Manny, Rachelle, and Peg race each other in carts. There are no other racers, and exactly one racer wins each race (no ties allowed!). The following graph shows an incomplete probability model for who will win any given race.

3/ 4+2/ 5=1 3/ 20

3 forths + 2 fifths = 1 and 3 twentyiths

Hope i helped! :)

Answer:

.25 or 1/4

Step-by-step explanation:

1/3-1/12 is .25 or if you turn it into a fraction it is 1/4.Because one quarter out of a dollar equals .25 cents.

Answer:

3/2y * -5

Step-by-step explanation:

-1/2 * -3y= 3/2

-1/2* 10 = -5

Answer:

The Normal curve with the mean and standard deviations is shown below.

Step-by-step explanation:

According to the Empirical Rule in a normal distribution with mean µ and standard-deviation σ, nearly all the data will fall within 3 standard deviations of the mean. The empirical rule can be broken into three parts:

The length of the thorax in a population of male fruit flies is approximately Normal.

The mean is, µ = 0.800 mm and the standard deviation is, σ = 0.078 mm.

Then:

The Normal curve with the mean and standard deviations is shown below.

Answer:

Answer is 32 if you are doing DeltaMath

Step-by-step explanation:

Answer and Step-by-step explanation:

We can easily add up these two numbers:

    1,563,001

+     253,965

___________

    1,816,966

Now, we need to write out the answer is words:

"One million, eight hundred sixteen thousand, nine hundred and sixty-six"

Hope this helps!

Answer:

Step-by-step explanation:

1,563,001

+ 253,965

___________

1,816,966

In words:

"One million Eight Hundred Sixteen Thousan Nine hundred and. Sixty Six"

Answer:

Check below for the required explanations

Step-by-step explanation:

The population correction factor is given by the formula :

PCF = [(N-n)/(N-1)]^1/2..........(1)

a) When the sample size is smaller than 5% of N. That is n < 0.05N

If n = 0.05N is substituted into the PCF formula, PCF will be approximately 1.

For a value of 1, PCF can be safely ignored.

b) N = 200

i) n = 10% N

n = 0.1 × 200 = 20

Substitute n = 20 into equation (1)

PCF = (200-20)/(200-1)]^1/2

PCF = 0.95

ii) n = 5% N

n = 0.05× 200 = 10

Substitute n = 20 into equation (1)

PCF = (200-10)/(200-1)]^1/2

PCF = 0.98

iii) i) n = 3% N

n = 0.03 × 200 = 6

Substitute n = 20 into equation (1)

PCF = (200-6)/(200-1)]^1/2

PCF = 0.99

iiii) n = 1% N

n = 0.01 × 200 = 2

Substitute n = 20 into equation (1)

PCF = (200-2)/(200-1)]^1/2

PCF = 0.998(approx. =1)

It is noticed that the smaller the sample size, the closer the population correction factor to unity. At 1% of the population, the population correction factor is negligible

Answer:

0.01% probability that the mean ticket price exceeds $33

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the zscore of a measure X is given by:

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

In this problem, we have that:

[tex]\mu = 29.89, \sigma = 5.28, n = 40, s = \frac{5.28}{\sqrt{40}} = 0.8348[/tex]

Find the probability that the mean ticket price exceeds $33

This is 1 subtracted by the pvalue of Z when X = 33. So

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

By the Central Limit Theorem

[tex]Z = \frac{X - \mu}{s}[/tex]

[tex]Z = \frac{33 - 29.89}{0.8348}[/tex]

[tex]Z = 3.73[/tex]

[tex]Z = 3.73[/tex] has a pvalue of 0.9999

1 - 0.9999 = 0.0001

0.01% probability that the mean ticket price exceeds $33

Answer:

[tex]P(\bar X >33)[/tex]

And we can use the z score formula given by:

[tex] z =\frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}[/tex]

And if we find the z score for 33 we got:

[tex] z = \frac{33-29.89}{\frac{5.28}{\sqrt{40}}}= 3.725[/tex]

And we can use the complement rule and we got:

[tex] P(Z>3.725)= 1-P(Z<3.725)= 1-0.9999= 0.0001[/tex]

Step-by-step explanation:

Previous conepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".  

