Spencer goes on a 5 hour work shift. After
7
5
hours, he takes a break. How much of the work shift does he have left?

Answer:

cilejjwkwkwkwkwkwkw

Answer: No they are not equivalent.

Step-by-step explanation: 8(9x-2) = 72x-2

= 8(9x-2) = 72x - 16

= 72x -16 is not equivalent to 72x -2.

Answer:

Add (or subtract) a number from both sides.

Multiply (or divide) both sides by a positive number.

Simplify a side.

Step-by-step explanation:

Hope this helps:)

Step-by-step explanation:

Hey there!

The points P1(3,-3) and midpoint is (-2,1). Let (X,y) be the end point.

Note: Find through using midpoint formula.

midpoint = ( \frac{x1 + x2}{2} , \frac{y1 + y2}{2} )

Put all values.

(\frac{X+3}{2},\frac{-3+y}{2})

Since they are equal, equating with it's corresponding components.

\frac{3 + x}{2} = - 2

-2 = 3 + x

-4 -3= x

Therefore, X= -7.

Now,

\( \frac{ - 3 + y}{2} = 1\)

\( - 3 + y = 2\)

Therefore, y= 5

Therefore, the other end point is (3,5).

Hope it helps...

Answer:

The answer is C. 2/6+4/6+5/6

Step-by-step explanation:

2/6+4/6=6/6 which is 1 and then 1 + 5/6 = 1 5/6

Bryant Industries uses forecasting to estimate the number of orders that will be placed by their customers. The table below gives the sales figures for the last four months.Month2345Sales92491942004There are different forecasting techniques used by companies like Bryant Industries to predict future sales.

One of the most widely used methods is the time-series method. This method is particularly useful when the demand for a product or service changes over time and when there are no external factors that affect the sales. In this case, we can use a time-series method called the moving average to estimate future sales. The moving average is a time-series method that uses the average of past sales data to estimate future sales. It is particularly useful when there are no external factors that affect the sales. In this case, we can use a 3-month moving average to estimate future sales. The 3-month moving average is calculated as follows: (924 + 919 + 420) / 3 = 754.33. This means that we can expect sales of around 754 units next month. The moving average method is easy to use and is a good way to get a quick estimate of future sales. However, it has some limitations. For example, it does not take into account external factors that may affect sales, such as changes in the economy or in consumer behavior.

In conclusion, Bryant Industries can use the moving average method to estimate future sales. However, they should also consider other factors that may affect sales, such as changes in the economy or in consumer behavior. By doing so, they can make more accurate forecasts and improve their overall performance.

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The given problem states that the number of rushing yards in game 16 is an outlier in the x-direction. Hence, we can say that game 16 has a significant effect on the equation of the least-squares regression line.

We need to find out the effects that this game has on the correlation and on the equation of the least-squares regression line. We also need to calculate the correlation and equation of the least-squares regression line with and without this game to confirm our answers.

Effect on Correlation:

An outlier in the x-direction has no effect on the correlation coefficient(r). The correlation coefficient measures the strength and direction of a linear relationship between two variables. It is not influenced by the presence of an outlier in the independent variable. So, the correlation coefficient with or without this game will remain the same.

Effect on Equation of the Least-Squares Regression Line:

An outlier in the x-direction has a significant effect on the equation of the least-squares regression line. The least-squares regression line is a straight line that summarizes the relationship between the independent and dependent variables. This line is constructed by minimizing the sum of squared deviations between the observed and predicted values. If an outlier is present, it will pull the regression line closer to it. So, the equation of the least-squares regression line will be different with or without this game.

Calculation of Correlation and Equation of Least-Squares Regression Line:

Without game 16,

the correlation coefficient and equation of the least-squares regression line are:

Correlation Coefficient(r) = 0.94

The equation of the least-squares regression line:

y = 2.25x + 10.5 With game 16,

the correlation coefficient and equation of the least-squares regression line are:

Correlation Coefficient(r) = 0.93

The equation of the least-squares regression line:

y = 1.92x + 22.8

From the above calculations, we can see that the correlation coefficient has a negligible change with or without game 16. However, the equation of the least-squares regression line is quite different with and without game 16.

