Solve 3(5x + 7) = 9x + 39.
O A. X=-3
B. X= -10
O c. x = 10
O D. x= 3

Answer:

Missing length = 12

Step-by-step explanation:

Let x represent the missing length.

Since the triangles are similar, ∆KLM ~ ∆KRS, therefore, the ratio of their corresponding side lengths would be the same. This implies that:

KL/KR = KM/KS

KL = 65

KR = 65 - 52

KR = 13

KM = 60

KS = x

Plug in the values

65/13 = 60/x

Cross multiply

65*x = 60*13

65x = 780

Divide both sides by 65

65x/65 = 780/65

x = 12

Answer:

huh

Step-by-step explanation:

that don't make no sense

If a,b,c,d,e are in continued proportion then a/c =  a²/b²

Ratio is defined as a relationship between two quantities, it is expressed one divided by the other.

It is given that

a,b,c,d,e are in continued proportion

We can write it as

a/b = b/c = c/d = d/e = k

d=ek, c=ek², b=ek³ and a=ek⁴

Here a/c

Substitute the values of a and c

⇒ ek⁴/ek²

⇒ k²    [ k = a/b ]

⇒ a²/b²

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Answer:

Smaller Figure 3

Larger Figure 5

Scale factor = 15/25 = 3/5

Answer:

b

Step-by-step explanation:

ax^2+bx+c=0

1/2x^2-3x-2=0

Answer:

B

Step-by-step explanation:

Answer:

[tex]1.5,1.2,0.96,0.768[/tex]

Common ratio between successive terms

[tex]=\frac{1.2}{1.5}[/tex]

[tex]=0.8[/tex]

OAmalOHopeO

The value of the common ratio between successive terms in the sequence is,

⇒ 0.8

An sequence has the ratio of every two successive terms is a constant, is called a Geometric sequence.

Given that;

The sequence is,

⇒ 1.5, 1.2, 0.96, 0.768,...

Hence, The value of the common ratio between successive terms in the sequence is,

⇒ 1.2 / 1.5

⇒ 12/15

⇒ 0.8

Thus, The value of the common ratio between successive terms in the sequence is,

⇒ 0.8

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Answer:

the answer is B ...Extreme Values

Answers:

===========================================

Explanation:

Side MO is twice as long as the midsegment XY. Note how XY and MO are parallel.

This makes

MO = 2*XY = 2*10 = 20

Side MO breaks into two equal halves MZ and ZO

Each of MZ and ZO are 20/2 = 10 units long.

Put another way: XY, MZ and ZO are all the same length (all 10 units long).

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

The diagram shows that segment NO is 18 units long, which cuts in half to 18/2 = 9. This is the length of NY, YO and XZ

Also, MN = 14 which cuts in half to 7. This means MX, XN and YZ are all 7 units each.

Answer:

Kindly check explanation

Step-by-step explanation:

The data table :

Use of SM _UK _China _ Russia _US _ col total

Yes ______480 __215 ___343 __640 _ 1678

No _______320 _ 285 ___357__ 360 _ 1322

Row Total__ 800 _500 __ 700_ 1000_ 3000

H0 : p1 = p2 = p3 = p4

H1 : p1 ≠ p2 ≠ p3 ≠ p4

Test statistic :

χ² = Σ(observed - Expected)² / Expected

Expected value of each cell = (Row total * column total) / N

N = grand total

Expected Values:

447.467 _279.667 _ 391.533 _ 559.333

352.533 _ 220.333 _ 308.467 _ 440.667

χ²=(2.36536+14.9527+6.01605+11.6337+3.00232 18.9793+7.63611+14.7665) = 79.352

Degree of freedom, df = (row-1)*(column-1) = (2 - 1) * (4 - 1) = 1 * 3 = 3

Using the Pvalue from Chisquare calculator :

Pvalue(79.352, 3) = 0.00000000001

Decision region :

Reject H0 ; If Pvalue < α

α = 0.05

Since 0.000000001 < 0.05 ; Reject H0 and conclude that not all population proportion are equal.