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

Solution to the problem

We know the following properties for the variable of interest:

[tex]\mu = 29.89 , \sigma=5.28 [/tex]

We select a sample size of n = 40>30. From the central limit theorem we know that the distribution for the sample mean [tex]\bar X[/tex] is given by:

[tex]\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})[/tex]

And for this case we want to find this probability:

[tex]P(\bar X >33)[/tex]

And we can use the z score formula given by:

[tex] z =\frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}[/tex]

And if we find the z score for 33 we got:

[tex] z = \frac{33-29.89}{\frac{5.28}{\sqrt{40}}}= 3.725[/tex]

And we can use the complement rule and we got:

[tex] P(Z>3.725)= 1-P(Z<3.725)= 1-0.9999= 0.0001[/tex]

Answer:

  9 = 1 + 2n

Step-by-step explanation:

A sum is indicated with a plus sign (+). Two times a number is represented by the product of 2 and the variable used to represent the number. Here, we have chosen "n" (for "number"). Then the sum is ...

  1 + 2n

"Is" in this context means "equals", so we have ...

  9 = 1 + 2n

Answer:

The scale doesn't start at 0. One could argue the maximum value should be 7, but this is a formatting choice.

Step-by-step explanation:

Answer:

The scale doesn't start at 0. One could argue the maximum value should be 7, but this is a formatting choice.

Step-by-step explanation:

Options:

a. His predicted weight loss was 5.5 pounds higher than his actual weight loss.

b. His actual weight loss was 5.5 pounds higher than his predicted weight loss.

c. His actual weight loss was 5.5 pounds higher than it would have been if he didn’t exercise.

d. His predicted weight loss was 5.5 pounds higher than it would have been if he didn’t exercise.

Answer:

Option B. His actual weight loss was 5.5 pounds higher than his predicted weight loss.

Step-by-step explanation:

In regression analysis, the difference between the observed value of the dependent variable (y) and the predicted value (ŷ) is called the residual (e). Residual value= Observed value - Predicted value.i.e. e = y - ŷ

Since the residual weight of the client was 5.5 pounds, this means that His actual weight after compliance with the exercise program his 5.5 pounds higher than what he predicted.

Final answer:

The client's residual of 5.5 pounds means they lost 5.5 pounds more than predicted by the exercise program's linear regression model, indicating a discrepancy between the predicted and actual weight loss.

Explanation:

When a client has been told that his residual was 5.5 pounds in the context of a linear regression, it refers to the difference between the actual weight the client lost and the weight the regression model predicted they would lose based on their percent compliance with the exercise program. A residual of 5.5 pounds indicates that the client lost 5.5 pounds more than what the model predicted. Residuals are used to measure the accuracy of regression models; if they are small, it suggests that the model is accurately predicting outcomes. However, large or systematic residuals can indicate a problem with the model's fit to the data.

Translation from Google

the area of ​​a rectangle measures 15 square units. if one side of a rectangle is 3.75 units long how much unit is the perimeter of the rectangle?

Answer:

15.5 unidades

Step-by-step explanation:

Area of rectangle is given by the product of its length and width and expressed as

A=lw

Where l is length, w is width and A is area.

Similarly, perimeter is given by

P=2(l+w)

Given that A=15 and w=3.75

15=3.75l

l=15÷3.75=4 unidades

Now perimeter will be

P=2(4+3.75)=2(7.75)=15.5 units

Answer:

24

Step-by-step explanation:

because you need to multiply 6x4x3 divided by 3

Answer:

Bb 24in2

Step-by-step explanation:

It is correct

correct on E D G E N U I T Y

Answer: The accident proportion differ between the two age groups is 0.11.

