20 POINTS
If a car is traveling 100 miles per hour how many feet will it have traveled in 5 seconds - show your work- otherwise if you don't or nonsense answers will be reported.

a) the odds against the team winning its next match are 8/19.

b) the probability of winning a new TV is 3/16.

(a) To find the odds against the rugby team winning its next match, we can use the probability of the team winning. The odds against an event are calculated by subtracting the probability of the event from 1 and expressing it as a ratio.

Probability of the team winning = 11/19

Odds against the team winning = 1 - (11/19) = 8/19

Therefore, the odds against the team winning its next match are 8/19.

(b) To find the probability of winning a new TV, we can use the given odds in favor of winning. The odds in favor of an event can be expressed as a ratio of favorable outcomes to total outcomes.

Odds in favor of winning a new TV = 3/13

Probability of winning a new TV = favorable outcomes / total outcomes

Probability of winning a new TV = 3 / (3 + 13) = 3/16

Therefore, the probability of winning a new TV is 3/16.

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Answer:

Step-by-step explanation:

Yes, the residual plot supports the appropriateness of a linear model because it displays a clear pattern and has roughly equal amounts of positive and negative residuals.

Is the residual plot relatively symmetrical around the centerline?

Yes, if the residual plot is relatively symmetrical around the centerline, it suggests that the linear model is appropriate because it is capturing the variance in the data evenly. If the residual plot is skewed or has outliers, it may indicate that the linear model is not accurately representing the data and a different model may be needed.

A residual plot is a graph that plots the residuals (the differences between the observed values and the predicted values) on the y-axis and the independent variable on the x-axis. The residual plot is used to assess the appropriateness of a linear model. If the residual plot displays a clear pattern and has roughly equal amounts of positive and negative residuals, it suggests that the linear model is appropriate. On the other hand, if the residual plot is scattered randomly or displays a clear pattern, it suggests that the linear model is not appropriate. In this case, the residual plot supports the appropriateness of a linear model because it displays a clear pattern and has roughly equal amounts of positive and negative residuals. This suggests that the linear model is able to accurately predict the dependent variable based on the independent variable.

Therefore, the residual plot supports the appropriateness of a linear model.

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Yes, because it displays a clear pattern and has roughly equal amounts of positive and negative residuals.

What is a residual plot and how does it support the appropriateness of a linear model?

A residual plot is a graphical representation of the residuals (prediction errors) of a regression model. It can be used to assess the appropriateness of a linear model by examining the pattern of the residuals and the presence of any unusual points or trends.

If the residual plot shows a clear pattern, such as a trend or curvature, it suggests that the linear model may not be appropriate for the data. On the other hand, if the residual plot appears random and shows roughly equal amounts of positive and negative residuals, it supports the appropriateness of a linear model.

The residual plot is used to judge whether a linear model is suitable. The linear model may be useful if the residual plot exhibits a distinct pattern and contains nearly equal numbers of positive and negative residuals. On the other hand, it indicates that the linear model is inappropriate if the residual plot is dispersed randomly or exhibits a distinct pattern. The residual plot shows a distinct pattern and about equal numbers of positive and negative residuals, which in this instance confirms the suitability of a linear model. This suggests that the dependent variable may be accurately predicted by the linear model utilising the independent variable.
Therefore, the residual plot confirms that a linear model is adequate.

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Answer:

The correct option is;

y = 12·cos[π/2·(x- 2)] + 18

Step-by-step explanation:

The general equation for the motion of a Ferris wheel as a function of time, t, can be presented as follows;

f(t) = A·cos(B·x + C) + D

Where;

A = The amplitude = Diameter/2 = 24/2 = 12 meters

The period = 4 minutes

B = 2·π/Period = 2·π/4 = π/2

D = The midline = 6 + 12 = 18 meters

Substituting the values gives;

f(x) = 12·cos(π/2·x + C) + 18

At x = 0, we have

f(x) = 6 = 12·cos(π/2×0 + C) + 18

cos( C) = (6 - 18)/12 = -1

∴ C = -π

We therefore have;

f(t) = y = 12·cos(π/2·t - π) + 18 =  12·cos[π/2·(x- 2)] + 18

y = 12·cos[π/2·(x- 2)] + 18.

A) Approx. 1067 is the required sample size to ensure 95% confidence that the sample proportion will not differ from the true proportion by more than 3%.

B) When the previous estimate is 65%, approx. 971 is the sample size needed to achieve 95% confidence that the sample proportion will not differ by more than 3% from the true proportion.

To determine the sample size needed for estimating the proportion of drivers exceeding the speed limit, we can use the formula for sample size calculation for proportions:

n = (Z² * p * (1 - p)) / E²

where:

n = the sample size.