Sample proportion :

Phat = number of yes, x / total

For UK, Phat = 480/800 = 0.6

For China , Phat = 215/500 = 0.43

For Russia , Phat = 343/700 = 0.49

For US, Phat = 640/1000 = 0.64

It does not appear that police can use a shoe print length to estimate the height of a​ male.

The given parameters are:

[tex]\begin{array}{cccccc}{Shoe\ Print} & {28.6} & {29.4} & {32.2} & {32.4} & {27.3} \ \\ Height (cm) & {172.5} & {176.7} & {188.4} & {170.1} & {179.2} \ \end{array}[/tex]

Rewrite as:

[tex]\begin{array}{cccccc}{x} & {28.6} & {29.4} & {32.2} & {32.4} & {27.3} \ \\ y & {172.5} & {176.7} & {188.4} & {170.1} & {179.2} \ \end{array}[/tex]

See attachment for scatter plot

To determine the correlation coefficient, we extend the table as follows:

[tex]\begin{array}{cccccc}{x} & {28.6} & {29.4} & {32.2} & {32.4} & {27.3} & y & {172.5} & {176.7} & {188.4} & {170.1} & {179.2} & x^2 & {817.96} & {864.36} & {1036.84} & {1049.76} & {745.29} & y^2 & {29756.25} & {31222.89} & {35494.56} & {28934.01} & {32112.64} & x \times y & {4933.5} & {5194.98} & {6066.48} & {5511.24} & {4892.16} \ \end{array}[/tex]

The correlation coefficient (r) is:

[tex]r = \frac{\sum(x - \bar x)(y - \bar y)}{\sqrt{SS_x * SS_y}}[/tex]

We have:

[tex]n =5[/tex]

[tex]\sum xy =4933.5+5194.98+6066.48+5511.24+4892.16 =26598.36[/tex]

[tex]\sum x =28.6+29.4+32.2+32.4+27.3=149.9[/tex]

[tex]\sum y =172.5+176.7+188.4+170.1+179.2=886.9[/tex]

[tex]\sum x^2 =817.96+864.36+1036.84+1049.76+745.29=4514.21[/tex]

[tex]\sum y^2 =29756.25+31222.89+35494.56+28934.01+32112.64=157520.35[/tex]

Calculate mean of x and y

[tex]\bar x = \frac{\sum x}{n} = \frac{149.9}{5} = 29.98[/tex]

[tex]\bar y = \frac{\sum y}{n} = \frac{886.9}{5} = 177.38[/tex]

Calculate SSx and SSy

[tex]SS_x = \sum (x - \bar x)^2 =(28.6-29.98)^2 + (29.4-29.98)^2 + (32.2-29.98)^2 + (32.4-29.98)^2 + (27.3-29.98)^2 =20.208[/tex]

[tex]SS_y = \sum (y - \bar x)^2 =(172.5-177.38)^2 + (176.7-177.38)^2 + (188.4-177.38)^2 + (170.1-177.38)^2 + (179.2-177.38)^2 =202.028[/tex]

Calculate [tex]\sum(x - \bar x)(y - \bar y)[/tex]

[tex]\sum(x - \bar x)(y - \bar y) = (28.6-29.98)*(172.5-177.38) + (29.4-29.98)*(176.7-177.38) + (32.2-29.98)*(188.4-177.38) + (32.4-29.98)*(170.1-177.38) + (27.3-29.98) *(179.2-177.38) =9.098[/tex]

So:

[tex]r = \frac{\sum(x - \bar x)(y - \bar y)}{\sqrt{SS_x * SS_y}}[/tex]

[tex]r = \frac{9.098}{\sqrt{20.208 * 202.028}}[/tex]

[tex]r = \frac{9.098}{\sqrt{4082.581824}}[/tex]

