How can she might choose a random sample of five students from her class of 35 students?

Answer:

A is incorrect!  B. should be the correct answer

Step-by-step explanation

After some research, i found that the correct answer is most likely B.  a regression line and trend line are equivalent terms

A trendline and a regression can be the same.

A regression line is based upon the best fitting curve Y= a + bX Most often it’s a least-squares fit (where the squared distances from the points to the line (along the Y-axis) is minimized).

It can be quadratic or logistic or otherwise, but most often it is linear.

A trendline is often constructed by smoothing of the results, making it less peaked. (often by using a moving average); but can also come from ARIMA projections or curve fitting techniques (such as regression).

Let me know if i helped you!

The statement that is true about a trend line is B. A regression line and trend line are equivalent terms.

It should be noted that the trend line doesn't represent only the largest data points. It can also represent the smallest data points.

The regression line and the trend line are not opposite terms but rather, the regression line and trend line are equivalent terms.

In conclusion, the correct option is B.

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Using Romberg integration, f(1) = 4.01.

Based on the given information, we can deduce the order of approximation for [tex]R_{33}[/tex] in Romberg integration as h10, h8, h4, h6. Additionally, we are given the values [tex]R_{21} = 7[/tex] and [tex]R_{22} = 7.21[/tex].

Romberg integration typically follows the pattern R(k, m), where k represents the number of iterations and m denotes the number of function evaluations per iteration.

From [tex]R_{21} = 7[/tex], we can determine that the approximation achieved after two iterations is 7.

From [tex]R_{22} = 7.21[/tex], we can conclude that the approximation achieved after two iterations is 7.21.

Since [tex]R_{21}[/tex] and [tex]R_{22}[/tex] represent the same number of iterations (k = 2), we can directly compare the results of these two approximations.

Therefore, the final answer is: f(1) = 4.01.

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The Economic Stimulus Act of 2008 focused on providing tax relief, while the American Recovery and Reinvestment Act of 2009 focused on a combination of tax cuts, government spending, and investments for economic growth.

the Economic Stimulus Act of 2008 was enacted in response to the economic downturn that occurred during the Great Recession. Its main objective was to provide immediate relief to the economy by implementing tax incentives and rebates for individuals and businesses. The act aimed to encourage consumer spending and investment by putting more money into people's pockets and reducing the tax burden on businesses.

On the other hand, the American Recovery and Reinvestment Act of 2009 was a broader and more comprehensive economic stimulus package. It included a mix of tax cuts, grants, loans, and direct government spending on infrastructure projects, renewable energy, healthcare, education, and other areas. The goal was to not only provide short-term economic relief but also to make long-term investments that would promote sustainable economic growth and job creation.

Overall, while both acts were aimed at stimulating the economy, the focus of the Economic Stimulus Act of 2008 was primarily on tax relief, while the American Recovery and Reinvestment Act of 2009 encompassed a wider range of measures to boost the economy and address various sectors affected by the recession.

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Answer:

Air

Step-by-step explanation:

You can't really measure air

Answer:

Air

Step-by-step explanation:

You cannot measure air because it is invisible.

Answer:

65

Step-by-step explanation:

all triangles angles equal 180°

180-72-43=65

Step-by-step explanation:

the answer is x=-4 ,y=-5

Given that a manufacture has been selling 1350 television sets a week at $480 each. A market survey indicates that for each $15 rebate offered to a buyer, the number of sets sold will increase by 150 per week.

a) The demand function is p(x) = 480 - 15x/150, Where x is the number of television sets sold per week.

b) The company should offer a rebate of $25 to maximize its revenue.

c) The company should offer a rebate of $23 to maximize its profit.

a) Demand function:

p(x) = 480 - 15x/150, Where x is the number of television sets sold per week.

b) To maximize revenue, we need to find the point where the marginal revenue equals zero.

Marginal revenue is the derivative of the revenue function.

Revenue function is given by R(x) = x p(x), where x is the number of television sets sold and p(x) is the price per set.