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Step-by-step explanation:

100 / 2 (1500/5 (6 + (21 + 7 - 24)))

= 100 / 2 (1500/5 (6 + (28 - 24)))

= 100 / 2 (1500/5 (6 + (4)))

= 100 / 2 (1500/5 (10))

= 100 / 2 (300 (10))

= 100 / 2 (3000)

= 50 (3000)

= 150000

Rule: BODMAS (Brackets first so if there's bracket in bracket solve the bracket in the brackets first)

if left + - x /, solve x and / first from left to right then when there's + - left solve everything from left to right

If you like to venture further, feel free to check out my insta (learntionary). I'll be constantly posting math tips and notes! Thanks!

Answer:

c = 30:2 = 15

x =

\( \sqrt{15 {}^{2} } + 33 {}^{2} \)

(its all under square root)

so x = 36,25.. mate it could be wrong on there

The triangle ABC is equal to triangle ADE by Angle-Angle-Side theorem.

What are triangles?

Triangles are a particular sort of polygon in geometry that have three sides and three vertices. Three straight sides make up the two-dimensional figure shown here. An example of a 3-sided polygon is a triangle. The total of a triangle's three angles equals 180 degrees. One plane completely encloses the triangle.

In triangle ABC and ADE -

It can be seen that ∠B = ∠E.

Also the line segment AB is equal to line segment AE.

AB = AE

The angles ∠BAC and ∠DAE are vertically opposite to each other.

So, they are also equal.

∠BAC = ∠DAE

Angle-Angle-Side is abbreviated as AAS. The triangles are said to be congruent when two angles and a non-included side of one triangle match the corresponding angles and sides of another triangle.

Therefore, Δ ABC ≅ Δ ADE according to AAS theorem.

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let's recall that for any expression, its inverse will have its domain as its range and its range as its domain, now that sounds like a mouthful.

Another way to say it is, if the original function has a domain ⅅ and a range ℝ, then its inverse will have a domain of ℝ and a range of ⅅ, so the inverse  (x , y) pairs are pretty much the same as the original but flipped sideways, for example on the function above we have a point at (π , -0.5), so the inverse function will have a point of (-0.5 , π) pretty much the same thing but flipped sideways.  What the hell all that means?

well, if we look above, the ℝange goes up to 0.5 and down to -0.5, so that means the ⅅomain of the inverse is just that, from 0.5 down to -0.5.

Answer:

a. The population distribution of the duration of your phone calls is likely to be bell shaped. This is because the mean, median, and mode are all equal to 2.4 minutes. This means that the data is evenly distributed around the mean.

b. The distribution of the 45 calls is likely to be bell shaped as well. This is because the sample size is large enough to be representative of the population. Additionally, the sample was randomly selected, so it is unlikely to be biased.

c. The sampling distribution of the sample mean is also likely to be bell shaped. This is because the central limit theorem states that the sampling distribution of the sample mean will be approximately normal, regardless of the shape of the population distribution, as long as the sample size is large enough. In this case, the sample size is 45, which is large enough to satisfy the requirements of the central limit theorem.

It is important to note that the sampling distribution of the sample mean will only be exactly normal if the population distribution is normal. However, in most cases, the sampling distribution will be approximately normal, even if the population distribution is not.

Step-by-step explanation:

The expected profit of the proposal to open the online gambling business is $2,860,000.

To calculate the expected profit of the proposal to open the online gambling business, we need to consider the probabilities and associated profits for each scenario.

The base scenario is that the business generates $8 million in profit each year, which has a probability of 100% - (5% + 2%) = 93%.

The first exception scenario is that the business is wildly successful and generates $26 million in profit each year, which has a probability of 5%.

The second exception scenario is that the business is found to be illegal, resulting in a total loss of $294 million, which has a probability of 2%.