[tex]r = \frac{9.098}{63.90}[/tex]

[tex]r = 0.142[/tex]

Calculate test statistic:

[tex]t = \frac{r}{\sqrt{\frac{1 - r^2}{n-2}}}[/tex]

[tex]t = \frac{0.142}{\sqrt{\frac{1 - 0.142^2}{5-2}}}[/tex]

[tex]t = \frac{0.142}{\sqrt{\frac{0.979836}{3}}}[/tex]

[tex]t = \frac{0.142}{\sqrt{0.326612}}[/tex]

[tex]t = \frac{0.142}{0.5715}[/tex]

[tex]t = 0.248[/tex]

Calculate the degrees of freedom

[tex]df = n - 2 = 5 - 2 = 3[/tex]

The [tex]t_{\alpha/2}[/tex] value at:

[tex]df =3[/tex]

[tex]t = 0.248[/tex]

[tex]\alpha = 0.01[/tex]

The value is:

[tex]t_{0.01/2} = \±5.841[/tex]

This means that we reject the null hypothesis if the t value is not between -5.841 and 5.841

We calculate the t value as:

[tex]t = 0.248[/tex]

[tex]-5.841 < 0.248 < 5.841[/tex]

Hence, we do not reject the null hypothesis because they do not appear to have any correlation.

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Answer:

a. 16 slug b. 3.2 ft

Step-by-step explanation:

a. Total mass of the rod

Since the linear density at a point of the rod,λ varies directly as the third power of the measure of the distance of the point form the end, x

So, λ ∝ x³

λ = kx³

Since the linear density λ = 2 slug/ft at then center when x = L/2 where L is the length of the rod,

k = λ/x³ = λ/(L/2)³ = 8λ/L³

substituting the values of the variables into the equation, we have

k = 8λ/L³

k = 8 × 2/4³

k = 16/64

k = 1/4

So, λ = kx³ = x³/4

The mass of a small length element of the rod dx is dm = λdx

So, to find the total mass of the rod M = ∫dm = ∫λdx we integrate from x = 0 to x = L = 4 ft

M = ∫₀⁴dm

= ∫₀⁴λdx

= ∫₀⁴(x³/4)dx

= (1/4)∫₀⁴x³dx

= (1/4)[x⁴/4]₀⁴

= (1/16)[4⁴ - 0⁴]

= (256 - 0)/16

= 256/16

= 16 slug

b. The center of mass of the rod

Let x be the distance of the small mass element dm = λdx from the end of the rod. The moment of this mass element about the end of the rod is xdm =  λxdx = (x³/4)xdx = (x⁴/4)dx.

We integrate this through the length of the rod. That is from x = 0 to x = L = 4 ft

The center of mass of the rod x' = ∫₀⁴(x⁴/4)dx/M where M = mass of rod

= (1/4)∫₀⁴x⁴dx/M

= (1/4)[x⁵/5]₀⁴/M

= (1/20)[x⁵]₀⁴/M

= (1/20)[4⁵ - 0⁵]/M

= (1/20)[1024 - 0]/M

= (1/20)[1024]/M

Since M = 16, we have

x' =  (1/20)[1024]/16

x' = 64/20

x' = 3.2 ft

Answer:

72.47

Step-by-step explanation:

FV cos 43 = TV

FV (0.73135370161 ) = 53

FV = 72.468 = 72.47

Answer:

A

Step-by-step explanation:

Using the cosine ratio in the right triangle

cos43° = [tex]\frac{adjacent}{hypotenuse}[/tex] = [tex]\frac{TV}{FV}[/tex] = [tex]\frac{53}{FV}[/tex] ( multiply both sides by FV )

FV × cos43° = 53 ( divide both sides by cos43° )

FV = [tex]\frac{53}{cos43}[/tex] ≈ 72.47 ( to 2 dec. places )

9514 1404 393

Answer:

  ₱6400

Step-by-step explanation:

Let 'b' represent the amount invested in scheme B. Then 13900-b is the amount invested in scheme A. The total interest for 2 years is then ...