Step-by-step explanation:

Since we have given that

Number of clients under 18 years of age = 500

Number of clients had had an accident = 180

Proportion of client had an accident = [tex]\dfrac{180}{500}=\dfrac{18}{50}[/tex]

Number of clients aged 18 and older = 600

Number of clients had had an accident = 150

Proportion of client had an accident = [tex]\dfrac{150}{600}=\dfrac{15}{60}[/tex]

So, According to question, we get that

Difference between the two age groups proportions would be :

[tex]\dfrac{18}{50}-\dfrac{15}{60}\\\\=\dfrac{108-75}{300}\\\\=\dfrac{33}{300}\\\\=\dfrac{11}{100}[/tex]

Hence, the accident proportion differ between the two age groups is 0.11.

Answer:

P = -0.004Q + 149

Step-by-step explanation:

The general form of the linear equation is:

[tex]P=aQ+b[/tex]

The slope of the equation (a) can be found by using the two given points (4,000; $133) and (27,000; $41)

[tex]a = \frac{\$41-\$133}{27,000-4,000}\\a=-0.004[/tex]

Applying the point (4,000; $133) to the equation below yields in the linear equation for Price as a function of the number of shirts:

[tex]P-P_0=a(Q-Q_0)\\P-133=-0.004(Q-4,000)\\P = -0.004Q+149[/tex]

The linear equation is:

P = -0.004Q + 149

Answer:

0.0625 gallons per mile

Step-by-step explanation:

To find the sample mean greater than 95% of possible sample means for a population mean of 3.25 and standard deviation of 1.75 with a sample size of 300, apply the Central Limit Theorem to calculate the standard error and use the 95th percentile z-score to compute the desired sample mean.

To find the sample mean that is greater than exactly 95% of possible sample means given a population mean (μ) of 3.25 and a standard deviation (σ) of 1.75 for a sample size (n) of 300, we use the concept of a z-score in conjunction with the Central Limit Theorem.

The Central Limit Theorem states that the sampling distribution of the sample mean will be normally distributed if the sample size is large enough. Since we have a large sample size of 300, we can assume normality. The formula for the standard error of the mean (SEM) is given by σ divided by the square root of n, SEM = σ / √n. In this case, SEM would be 1.75 / √300.

To find the specific sample mean that would exceed 95% of the sample means, we need to find the z-score that corresponds to the 95th percentile, which is typically around 1.645 for a one-tailed test (since we're looking for means greater than a value). We then multiply this z-score by the SEM and add it to the population mean. The calculation is as follows:

By carrying out these calculations, we will obtain the sample mean that is greater than 95% of possible sample means.

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The sample mean that would be greater than exactly 95% of possible sample means is approximately 3.4164.

Given that the population mean [tex](\(\mu\))[/tex] is 3.25 and the population standard deviation [tex](\(\sigma\))[/tex] is 1.75, we can use the z-score formula to find the sample mean [tex](\(\bar{x}\))[/tex] that corresponds to the 95th percentile:

[tex]\[ z = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}} \][/tex]

We know that for a standard normal distribution, the z-score that corresponds to the 95th percentile is approximately 1.645 (since we want the value that is greater than exactly 95%, we use the one-tailed z-score). We can rearrange the formula to solve for [tex]\(\bar{x}\)[/tex]:

[tex]\[ \bar{x} = \mu + (z \times \frac{\sigma}{\sqrt{n}}) \][/tex]

Now we plug in the values:

[tex]\[ \bar{x} = 3.25 + (1.645 \times \frac{1.75}{\sqrt{300}}) \] \[ \bar{x} = 3.25 + (1.645 \times \frac{1.75}{\sqrt{300}}) \] \[ \bar{x} = 3.25 + (1.645 \times \frac{1.75}{17.3205080756888}) \] \[ \bar{x} = 3.25 + (1.645 \times 0.1010101010101) \] \[ \bar{x} = 3.25 + 0.16641664166417 \] \[ \bar{x} = 3.4164166416642 \][/tex]

(a) Null Hypothesis (H0): μ ≤ 100 ppb (The average level of BPA in the tomato soup is less than or equal to 100 ppb)

Alternative Hypothesis (H1): μ > 100 ppb (The average level of BPA in the tomato soup is greater than 100 ppb)

(b) A Type I Error would occur if the agency finds evidence that the average level of BPA is greater than 100 ppb (rejects H0) when, in fact, the true average level is less than or equal to 100 ppb.