R(x) = x p(x)

R(n) = (1350 + 150n)(480 - 15n/150)

R(n) = 648000 - 2250n - 45n²

R’(n) = -2250 - 90n

For marginal revenue, we need

R’(n) = 0

2250 - 90n = 0

n = -2250/90

n = -25

So, the company should offer a rebate of $25 to maximize its revenue.

c) Cost function: C(x) = 108000 + 160x

Total revenue (TR) = x p(x)

= (1350 + 150n)(480 - 15n/150)

= 648000 - 2250n - 45n²

Total cost (TC) = C(x)

= 108000 + 160n

Profit (P) = TR - TCP

P = 648000 - 2250n - 45n² - (108000 + 160n)

P = 540000 - 2090n - 45n²

For maximum profit, we need

P’(n) = 0 -2090 - 90n

P'(n) = 0

2090 - 90n = 0

n = -2090/90

n = -23.2

Since n has to be a whole number, the number of TVs sold will be

1350 + 150n = 1350 + 150(-23)

= 1020.

Therefore, the company should offer a rebate of $23 to maximize its profit.

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a. The 90% confidence interval for the true population mean watermelon weight is 30.41 to 35.59 ounces.

b. The 99% confidence interval for the true population mean turtle weight is 54.56 to 57.44 ounces.

c. The 99% confidence interval for the true population mean textbook weight is 56.884 to 67.116 ounces.

d. The 99% confidence interval for the true population proportion of people with kids is 0.464 to 0.576.

e. The 99% confidence interval for the true population proportion of people with kids is 0.585 to 0.675.

a. For the watermelon weights:

Sample mean (m) = 33 ounces

Population standard deviation (σ) = 7.7 ounces

Sample size (n) = 47

Confidence level = 90%

To construct the confidence interval, we can use the formula:

Confidence Interval = m ± Z * (σ/√n)

Where Z is the z-score corresponding to the desired confidence level. For a 90% confidence level, Z = 1.645 (obtained from the standard normal distribution table).

Confidence Interval = 33 ± 1.645 * (7.7/√47)

Confidence Interval ≈ 33 ± 2.586

Confidence Interval ≈ (30.41, 35.59) ounces

The 90% confidence interval for the true population mean watermelon weight is 30.41 to 35.59 ounces.

b. For the turtle weights:

Sample mean (m) = 56 ounces

Population standard deviation (σ) = 2.9 ounces

Sample size (n) = 21

Confidence level = 99%

Using the same formula as above and considering a 99% confidence level, the z-score is Z = 2.576.

Confidence Interval = 56 ± 2.576 * (2.9/√21)

Confidence Interval ≈ 56 ± 1.438

Confidence Interval ≈ (54.56, 57.44) ounces

The 99% confidence interval for the true population mean turtle weight is 54.56 to 57.44 ounces.

c. For the textbook weights:

Sample mean (m) = 62 ounces

Population standard deviation (σ) = 9.4 ounces

Sample size (n) = 34

Confidence level = 99%

Using the same formula as above and considering a 99% confidence level, the z-score is Z = 2.576.

Confidence Interval = 62 ± 2.576 * (9.4/√34)

Confidence Interval ≈ 62 ± 5.116

Confidence Interval ≈ (56.884, 67.116) ounces

The 99% confidence interval for the true population mean textbook weight is 56.884 to 67.116 ounces.

d. For the proportion of people with kids:

Number of people with kids (p) = 104

Sample size (n) = 200

Confidence level = 99%

To construct the confidence interval for a proportion, we can use the formula:

Confidence Interval = p ± Z * √[(p(1-p))/n]

Using the z-score corresponding to a 99% confidence level (Z = 2.576), we can calculate the confidence interval.