Z = the Z-value associated with the confidence level of 95%.

p = the estimated proportion or previous estimate.

E = the maximum allowable error, which is 3% or 0.03.

We calculate as follows:

A. No previous estimate for p is available:

Here, we will assume p = 0.5 (maximum variance) since we don't have any prior information about the proportion. So, adding the values into the formula:

n = (Z² * p * (1 - p)) / E²

n = ((1.96)² * 0.5 * (1 - 0.5)) / 0.03²

n= (3.842 * 0.5 * (0.5))/0.03²

n = (1.9208*0.5)/0.0009

n ≈ 1067.11

Thus, to be 95% confident that the sample proportion will not differ from the true proportion by more than 3%, a sample size of approximately 1067 is required.

B. Supposing previous studies found that the sample percentage of drivers who exceeded the speed limit is 65%:

Here, we have a previous estimate of p = 0.65:

Putting the values into the formula:

n = (Z²* p * (1 - p)) / E²

n = ((1.96)² * 0.65 * (1 - 0.65)) / 0.03²

n= (3.842 * 0.65 *(0.35))/0.0009

n ≈ 971

Hence, with the previous estimate of 65%, a sample size of approximately 971 is necessary to be 95% confident that the sample proportion will not differ from the true proportion by more than 3%.

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To assess the given fundamentally, we utilize the rules of integration.

When we evaluate∫(20x⁸ + 5x³ - 12/x⁵) dx ,we get the answer as

=(20/9)x + (5/4)x - 12ln|x| + C where, C is a self-assertive steady.

 The fundamental could be a numerical operation that finds the antiderivative of a work, which is the inverse of the subsidiary. The antiderivative of work can be found utilizing the control run of the show and the natural logarithm run of the show.

In this specific case, the necessity to assess are:

∫(20x⁸ + 5x³ - 12/x⁵) dx

Ready to apply the control run the show, which states that the antiderivative of xⁿ is (1/(n+1))x(n+1), where n may be consistent. Utilizing this run the show, we will discover the antiderivatives of each term within the integral:

∫(20x⁸) dx = (20/9)x + C1

∫(5x³) dx = (5/4)x + C2

∫(-12/x⁵) dx = -12ln|x| + C3

where C1, C2, and C3 are constants of integration.

To get the antiderivative of the whole necessarily, we include the antiderivatives of each term:

∫(20x⁸ + 5x³ - 12/x⁵) dx = (20/9)x + (5/4)x - 12ln|x| + C

where C is the consistency of integration.

Subsequently, the solution to the given fundamentally is:

∫(20x⁸ + 5x³ - 12/x⁵) dx = (20/9)x + (5/4)x - 12ln|x| + C

where C is a self-assertive steady. 

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Answer:

3:4

Step-by-step explanation:

12:16

divide by 4 on both sides

3:4

can't simplify any further

so therefore the answer is 3:4

To estimate the number of sandwiches the deli manager should anticipate selling when 178 customers visit the deli, we can analyze the given data and approximate it using a linear function.

By observing the table, we notice that the number of sandwiches sold varies with the number of customers. This indicates a relationship between the two variables.

To estimate the number of sandwiches, we can fit a line to the data points and use the linear function to make predictions. Using a statistical software or a spreadsheet, we can perform linear regression analysis to find the equation of the best-fit line. However, since we are limited to text-based interaction, I will provide a general approach.

Let's assume the number of customers is the independent variable (x) and the number of sandwiches is the dependent variable (y). Using the given data points, we can calculate the equation of the line.

After calculating the linear equation, we can substitute the value of 178 for the number of customers (x) into the equation to estimate the number of sandwiches (y).

Please provide the data points for the number of sandwiches sold corresponding to each number of customers so that I can perform the linear regression analysis and provide a more accurate estimate for you.

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The diameter of the given circle with a circumference of 28.6 centimeters is approximately 9.11 cm.

The circumference of a circle is given by the simple formula: C = πd, where C is the circumference, π is the constant pi and d is the diameter of the circle.

Given that the circumference of the circle is 28.6 cm, we can use the formula to find the diameter:

28.6 = πd

d = 28.6/π

Using 3.14 as an approximation for π, we get:

d ≈ 28.6/3.14 ≈ 9.11

Therefore, the diameter of the circle is approximately 9.11 cm.

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Answer:

0.5

Step-by-step explanation:

The slope of the line is 1/2, so the answer is 0.5

Component 3 must be started on day 197 to meet the delivery date of day 200.

To illustrate the backward schedule, we need to start from the delivery date (day 200) and work our way backward, taking into account the lead times and dependencies of each operation.