To calculate the expected profit, we multiply each scenario's profit by its probability and sum them up:

Expected profit = (0.93 * $8,000,000) + (0.05 * $26,000,000) + (0.02 * -$294,000,000)

Expected profit = $7,440,000 + $1,300,000 - $5,880,000

Expected profit = $2,860,000

Therefore,  expected profit of the proposal to open online gambling business is $2,860,000.

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Answer:

Distance = 240 km = 240000 m

Time = 3 hour = 3×60 × 60 = 10800 s

Speed = distance/time

= 240000 m/10800 s

= 200/9

= 22.22 m/s.

Answer:

What's the question dude

12:40

Answer:

12:40 hope this will help u

have a great day

Before 5g left, 7.2 minutes had gone by.

Distance, time, and speed are all related to one another in their fundamental concepts. Distance traveled by a body in a given amount of time is its speed. That is Speed = Distance / Time. Normal Speed: Normal Total distance times speed Speed: Speed = Distance x Time Distance = D. (Speed x Time) Distance multiplied by speed equals time. mass This means that it would take 7.2 minutes before you had 5 g left: 0. 502.4. 254.8. 12.57.2. 6.259.6.

The amount of time it takes for a radioactive element to decay to half its original value is known as its half life. The time it takes for a source's activity to decrease to half its initial value is implied by this to be the definition of a half life.

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We can use the equation for the lateral surface area of a right circular cone, s = πr√(r^2 + h^2). Given that the lateral surface area is 364.25 square centimeters and the radius is 5.75 centimeters and the height will be h ≈ √(20.34 - 5.75^2).

The lateral surface area (s) of a cone is given by the formula s = πr√(r^2 + h^2), where r is the radius of the circular base and h is the height of the cone. In this case, the lateral surface area is given as 364.25 square centimeters, and the radius (r) is 5.75 centimeters. We can plug these values into the formula and solve for h:

364.25 = π * 5.75 * √(5.75^2 + h^2)

To isolate h, we need to rearrange the equation and solve for h:

√(5.75^2 + h^2) = 364.25 / (π * 5.75)

Squaring both sides of the equation, we get:

5.75^2 + h^2 = (364.25 / (π * 5.75))^2

Simplifying the right side of the equation, we have:

5.75^2 + h^2 = 20.34

Now, we can solve for h by subtracting 5.75^2 from both sides:

h^2 = 20.34 - 5.75^2

Taking the square root of both sides, we find:

h ≈ √(20.34 - 5.75^2)

Evaluating this expression will give us the height of the large cone-shaped funnel to the nearest hundredth of a centimeter.

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The possible sequences of rolls could there have been Sequence = 120

Given

6 rolls of a die;

Required

Determine the possible sequence of rolls

From the question, we understand that there were three possible outcomes when the die was rolled;

The outcomes are either of the following faces: 1, 2 and 3

Total Number of rolls = 6

Possible number of outcomes = 3

The possible sequence of rolls is then calculated by dividing the factorial of the above parameters as follows;

Sequence = \frac{6!}{3!}

Sequence = \frac{6 * 5 * 4* 3!}{3!}

Sequence = 6 * 5 * 4

Sequence = 120

Hence, there are 120 possible sequence.

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To complete the tables, we multiply the given numbers:

First table:

Second table:

Now, to determine which option is less expensive we compute the total annual expenses for each case:

1.-Rental:

2.- Homeowner:

From the above calculations, we can conclude that the cheaper option based on the given annual expenses is to rent.

Renting is less expensive.

The area of a shape is the amount of space on the shape

The largest possible area of the garden is 1666.67 square yards

Represent the length of the gardens with y, and the widths of the whole garden with x.

So, the perimeter of the garden is:

\(P = 3y + 2x\)

The farmer has 200 yards.

So, we have:

\(3y + 2x= 200\)

The area of the garden is:

\(Area = xy\)

Divide the amount of fencing equally to maximize the area.