Confidence Interval = 104/200 ± 2.576 * √[(104/200)(1 - 104/200)/200]

Confidence Interval ≈ 0.52 ± 0.056

Confidence Interval ≈ (0.464, 0.576)

The 99% confidence interval for the true population proportion of people with kids is 0.464 to 0.576.

e. For the proportion of people with kids:

Number of people with kids (p) = 189

Sample size (n) = 300

Confidence level = 99%

Using the same formula as above and considering a 99% confidence level, the z-score is Z = 2.576.

Confidence Interval = 189/300 ± 2.576 * √[(189/300)(1 - 189/300)/300]

Confidence Interval ≈ 0.63 ± 0.045

Confidence Interval ≈ (0.585, 0.675)

The 99% confidence interval for the true population proportion of people with kids is 0.585 to 0.675.

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Answer:

12z-32

Step-by-step explanation:

-4z-12-20+16z

The differential equation governing the variation of the charge (c) in the given RC circuit is 2(d^2q/dt^2) + (1/0.5)(dq/dt) = d(E(t))/dt, where q represents the charge, t represents time, and E(t) represents the electromotive force of the circuit. This equation describes the relationship between the charge, current, and voltage in the RC circuit.

To compute the differential equation governing the variation of the charge (c) in the given RC circuit, we can start by using Kirchhoff's voltage law (KVL) and the relationship between charge, capacitance, and voltage.

Kirchhoff's voltage law states that the sum of the voltages in a closed loop in a circuit is equal to zero. In this case, the voltage across the resistor (VR) and the voltage across the capacitor (VC) must sum up to the electromotive force (E) of the circuit.

The voltage across the resistor can be calculated using Ohm's Law: VR = IR, where I is the current flowing through the circuit.

The voltage across the capacitor can be calculated using the formula: VC = (1 / C) ∫Idt, where ∫Idt represents the integral of the current over time.

Combining these equations, we have:

E(t) = VR + VC

E(t) = IR + (1 / C) ∫Idt

Differentiating both sides of the equation with respect to time, we get:

d(E(t)) / dt = d(IR) / dt + d((1 / C) ∫Idt) / dt

Since the resistance R and the capacitance C are constant values, their derivatives with respect to time are zero.

d(E(t)) / dt = R(dI / dt) + (1 / C)(I)

Substituting I with dq / dt, where q represents the charge c, we get:

d(E(t)) / dt = R(d^2q / dt^2) + (1 / C)(dq / dt)

Simplifying the equation, we obtain the differential equation governing the variation of c:

R(d^2q / dt²) + (1 / C)(dq / dt) = d(E(t)) / dt

In summary, the differential equation governing the variation of the charge (c) in the given RC circuit is:

2(d^2q / dt²) + (1 / 0.5)(dq / dt) = d(E(t)) / dt

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Answer: Kianna is incorrect, if Elena’s house was a reflection of her house across the y-axis, the coordinates would be (-4,2).

Step-by-step explanation:

Answer:

-5p+3

Step-by-step explanation:

-p-2-4p+5

-p-4p-2+5

-5p+3

Answer:

286 Degrees

Step-by-step explanation:

A circle is 360 degrees, and from the question, we know that it is asking for ACB, so it would be 360 - 74, which is 286 degrees

Brainliest Maybe?

Answer:

y = 4x+1

Step-by-step explanation:

Mark's approach to solving the problem is incorrect.

We have,

To find the sum of percentages, you cannot simply add the numbers together as you would with regular numbers.

Percentages represent proportions or ratios, so they must be converted to their corresponding decimal or fraction forms before adding them.

To solve 12% + 3%, you need to convert each percentage to its decimal form and then add the decimals together.

Here's the correct approach:

12% = 12/100 = 0.12

3% = 3/100 = 0.03

Now, add the decimals:

0.12 + 0.03 = 0.15

Therefore,

The value of 12% + 3% is 0.15 or 15%.

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Thus, the adult male foot length of 31.6 cm is significantly high since it falls outside the range of values considered normal for adult male foot length.