(a) Backward schedule:

Operation    |  Work Center  |  Time (hours)  |  Start Day

--------------------------------------------------------

Final Assembly |   Work Center 1  |        1       |     200

Sub-assembly 1 |   Work Center 2  |        2       |     199

Component 4     |   Work Center 3  |        3       |     197

Component 2     |   Work Center 4  |        4       |     196

Component 3     |   Work Center 5  |        3       |     ????

(b) To identify when component 3 must be started to meet the delivery date, we need to consider its dependencies and lead times.

From the backward schedule, we see that component 3 is required for sub-assembly 1, which is scheduled to start on day 199. The time required for sub-assembly 1 is 2 hours, which means it should be completed by the end of day 199.

Since component 3 is needed for sub-assembly 1, we can conclude that component 3 must be started at least 2 hours before the start of sub-assembly 1. Therefore, component 3 should be started on day 199 - 2 = 197 to ensure it is completed and ready for sub-assembly 1.

Hence, component 3 must be started on day 197 to meet the delivery date of day 200.

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Answer:

Step-by-step explanation:

the triangles have two equal sides

and one angle

Answer:

124.6

Explanation:

Let the number be x.

Thus, we have that: 13% of x =16.2

Next, solve for x by cross-multiplying.

Divide both sides by 13.

Thus, 16.2 is 13% of 124.6 (rounded to the nearest tenth).

Answer:

hi i think the answer shouldbe 34/43 as its out of 43 and there is 34 people on the internet. I think this should be write but it may  be wrong. if this is right please vote for brainliest. i hope my answer is right and this helps bye.

18 root

2

Step-by-step explanation:

In this question imagine there is no y

It will be 10 root2 +5 root2 +3 root 2 it will be 18 root 2

Answer:

2

Step-by-step explanation:

change in y ÷ change in x

4÷2=2

The scatterplot with the least squares line provides insights into the relationship between average annual global surface temperature and the years from 2000 to 2015, allowing us to assess trends, strength of correlation, and make predictions within certain limitations.

The scatterplot represents the relationship between the average annual global surface temperature, in degrees Celsius, and the corresponding years from 2000 to 2015. The line drawn on the plot is the least squares line, which is the best fit line that minimizes the overall distance between the observed data points and the line.

The least squares line is determined using a statistical method called linear regression. It calculates the equation of a straight line that represents the trend in the data. This line serves as a mathematical model to estimate the average temperature based on the year.

By analyzing the scatterplot and the least squares line, we can make several observations. Firstly, we can see whether the temperature has been increasing, decreasing, or remaining relatively stable over the given years. If the slope of the line is positive, it indicates a positive correlation, implying that the temperature has been increasing. Conversely, a negative slope suggests a decreasing trend.

Additionally, we can evaluate the strength of the relationship between temperature and time by examining how closely the data points cluster around the line. If the points are closely grouped around the line, it suggests a strong correlation, indicating that the line is a good representation of the data. On the other hand, if the points are more scattered, the correlation may be weaker.

Furthermore, the line can be used to predict the average annual global surface temperature for future years beyond the data range of 2000 to 2015. However, it's important to note that such predictions should be made with caution and considering other factors that may affect global temperatures, such as climate change and natural variability.

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Question

The given scatterplot shows the average annual global surface temperature, in degrees celsius, for each year from 2000 to 2015. the line drawn is the least squares line for the data set.

When the problem asks to evaluate an expression often means to test certain variable with a specific value. However, in this case, evaluating this equation refers to solving it to see if the equation is true or false. So, let's solve it

The first step is to solve products:

Then, we solve the difference:

As you can observe, 17 isn't equal to 12, which means the initial expression is completely false.

Therefore, according to the evaluation, the expression is false.

The value of 4.62 times 3.78 equals 17.4828.

We have to find  4.62 times 3.78 in standard algorithm

To multiply 4.62 by 3.78 using the standard algorithm, follow these steps:

 4.62

x 3.78

------

 27756  (multiply 8 by 2)

+184368  (multiply 7 by 2, then 8 by 6, and add to the previous result)

-------

 17.4828  (the final answer)

Hence, the value of 4.62 times 3.78 equals 17.4828.

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Answer:

- 18x - 45

Step-by-step explanation:

=  12 (-2x + 3) - 3x + 9 (x - 9)

=  -24x + 36 - 3x + 9 (x - 9)

=  -24x + 36 - 3x + 9x - 81

=  -24x + 8x + 36 - 81

=  - 18x - 45

Answer:

Step-by-step explanation:

First, distribute,

12(-2x+3)-3x+9(x-9)

-24x+36-3x+9x-81

Now combine like terms,

Therefore, the answer is -18x-45

Answer:

If I'm correct, the answer should be  negative 40/3.