(c) A Type II Error would occur if the agency fails to find evidence that the average level of BPA is greater than 100 ppb (fails to reject H0) when, in fact, the true average level is greater

(a) Null Hypothesis (H₀): The average level of BPA in the company's tomato soup is less than or equal to 100 ppb.

Mathematically: μ ≤ 100 ppb

Alternative Hypothesis (H₁):

The average level of BPA in the company's tomato soup is greater than 100 ppb.

Mathematically: μ > 100 ppb

(b) A Type I error occurs when we reject the null hypothesis when it is actually true. In this context, a Type I error would mean that the consumer protection agency concludes that the average BPA level is greater than 100 ppb and recalls the soup and sues the company, when in reality, the average BPA level is less than or equal to 100 ppb. This would result in unnecessary product recall and legal action against the company.  

(c) A Type II error occurs when we fail to reject the null hypothesis when it is actually false. In this context, a Type II error would mean that the consumer protection agency concludes that the average BPA level is less than or equal to 100 ppb and does not recall the soup or sue the company, when in reality, the average BPA level is greater than 100 ppb. This would allow unsafe products to remain on the market, potentially harming consumers.  

The average rate of change of the function g(x) over the interval from x = -8 to x = -4 is -2.

The average rate of change of a function over a certain interval is similar to finding the slope of the secant line that passes through the points on the graph of the function corresponding to the end points of the interval. In this case, for the function g(x) = x^2 + 10x + 23, we want to find the average rate of change over the interval from x = -8 to x = -4.

To do this, we calculate the change in g(x) divided by the change in x (\(\Delta x\)).

The value of the function at x = -8 is g(-8) = (-8)^2 + 10(-8) + 23 = 64 - 80 + 23 = 7.

The value of the function at x = -4 is g(-4) = (-4)^2 + 10(-4) + 23 = 16 - 40 + 23 = -1.

Now, the average rate of change is given by the formula:

Average rate of change = (g(-4) - g(-8)) / (-4 - (-8))

= (-1 - 7) / (-4 + 8) = -8 / 4 = -2

So, the average rate of change of the function g(x) over the interval from x = -8 to x = -4 is -2.

Answer:

89.44% probability that the total weight of the King Salmons caught is greater than 575 lbs

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the zscore of a measure X is given by:

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For sums of size n from a population, the mean is [tex]\mu*n[/tex] and the standard deviation is [tex]\sigma\sqrt{n}[/tex]

The weight of the King Salmons are i.i.d. ∼ Normal with µK = 150 lbs and σK = 10 lbs. 4 king salmons.

So [tex]\mu = 4*150 = 600, \sigma = 10\sqrt{4} = 20[/tex]

What is the probability that the total weight of the King Salmons caught is greater than 575 lbs?

This is 1 subtracted by the pvalue of Z when X = 575. So

[tex]Z = \frac{X - \mu}{\sigma}[/tex]

[tex]Z = \frac{575 - 600}{20}[/tex]

[tex]Z = -1.25[/tex]

[tex]Z = -1.25[/tex] has a pvalue of 0.1056

1 - 0.1056 = 0.8944

89.44% probability that the total weight of the King Salmons caught is greater than 575 lbs

Answer:

Statement A - True.

Statement B - False.

Statement C - True.

Statement D - False.

Step-by-step explanation:

(a) A sequence is convergent if and only if all of its subsequences are convergent - this statement is correct.

(b) A sequence is bounded if and only if all of its subsequences are bounded - this statement is incorrect.

(c) A sequence is monotonic if and only if all of its subsequences are monotonic - this statement is correct.

(d) A sequence is divergent if and only if all of its subsequences are divergent - this statement is incorrect.