Range rule of thumb:The range rule of thumb is a formula used to calculate the range of the data that's spread around the mean. The range is the difference between the maximum and minimum data values. The range rule of thumb estimates the expected range for a normally distributed dataset by taking the difference between the maximum and minimum values, then multiplying that difference by 4. This estimate is usually only useful for datasets with more than 15 data points. Thus, using the range rule of thumb, the range of a normally distributed data set is approximately four times the standard deviation. Thus, the range of the adult male foot length is as follows:Range = 4 × Standard deviation= 4 × 1.41 cm= 5.64 cmWe can then identify the limits separating values that are significantly low or high as follows:Significantly low values are cm or lower: 28.48 - 2 x 1.41 = 25.66 cmSignificantly high values are cm or higher: 28.48 + 2 x 1.41 = 31.3 cmThus, the adult male foot length of 31.6 cm is significantly high since it falls outside the range of values considered normal for adult male foot length.

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Answer:

14

Step-by-step explanation:

Answer:

Step-by-step explanation:

4(2x+10)=0

8x+40=0

-40       -40

8x=-40

x=-5

Answer:

-5

Step-by-step explanation:

First you have to distribute 4 to 2x and 10, giving us 8x + 40 = 0. Then subtract 40 from both sides, giving us 8x = -40. Now divide -40 by 8, which gives us -5.

Answer:

i dont know i just want points

Step-by-step explanation:

>;)

The correct option is (C) 23.7%.

If the coefficient of determination is 0.237, the percentage of the data about the regression line that is explained is 23.7%.

The coefficient of determination is used to explain how much variability in a dependent variable is due to the independent variable in a regression analysis.

It is usually represented as R², which is a fraction between 0 and 1, or expressed as a percentage between 0% and 100%.

It measures the proportion of variability in the dependent variable that can be attributed to the independent variable(s).

In this problem, the coefficient of determination is given as 0.237, which means that 23.7% of the variability in the dependent variable is explained by the independent variable(s) in the regression line. Therefore, the correct option is (C) 23.7%.

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Answer:

480 - 12.25d = 50

1. The participants would be a member of all four groups. In a within-subjects ANOVA, participants are exposed to all levels of the factor, so they experience each group.

2. The within-groups sums of squares is only found in a one-way within-subjects ANOVA. This represents the variability within each group or condition.

3. In this case, you would typically conduct a post-hoc analysis to determine which specific groups or conditions differ significantly from each other. Common post-hoc tests for within-subjects ANOVA include the Bonferroni or Tukey tests. These tests help identify pairwise differences between the groups.

4. True. Eta-squared, partial eta-squared, and R-squared are all measures of effect size that indicate the proportion of variance explained within a study. They provide information about the strength and magnitude of the effect being studied.

5. When conducting a two-way between-subjects ANOVA, you would typically run two F-tests: one for each main effect and one for the interaction effect. The main effects assess the effects of each independent variable separately, while the interaction effect examines whether the combined influence of the variables is significant.

6. The between-groups sums of squares is only found in a two-way between-subjects ANOVA. It represents the variability between the different groups or levels of the independent variables.

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The set of real numbers with a repeating decimal expansion is countable because it can be mapped to a countable set of unique pairs of rational numbers.

To show that the set of all real numbers with a repeating decimal expansion is countable, we can prove that it is at most countable.

Let's consider a repeating decimal number 0.d1d2d3...dn(dn+1...dn+m-1dn+m...). This can be represented as 0.d1d2d3...dn + (dn+1...dn+m-1dn+m...)/([tex]10^m - 1[/tex]).

We can see that for any repeating decimal number, the part before the repeating block (0.d1d2d3...dn) is a finite decimal representation, which is a rational number.

The part within the repeating block ((dn+1...dn+m-1dn+m...)/([tex]10^m - 1[/tex])) is also a rational number because it can be expressed as a fraction.

Since both the finite decimal part and the repeating block part are rational numbers, we can map each repeating decimal number to a unique pair of rational numbers (finite decimal part, repeating block part).