Step-by-step explanation:

Because you first combine multiplied terms into a single fraction, then you multiply all terms by the same value to eliminate fraction denominators. Now you simplify it, by canceling multiplied terms that are in the denominator and then you multiply the numbers. Now you will divide both sides of the equation by the same term. Finally, you will simplify the equation.

(Hope this helps) I had to get a little help myself on this problem)

Triangle DEF and FGD are congruent to each other as per Side angle Side (SAS) Congruency theorem.

In the attached figure of triangles,

In the triangle DEF and FGD ,

Congruent parts of the triangle from the attached figure are given as follow,

DE ≅ FG

∠EDF ≅ ∠GFD

Common line segment between triangle DEF and FGD is FD.

FD ≅ DF ( common side)

Applying Side angle Side (SAS) Congruency theorem we have,

ΔDEF is congruent to ΔFGD.

Therefore, the two triangles DEF and FGD are congruent by using Side angle Side (SAS) Congruency theorem.

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The given question is incomplete, I answer the question in general according to my knowledge:

Triangle DEF and FGD have been separated. Fill in the missing information.

Attached figure.

Where the above is given, the required angle m∠BAC = 45°.

In triangle ABC. AC represents the foreman’s line of sight to the top of the landfill. Landfill height is BC

The triangle is geometric shape which includes 3 sides and sum of interior angle should not grater than 180°

According to conditions angle b = 90°

The sum of angles of a triangle= 180°

That is a + b + c = 180

Therefore, c = a

       a = (180 - b)/2

          = (180 - 90) / 2

          = 90 / 2

          = 45°

Hence, the required angle m∠BAC = 45°

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Full Question:

Although part of your question is missing, you might be referring to this full question:

Question 1 Create Triangle ABC by drawing AC. Segment AC represents the foreman’s line of sight to the top of the landfill. What is Angle m BAC?

you take 99 divided by 22 = 4.5

I don't understand what exactly your teachers meaning so I would also suggest asking but the answer compered to everything I've learned in math would be 4.5.

have a good day/night!! :)

Therefore, the margin of error, rounded to two decimal places, is approximately 2.27.

To find the margin of error for a random sample, we can use the formula:

Margin of Error = Critical Value * (Standard Deviation / sqrt(Sample Size))

Given:

Sample Size (n) = 150

Test Score Average (Sample Mean) = 70

Standard Deviation (σ) = 10.8

Confidence Level = 0.99

First, we need to find the critical value associated with the confidence level. For a 99% confidence level, the critical value can be found using a standard normal distribution table or a calculator. The critical value corresponds to the z-score that leaves a tail probability of (1 - confidence level) / 2 on each side.

Using a standard normal distribution table or a calculator, the critical value for a 99% confidence level is approximately 2.576.

Now, we can calculate the margin of error:

Margin of Error = 2.576 * (10.8 / sqrt(150))

Calculating the square root of the sample size:

sqrt(150) ≈ 12.247

Margin of Error ≈ 2.576 * (10.8 / 12.247)

Margin of Error ≈ 2.27

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The function that will standardize a multivariate normal sample is

Ф(X) = (1/2π)^(p/2).|M|^(-1/2).exp(-1/2(X-m)'M^(-1)(X-m))

According to a multivariate normal distribution with mean vector m and variance-covariance matrix M, then this random vector, X , will have the joint density function as shown:

Ф(X) = (1/2π)^(p/2).|M|^(-1/2).exp(-1/2(X-m)'M^(-1)(X-m))

where, |M| denotes the determinant of the variance-covariance matrix M and M^(-1) is just the inverse of the variance-covariance matrix M.

Hence, the function that will standardize a multivariate normal sample is

Ф(X) = (1/2π)^(p/2).|M|^(-1/2).exp(-1/2(X-m)'M^(-1)(X-m))

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Answer:

can you copy and paste it on a question its kinda blurry for me

Step-by-step explanation:

Answer:

B / The Girl

Step-by-step explanation:

.

Answer:

1/6

Step-by-step explanation:

1/2 ÷ 3 = 1/2 × 1/3 = 1/6

Water will reach a depth of 12 meters after 3.1 hours approximately.

What is function?

In mathematics, a function from a set X to a set Y assigns to each element of X exactly one element of Y.

Main body:

Function representing the level of water by 'y' and number of hours by 'x' is,

y = 2.5 cos (2πx/12.5)+12

For y = 12 meters, (Substitute the value of y)

12 = 2.5 cos (2πx/12.5)+12

12 -12 = 2.5 cos (2πx/12.5)

cos (2πx/12.5) = 0

2πx/12.5 = π/2

πx/12.5 = π/4

x = 3.125

x ≈3.1 hours

Therefore, water will reach a depth of 12 meters after 3.1 hours approximately.

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