Final answer:

In the context of mathematical sequences, statements (a), (b), and (c) regarding convergence, boundedness, and monotonicity are true as all subsequences follow the property of the original sequence. However, statement (d) is false because even divergent sequences can have convergent subsequences.

Explanation:

When analyzing the behavior of sequences and series in mathematics, it's important to understand various characteristics such as convergence, boundedness, monotonicity, and divergence. We can assess the truthfulness of the given statements based on these characteristics.

Assessment of the Statements:

(a) True: A sequence is convergent if and only if all of its subsequences are convergent. This is a fundamental property of convergent sequences, implying that if the original sequence approaches a specific value, every subsequence will also approach that same value.

(b) True: A sequence is bounded if and only if all of its subsequences are bounded. Every subsequence of a bounded sequence also has to be bounded, because it cannot exceed the bounds set by the original sequence.

(c) True: A sequence is monotonic if and only if all of its subsequences are monotonic. Regardless of which elements are chosen to form a subsequence, if the original sequence preserves its direction of progression (either increasing or decreasing), so will the subsequences.

(d) False: A sequence is divergent if and only if all of its subsequences are divergent. Here we have a counterexample: consider the sequence (-1)ⁿ, which diverges. However, it has convergent subsequences, such as the constant subsequences of all 1s or all -1s.

Therefore, statements (a), (b), and (c) are true, while statement (d) is false. Understanding the conditions for convergence, boundedness, and monotonicity is key to studying the behaviors of sequences and leveraging properties like the comparison test for evaluating whether a series converges or diverges.

Answer:

(C) No, the probability of making a second purchase is not equal to the probability of making a second purchase given that a coupon was sent.

Step-by-step explanation:

Let A = the customer makes a second purchase within 30 days and let B = customer is sent a coupon. Events A and B are independent if P(A) = P(A | B).

P(A) = P(the customer makes a second purchase within 30 days) = \frac{50}{100} = 0.5  

100

50

​  =0.5

P(A | B) = P(the customer makes a second purchase within 30 days | customer is sent a coupon) = \frac{34}{60} = 0.567  

60

34

​  =0.567

Because P(A) ≠ P(A | B) making a second purchase is not independent of being sent a coupon.

Answer:

true

Step-by-step explanation:

Answer:

a) [tex]0.81-2.62\frac{0.34}{\sqrt{110}}=0.725[/tex]    

[tex]0.81+2.62\frac{0.34}{\sqrt{110}}=0.895[/tex]    

So on this case the 99% confidence interval would be given by (0.725;0.895)    

b) [tex]n=(\frac{2.58(0.34)}{0.1})^2 =76.95 \approx 77[/tex]

So the answer for this case would be n=77 rounded up to the nearest integer

Step-by-step explanation:

Previous concepts

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".

The margin of error is the range of values below and above the sample statistic in a confidence interval.

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

[tex]\bar X=0.81[/tex] represent the sample mean

[tex]\mu[/tex] population mean (variable of interest)

s=0.34 represent the sample standard deviation

n=110 represent the sample size  

Part a

The confidence interval for the mean is given by the following formula:

[tex]\bar X \pm t_{\alpha/2}\frac{s}{\sqrt{n}}[/tex]   (1)

In order to calculate the critical value [tex]t_{\alpha/2}[/tex] we need to find first the degrees of freedom, given by:

[tex]df=n-1=110-1=109[/tex]

Since the Confidence is 0.99 or 99%, the value of [tex]\alpha=0.01[/tex] and [tex]\alpha/2 =0.005[/tex], and we can use excel, a calculator or a table to find the critical value. The excel command would be: "=-T.INV(0.005,119)".And we see that [tex]t_{\alpha/2}=2.62[/tex]

Now we have everything in order to replace into formula (1):

[tex]0.81-2.62\frac{0.34}{\sqrt{110}}=0.725[/tex]    

[tex]0.81+2.62\frac{0.34}{\sqrt{110}}=0.895[/tex]    

So on this case the 99% confidence interval would be given by (0.725;0.895)    

Part b

The margin of error is given by this formula:

[tex] ME=z_{\alpha/2}\frac{\sigmas}{\sqrt{n}}[/tex]    (a)

And on this case we have that ME =0.1 and we are interested in order to find the value of n, if we solve n from equation (a) we got:

[tex]n=(\frac{z_{\alpha/2} \sigma}{ME})^2[/tex]   (b)

We can assume the following estimator for the population deviation [tex]\hat \sigma =s =0.34[/tex]

The critical value for 99% of confidence interval now can be founded using the normal distribution. And in excel we can use this formla to find it:"=-NORM.INV(0.005;0;1)", and we got [tex]z_{\alpha/2}=2.58/tex], replacing into formula (b) we got:

[tex]n=(\frac{2.58(0.34)}{0.1})^2 =76.95 \approx 77[/tex]

So the answer for this case would be n=77 rounded up to the nearest integer

Answer:

30.5 in

Step-by-step explanation:

Answer:

30.5 inches

Step-by-step explanation:

Since the board is 77.47 cm and 1 inch is 2.54 cm you have to divide 77.47 by 2.54.

Answer:

1.372 × 10^3

Step-by-step explanation:

Answer:

500 tablets is what you give

Final answer:

To provide a daily dose of 600 mg of calcium carbonate with 400 mg tablets, we need to administer 2 tablets because the calculation of 600 mg divided by 400 mg per tablet equals 1.5, and we round up to the next whole tablet.

Explanation:

If a calcium carbonate supplement is ordered at a dose of 600 mg daily, and the available form is 400 mg tablets, we need to determine how many tablets to administer. To calculate this, we divide the total daily dose required by the dosage available per tablet. Therefore, 600 mg / 400 mg/tablet equals 1.5 tablets. Since we cannot give half a tablet, we round up to the next whole tablet, as it is important not to under-dose a medication.

We get:
600 mg / 400 mg per tablet = 1.5 tablets
Thus, we need to administer 2 tablets to achieve the required daily dose of 600 mg.

Answer:

Ounces is smaller than lbs

Step-by-step explanation:

16 ounces = 1 lbs

Ounces is smaller than lbs

Answer:

An ounce is the smallest unit for measuring weight, a pound is a larger unit, and a ton is the largest unit

Step-by-step explanation:

Please give me brainliest

Answer:

~20 oz

Step-by-step explanation:

Originally, Bo had 32 ounces of flour, which is around 30 ounces. Then, he added in 47 ounces, which is about 50. So, now the new total is about 30 + 50 = 80 ounces.

During the week, he used up some of those ~80 ounces of flour. He used up enough so that at the end of the week, there was only 61 ounces, or approximately 60 oz, left. The difference between these two numbers must be how much Bo used:

80 - 60 = 20 ounces

Thus, the answer is about 20 ounces.

Hope this helps!

Bo started with 79 ounces of flour (32 ounces original and 47 ounces added), and ended with 61 ounces remaining, so he used an estimated 18 ounces of flour during the week.

To determine how much flour Bo used during the week, we need to find out how much the total weight of flour was at the beginning of the week, and then subtract the weight of the flour that was left at the end of the week.

At first, Bo's container had 32 ounces of flour. On Monday, he added 47 more ounces. Therefore, the weight of the flour at the beginning of the week was 32 + 47 = 79 ounces.

At the end of the week, the leftover flour weighed 61 ounces. So, to estimate how much flour Bo used, we subtract the leftover weight from the original total weight: 79 ounces - 61 ounces = 18 ounces.

So, Bo used an estimated 18 ounces of flour during the week.

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Answer:

you have to include how much the cd is worth first

Step-by-step explanation:

you would do

cd price x 1.6 (to the power of 10)

Answer:

87 green apples

Step-by-step explanation:

Red apples  : green apples = 2 : 3

Number of red apples = 2x

Number of green apples = 3x

Total apples = 145

3x + 2x = 145

5x = 145

x = 145/5

x = 29

Number of green apples = 3x = 3 * 29 = 87