Now, we know that the set of rational numbers is countable. Therefore, if we can show that the set of unique pairs of rational numbers is countable, then the set of repeating decimal numbers is at most countable.

To show that the set of unique pairs of rational numbers is countable, we can use the fact that the Cartesian product of countable sets is countable.

Since the set of rational numbers is countable, the set of unique pairs of rational numbers (finite decimal part, repeating block part) is also countable.

Thus, we can conclude that the set of all real numbers with a repeating decimal expansion is at most countable.

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1. to find angle x:

angles on a straight line add to 180º : 180-92º = 88º

to find angle y:

exterior angle equals opposite interior angles: 88+22=110º

2. to find angle x:

angles on a straight line add to 180º : 180-118 = 62º

to find angle y:

exterior angle equals opposite interior angles: 62+52 = 114º

sorry if i got any of the angles wrong, the picture was a bit blurry and I couldn't tell that well, let me know if i got it wrong and i'll fix it

The cumulative frequency of the 3rd bin in a frequency distribution table represents the number of data values that are less than the maximum value of the 3rd bin. The correct option is a.

A frequency distribution table, often known as a frequency table, is a table that displays the frequency or amount of occurrences of different values in a dataset. Frequencies may be written as total values or as percentages of the total (relative frequency).

Cumulative frequency is the total frequency of values less than or equal to a given value in a dataset. The cumulative frequency of the 3rd bin in a frequency distribution table represents the total number of data values that are less than or equal to the maximum value of the 3rd bin.

Because it is a cumulative frequency, it contains all the values that came before it as well as its own frequency. The frequency distribution table is used to compute cumulative frequency.

The calculation of cumulative frequency may be done using the data's ascending or descending order.

Thus, the correct option is a.

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Step-by-step explanation:

area of a quadrilateral is b x h

if the base is 7 the height is x and the area is 63 then:

7x=63

divide both sides by 7

7/7x=63/7

x=9

Hope that helps :)

Heather receives $35 every six months from the corporate bond.

The correct answer to the given question is option D.

To calculate how much Heather receives every six months from the corporate bond, we need to determine the semi-annual interest payment.

The annual interest rate is given as seven percent. Since interest is paid semi-annually, we divide the annual interest rate by 2 to get the semi-annual interest rate:

Semi-annual interest rate = 7% / 2 = 3.5%

Next, we calculate the semi-annual interest payment by multiplying the face value of the bond ($1,000) by the semi-annual interest rate:

Semi-annual interest payment = $1,000 * 3.5% = $1,000 * 0.035 = $35

Therefore, Heather receives $35 every six months from the corporate bond.

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Perpendicular means that two lines intersect at a right angle.

D and E have perpendicular sides

(blue rectangle at the bottom left and green trapezoid all the way at the bottom right)

(a) The estimated coefficient of X₂ captures the average impact of X₂ on the response variable Y, given the specified regression model. (b) a higher R² value indicates a better fit of the model to the data and a higher degree of explained variability.

(a.) The estimated coefficient of X₂ in the multiple linear regression equation (Y = 8 + 45X₁ + 16X₂) represents the expected change in the dependent variable (Y) for a one-unit increase in the independent variable X₂, while holding all other independent variables constant.

In this case, the estimated coefficient of X₂ is 16, so for every one-unit increase in X₂, the expected change in Y is an increase of 16 units, assuming X₁ and other variables remain constant.

(b.) The coefficient of determination (R²) is a measure of the proportion of the total variation in the dependent variable (Y) that can be explained by the independent variables (X₁ and X₂) in the regression model. In this case, if the R² value is 0.98, it means that approximately 98% of the total variation in Y can be explained by the linear relationship between X₁, X₂, and the constant term.

A high R² value of 0.98 indicates a very strong fit of the regression model to the data. It suggests that 98% of the variability in the dependent variable Y is accounted for by the independent variables X₁ and X₂, while the remaining 2% may be attributed to other factors or random variation.

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