So, we have:

\(3y + 2x = 100 + 100\)

This gives

\(3y = 100 \to y = \frac{100}3\)

\(2x = 100 \to x = 50\)

Recall that:

\(Area = xy\)

So, we have:

\(Area = 50 * \frac{100}3\)

Evaluate the product

\(Area = \frac{5000}3\)

\(Area = 1666.67\)

Hence, the largest possible area of the garden is 1666.67 square yards

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The two scatterplots representing the relationship between SAT scores and first-year GPA and SAT scores and second-year GPA can be compared by examining the strength and direction of the relationship displayed in each plot.

In the first scatterplot depicting the relationship between SAT scores and first-year GPA, we would expect to observe a stronger positive correlation between the two variables. This means that higher SAT scores would be associated with higher first-year GPAs. The scatterplot would show the data points more tightly clustered around a line that slopes upwards, indicating a stronger and more consistent relationship between SAT scores and first-year GPA.

In the second scatterplot representing the relationship between SAT scores and second-year GPA, we would expect a weaker positive correlation compared to the first scatterplot. This suggests that while there is still a positive relationship between SAT scores and second-year GPA, it is not as strong as the relationship with first-year GPA. The scatterplot would show a looser clustering of data points, potentially with more variability and a flatter slope compared to the first scatterplot.

By comparing the two scatterplots side by side, we can visually assess the differences in the strength and direction of the relationship between SAT scores and GPA in the first and second years. The first scatterplot would demonstrate a stronger positive correlation, indicating that SAT scores are a better predictor of first-year GPA. The second scatterplot would show a weaker positive correlation, suggesting that SAT scores have a lesser influence on second-year GPA compared to the first year. This comparison allows us to understand the relative importance of SAT scores in predicting academic performance in different stages of a student's college education.

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The Taylor series formula in summation notation f(t) = Σ[n=0 to infinity] { (1/n!)f^n(a)(t-a)^n } where f^n(a) denotes the nth derivative of f(t) evaluated at t = a.

Since the functions f(t) and g(t) have not been given in the question, I cannot provide a specific answer to this question. However, I can provide a general approach to finding the power series expansion of a function about a point.

To find the power series expansion of a function f(t) about a point t = a, we can use the Taylor series formula:

f(t) = f(a) + f'(a)(t-a) + (1/2!)f''(a)(t-a)^2 + (1/3!)f'''(a)(t-a)^3 + ...

where f'(a), f''(a), f'''(a), ... are the first, second, third, and higher-order derivatives of f(t) evaluated at t = a.

To find the first four nonzero terms of the power series expansion, we can calculate the values of f(a), f'(a), f''(a), and f'''(a) at t = a, substitute them into the Taylor series formula, and simplify the resulting expression. The first four nonzero terms will be the constant term, the linear term, the quadratic term, and the cubic term.

To find the general term of the power series expansion, we can write the Taylor series formula in summation notation:

f(t) = Σ[n=0 to infinity] { (1/n!)f^n(a)(t-a)^n }

where f^n(a) denotes the nth derivative of f(t) evaluated at t = a. The general term of the power series expansion is given by the expression in the curly braces. We can use this expression to find any term in the series by plugging in the appropriate values of n and f^n(a).

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If rxy = 0.83, we can conclude that x and y have a relatively strong positive linear relationship or correlation.

The correlation coefficient (r) measures the strength and direction of the linear relationship between two variables, in this case, x and y. The value of r ranges between -1 and 1. A positive value indicates a positive relationship, meaning that as one variable increases, the other variable tends to increase as well.

In this case, with rxy = 0.83, the correlation coefficient is close to 1, suggesting a strong positive linear relationship. This means that when x increases, y also tends to increase, and vice versa. The closer the value of r is to 1, the stronger the linear relationship between x and y.

It is important to note that correlation does not imply causation. While a high correlation coefficient indicates a strong linear relationship, it does not provide information about the underlying cause or direction of the relationship between the variables. Other factors and variables may influence the relationship, and further analysis may be required to understand the nature of the relationship between x and y.

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Answer:

south america and north america

Step-by-step explanation: