evaluated because it involves the composition of inverse cosine and cosine functions. The cosine of πº is -1, and the arccosine function returns the angle whose cosine is -1. Since the cosine function has a period of 2π, the angle π is equivalent to 180º, and therefore arccos(cos(πº)) = π.

Similarly, the expression arccos(cos(270º)) can be evaluated. The cosine of 270º is 0, and the arccosine function returns the angle whose cosine is 0. In this case, the angle is 90º or π/2 radians.

The expression cos(arccos(2)) cannot be evaluated because the arccosine function only returns values between -1 and 1. Since 2 is out.

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Step-by-step explanation:

My z-score tables are set up to show the area to the LEFT
  so you will need to find the z-score that is   1-.10 = .90  

  which , by looking at the tables is z-score =  +1.28

The weighted mean medical payments for these five plants is $11536.90

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

The table of values

The weighted mean medical payments for these five plants is calculated as

Weighted mean = Sum/Count

So, we have

Weighted mean = (7,773 * 125 + 14,808 * 404 + 12,392 * 253 + 6,708 * 105 + 3,532 * 70)/(125 + 404 + 253 + 105 + 70)

Evaluate

Weighted mean = 11536.90

Hence, the weighted mean is $11536.90

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Question

A corporation that operates five suppliers of athletic apparel in a region provides merchandise for a shoe company. The shoe company recently sought information from the five plants. One variable for which data were collected was the total money​ (in dollars) the company spent on medical support for its employees in the first three months of the year. Data on number of employees at the plants are also shown below.

Data:

Medical Employees

$7,773 125

$14,808 404

$12,392 253

$6,708 105

$3,532 70

Compute the weighted mean medical payments for these five plants using the numbers of employees as the weights.

Given (X,Y) has the two dimensional Gaussian distribution with parameters: vector of expectations μ = (EX,EY) = (1,-1) and covariance matrix as shown below: Cov(X,X) Cov(X,Y) 4 2 C = Cov(Y,X) Cov(Y,Y) 2 2.

We are supposed to find the correlation coefficient of X and Y.

Recall that the correlation coefficient is given by the formula

Cor(X, Y) = Cov(X, Y) / (SD(X) * SD(Y)) ... (1)

Thus, we will need to calculate Cov(X, Y), SD(X) and SD(Y).

Summary: The correlation coefficient of X and Y for the given two-dimensional Gaussian distribution is 1/√2.

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To calculate the total amount of liquid in all the containers, we need to add up the volumes of liquid in each container.

Since three 1-liter containers are filled, each containing 1000 milliliters, the total volume from these containers is 3 liters or 3000 milliliters.

The fourth container has 200 milliliters of liquid, and the fifth container has 800 milliliters. Adding these volumes, we get 200 milliliters + 800 milliliters = 1000 milliliters.

To find the total amount of liquid in all the containers, we add the volume from the three 1-liter containers (3000 milliliters) to the volume from the fourth and fifth containers (1000 milliliters). Thus, the total amount of liquid in the containers is 3000 milliliters + 1000 milliliters = 4000 milliliters.

Therefore, the combined volume of liquid in all the containers is 4000 milliliters.

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The price index for a train ticket from Lanchester to London, using the base year 2000, can be calculated by dividing the price in each year by the price in the base year and multiplying by 100. The price index for 1995 is 50, for 1996 is 66.67, for 1997 is 83.33, and for 2000 (the base year) is 100.

To calculate the price index for a train ticket from Lanchester to London, we need to compare the prices in different years to a base year, which in this case is 2000. The formula for calculating the price index is (Price in Year / Price in Base Year) x 100.

For the year 1995, the price of the ticket is £30. Dividing this price by the price in the base year (2000), which is £60, gives us 0.5. Multiplying this by 100 gives a price index of 50.

For 1996, the ticket price is £40. Dividing by the base year price (£60) gives us 0.6667. Multiplying by 100 gives a price index of approximately 66.67.

Similarly, for 1997, the price index is 83.33, as the ticket price of £50 divided by the base year price (£60) gives us 0.8333 when multiplied by 100.

Lastly, for the base year 2000, the price index is 100, as it is the reference point and used as the denominator in the formula.

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The limit does not exist when X approaches infinity. So, the answer is X = 0.

Given lim (x³e³) / X, with X approaching infinity.

We need to apply L'Hopital's rule.

As this expression is of the form infinity / infinity

So, differentiate the numerator and denominator with respect to X.

d/dx (x³e³) / d/dx (X) = 3x² e³ / 1d/dx (X)

= 1

Now we get the expression lim (3x² e³) / 1 with X approaching infinity

This still gives infinity / infinity, hence we again apply L'Hopital's rule.

d/dx (3x² e³) / d/dx (X)

= 6xe³ / 1

Now we get the expression lim (6xe³) / 1 with X approaching infinity

This expression evaluates to infinity as the numerator is infinity while the denominator is a finite number.

Thus, the limit does not exist when X approaches infinity.

So, the answer is X = 0.

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The area of surface of revolution is 401.354 square units (correct to 3 decimal places).

a) Arc Length:

First, we need to find the derivative of the given function y= 6x + 3 to get dy/dx, then substitute into the following equation:

Let L be the length of the curve, so,  So the length of the curve is 18.438 units (correct to 3 decimal places).

b) Area of surface of revolution:

The formula to find the surface area of revolution:

Substituting values in the above formula, we get:

Solving this integral with x = 6 gives an area of surface of revolution is 401.354 square units (correct to 3 decimal places).

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A confidence interval can be used to define a range of plausible values for an unknown parameter, like the variance ratio.

variances of two portfolios with sample variances of s1^2 and s2^2. Let's calculate the confidence interval for the ratio of population variances 05 using the given information.

[tex](s1^2 / s2^2) * (Fα/2),v2, v1 ≤ (s1^2 / s2^2) * (F1-α/2),v1,v2[/tex]

freedom. We must first determine the degrees of freedom and find the critical values for the F-distribution.In this case, v1 = n1 - 1 = 15 - 1 = 14 and v2 = n2 - 1 = 15 - 1 = 14.Using a significance level of 0.05 and degrees of freedom (14,14), the critical values from the F-distribution table are 0.414 and 2.377. Thus, the 95% confidence interval for the variance ratio is calculated as follows:

[tex](s1^2 / s2^2) * (Fα/2),v2, v1 ≤ (s1^2 / s2^2) * (F1-α/2),v1,v2= (0.0049 / 0.0064) * (2.377) ≤ (0.0049 / 0.0064) * (0.414)= 1.8375 ≤ 1.2156[/tex]

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Mean: The formula for calculating mean is [tex]\[\overline{x}=\frac{\sum\limits_{i=1}^{n}x_{i}}{n}\][/tex]

Variance: The formula for calculating variance is

Given distribution is:1 I 3 4 7 8

P(X=r)0.30.20.1

The mean and variance can be calculated as follows:

Mean calculation:Now, calculating the mean of distribution; we have:\

The variance of the distribution is 1.58.

Summary: The mean of the distribution is 1.3 and the variance of the distribution is 1.58.

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The overdetermined linear system for the linear regression problem, fitting a straight line to the points (-2, 2), (0, 0), (1, 2), and (2, 0), is represented by the matrix equation Az = b, where A is the matrix of coordinates and b is the vector of observed values.

To fit a straight line using linear regression, we can write the overdetermined linear system Az = b, where A is a matrix, z is the vector of unknowns (slope and intercept of the line), and b is the vector of observed values.

Given the points (-2, 2), (0, 0), (1, 2), and (2, 0), we can write the linear system as follows:

A = [x1 1; x2 1; x3 1; x4 1] =

[-2 1;

0 1;

1 1;

2 1]

z = [m; b] (slope and intercept of the line)

b = [y1; y2; y3; y4] =

[2;

0;

2;

0]

Therefore, the overdetermined linear system for the linear regression problem is:

[-2 1; 0 1; 1 1; 2 1] * [m; b] = [2; 0; 2; 0]

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Step-by-step explanation:

the underlined numbers are the answers

You would need approximately $71,428.57 today to set up the scholarship fund at San Jose State University, assuming a discount rate of 7% and an annual pay

To calculate the amount needed today to set up a scholarship fund that pays a student $5,000 per year indefinitely, we can use the concept of perpetuity and the formula for the present value of a perpetuity.

The formula for the present value of a perpetuity is given by:

Present Value = Annual Payment / Discount Rate

In this case, the annual payment is $5,000 and the discount rate is 7%. Plugging these values into the formula, we can calculate the present value:

Present Value = $5,000 / 0.07

Calculating this, we find:

Present Value ≈ $71,428.57

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The instances in which the described variable is binomial are 1) and 4).

A binomial random variable has two possible outcomes, often referred to as "success" and "failure," with a fixed probability for each outcome. In both instances 1 and 4, the random variables satisfy these conditions.

In instance 1, the random variable represents the total number of flips required to get tails in a coin flip. The outcomes are "success" (getting tails) or "failure" (getting heads), with a fixed probability of 0.50 for each outcome. The variable counts the number of trials until the desired outcome is achieved, making it a binomial random variable.

In instance 4, the random variable represents the total number of children from a particular pair of parents with blue eyes. Each child has a 0.30 probability of having blue eyes, which can be considered a "success." The variable counts the number of children with blue eyes among the six siblings, fulfilling the requirements of a binomial random variable.

Instances 2, 3, and 5 do not meet the criteria for a binomial random variable. In instance 2, the variable represents the number of products passing inspection out of 35 tested, but there are five guidelines to meet, indicating a different probability for each product. In instance 3, the variable represents the number of customers who ordered beef out of 254, but there are only two choices, not a fixed probability. In instance 5, the variable represents the number of king cards observed in seven draws without replacement, which does not have a fixed probability for success and involves dependent events.

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To calculate the pooled variance for the hypothesis test, assuming equal variances, we need to combine the variances from the two groups.

The pooled variance is used when the assumption of equal variances between the two groups is reasonable. The formula for pooled variance is: Pooled Variance = ((n1 - 1) * S1^2 + (n2 - 1) * S2^2) / (n1 + n2 - 2) where n1 and n2 are the sample sizes of the two groups, and S1^2 and S2^2 are the variances of the respective groups.

Using the given information:

Group 1 (Glucose): n1 = 15, S1 = 20.3

Group 2 (Placebo): n2 = 15, S2 = 13.5

Calculating the pooled variance:

Pooled Variance = ((15 - 1) * 20.3^2 + (15 - 1) * 13.5^2) / (15 + 15 - 2)

After performing the calculations, the pooled variance is found to be approximately 208.293 when rounded to three decimal places.

The pooled variance for the hypothesis test, assuming equal variances, is approximately 208.293. This value represents the combined variance of the two groups and is used in the calculation of the test statistic in the two-sample t-test.

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If the investment grows by 50% every 20 years, we can use the formula for exponential growth to express the balance, B, t years later.

The formula for exponential growth is given by: B = P * (1 + r)^t.Where: B is the balance after t years.P is the initial investment (principal). r is the growth rate per year.In this case, the initial investment is $100 and the growth rate is 50%, which can be written as 0.5. The time period is t years. Substituting the values into the formula, we get:B = 100 * (1 + 0.5)^t . Simplifying further: B = 100 * (1.5)^t.

Therefore, the formula that expresses the balance, B, t years later is:

B = 100 * (1.5)^t.

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17/36

Step-by-step explanation:

If t he question was check the probability of only red so it would be 12/36 but here it is red and orange so we will add the both 12 + 5 which will be 17 so 17 /36 is the right answer.

Answer:  [tex]\frac{17}{36}[/tex] or 0.472 or 47.2%

Step-by-step explanation:

P( red skittle OR orange skittle)=[tex]\frac{12}{36} +\frac{5}{36}[/tex]

P( red skittle OR orange skittle)=[tex]\frac{17}{36}[/tex] or 0.472 or 47.2%

The time spent unsupervised by the four year-olds in the country is normally distributed with a standard deviation of 1.8 hours. If we randomly select a four year-old who resides in a rural area, we would like to know the time he or she spends alone each day. Part (a) : Here we are supposed to find the mean of time spent alone by a child from a rural area.

Now to find `μ` we substitute the values in the formula and simplify it as follows: `-

1.28 = (3 - μ) / 1.8`.

Now we solve for

`μ` i.e,

Mean of time spent alone by a child from a rural area:

`μ = 3 + 1.8 (1.28)` `μ = 5.184`

Thus, the mean time that a four year-old from a rural area spends alone is `5.184` hours per day.

Part (b) : Here we are supposed to find the standard deviation of time spent alone by a child from a rural area.

Now to find `σ` we substitute the values in the formula and simplify it as follows:

`-1.28 = (x - 5.184) / σ`

Now we solve for `σ` i.e, standard deviation of time spent alone by a child from a rural area:

`σ = (x - 5.184) / -1.28`

Therefore, the standard deviation of time spent alone by a four year-old from a rural area is `(x - 5.184) / -1.28`Part (c) : Here we need to find the probability that a 4-year-old from a rural area spends more than 2 hours alone.

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What is the meaning of statement?