  14%(13900-b)(2) +11%(b)(2) = 3508

  1946 -0.03b = 1754 . . . . . . divide by 2, simplify

  -0.03b = -192 . . . . . . . . . subtract 1946

  b = 6400 . . . . . . . . . . . divide by -0.03

The amount invested in scheme B was ₱6400.

Answer:

The p-value of the test is 0.2262 > 0.02, which means that the decision is to fail to reject the null hypothesis.

Step-by-step explanation:

An automobile manufacturer has given its jeep a 51.3 miles/gallon (MPG) rating.

At the null hypothesis, we test if the mean is of 51.3, that is:

[tex]H_0: \mu = 51.3[/tex]

An independent testing firm has been contracted to test the actual MPG for this jeep since it is believed that the jeep has an incorrect manufacturer's MPG rating.

This means that at the alternative hypothesis, we test if the mean is different of 51.3, that is:

[tex]H_0: \mu \neq 51.3[/tex]

The test statistic is:

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

In which X is the sample mean, [tex]\mu[/tex] is the value tested at the null hypothesis, [tex]\sigma[/tex] is the standard deviation and n is the size of the sample.

51.3 is tested at the null hypothesis:

This means that [tex]\mu = 51.3[/tex]

After testing 230 jeeps, they found a mean MPG of 51.1. Assume the population variance is known to be 6.25.

This means that [tex]n = 230, X = 51.1, \sigma = \sqrt{6.25} = 2.5[/tex]

Value of the test statistic:

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

[tex]z = \frac{51.1 - 51.3}{\frac{2.5}{\sqrt{230}}}[/tex]

[tex]z = -1.21[/tex]

P-value of the test and decision:

The p-value of the test is the probability of the sample mean differing from 51.1 by at least 0.2, which is P(|z| > 1.21), which is 2 multiplied by the p-value of z = -1.21.

Looking at the z-table, z = -1.21 has a p-value of 0.1131.

2*0.1131 = 0.2262

The p-value of the test is 0.2262 > 0.02, which means that the decision is to fail to reject the null hypothesis.

9514 1404 393

Answer:

  false

Step-by-step explanation:

The statement is nonsense (false). Regardless of the sign of a product, subtraction plays no part in anything related to it.

Weight of someone who is 5.8ft tall is 149.8 lbs.

A quadratic equation is a method of representation of a unknown variable by some variable of degree upto 2.

a=5.607,b=-12.009,c=30.648

Let, y= weight(lbs) and x=height(ft)

y=5.61*x*x-12x+30.65

y=5.61*5.8*5.8-12*5.8+30.65

y=149.77

y≈149.8lbs.

Weight of someone having height 5.8ft tall is 149.8lbs.

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Answer:

e- 77

d- 52

f- 51

Step-by-step explanation:

Step-by-step explanation:

d=52° (,vertically opposite angle)

77+52+f=180 (linear pair)

f=180- 129

f=51°

52+51+e=180

e=180- 103

e=77°

Answer:

75

Step-by-step explanation:

All of the angles within the triangle must add up to 180 degrees. So, angle 2 =50

The angles 4,2,5 also equal 180 because they are on the blue line. Since angle 2=50 and angle 5= 55 angle 4 must be 75 which is needed to add up to 180.

Answer:

Angle 4 is 75°

b represent angle 2

d represent angle 4

70+60+b=180

130+b=180

b=50

55+50+d=180

105+d=180

d=75

Answer:

400%

Step-by-step explanation:

Is means equals and of means multiply

16 = P *4

Divide each side by 4

16/4 = P4/4

4 = P

Change to a percent by multiply by 100 and adding the percent sign

400% = P

Answer:

Pretty sure its y=4x-5 hope it helped

Step-by-step explanation:

Answer:

2500 mg

Step-by-step explanation:

Since r(t) is the rate at which the drug is being eliminated, we integrate r(t) with t from 0 to ∞ to find the original dose of drug, m. Since all of the drug will be eliminated at time t = ∞.

Since r(t) =  50 (e^-01t - e^-0.20t)

m = ∫₀⁰⁰50 (e^-01t - e^-0.20t)

= 50∫₀⁰⁰(e^-01t - e^-0.20t)

= 50[∫₀⁰⁰e^-01t - ∫₀⁰⁰e^-0.20t]

= 50([e^-01t/-0.01]₀⁰⁰ - [e^-0.20t/-0.02]₀⁰⁰)

= 50(1/-0.01[e^-01(∞) - e^-01(0)] - {1/-0.02[e^-0.02(∞) - e^-0.02(0)]})

= 50(1/-0.01[e^-(∞) - e^-(0)] - {1/-0.02[e^-(∞) - e^-(0)]})

= 50(1/-0.01[0 - 1] - {1/-0.02[0 - 1]})

= 50(1/-0.01[- 1] - {1/-0.02[- 1]})

= 50(1/0.01 - 1/0.02)

= 50(100 - 50)

= 50(50)

= 2500 mg

Answer:

Tandin has 16 children.

Step-by-step explanation:

Total of children:

3+5 = 8(first woman)

2(youngest wife)

1 + 1 = 2(two of the  remaining wives)

So

8 + 2 + 2 = 12

If the ratio of children of  5th wife was 1:3 with the children of other wives.

Thus the 5th wife has 12/3 = 4 children.

How many  children does Tandin have?

12 + 4 = 16

Tandin has 16 children.

Answer:

Answer 2/B

Step-by-step explanation:

The one with Parentheses

-(log6 x-4)

Answer:

X = 64

Step-by-step explanation:

All of the angles are right angles (because of the square at one of the angles shown above). This means each angle equals 90 degrees. If X + 26 = 90, then X = 64 because 90 - 26 = 64. I hope this helps!

Answer: X = 64

Step-by-step explanation:

Answer:

C. 98

Step-by-step explanation:

The proposed regression equation is weight = b + width * m

R2 = 0.423

A.) What is the regression equation for this example?

The estimate for the y-intercepts is b= 2.3013 and the estimate for the slope is m= 0.7963

In general, we can symbolize the estimated regression equation as ^Y= b + m*Xi. For this example you have to replace it with the calculated values of the regression coefficients to obtain the estimated regression equation:

^Y= 2.3013 + 0.7963Xi

B.) What is the explanatory, or predictor, variable in this study?

The explanatory or predictor variable is the variable that is suspected to have an effect over the response variable. In this example the predictor variable is:

X: Width of a horseshoe crab (cm)

C.) If the researcher wanted to test whether there is a statistically significant relationship between these two variables, what would the test statistic be? Calculate it from the table above.

To test if the regression is significant, the parameter of study will be the slope of the regression equation, symbolically: β. If the slope is equal to zero "β=0" then there is no linear regression between the response and explanatory variable. If the slope is different from zero "β≠0" then the regression is significant and the explanatory variable affects the response variable.

The hypotheses are:

H₀: β=0

H₁: β≠0

α: 0.05

The value of the statistic under the null hypothesis is t= 8.48

D.) What can we say about the p-value?

This test is two-tailed and so is the p-value, remember that the p-value is the probabulity of obtaining a value as extreme as the value of the statistic under the null hypothesis. The distribution for this test is a t with n-2= 100-2= 98 degrees of freedom. You can calculate the p-value as:

P(t₉₈≤-8.48) + P(t₉₈≥8.48)= P(t₉₈ ≤ -8.48) + (1 - P(t₉₈ < 8.48) ≅ 0.00001

E.) Ultimately, the reason that we find test statistics is so that we can compare them to a null distribution. For regression, that is a t-distribution based on the degrees of freedom. With 98 degrees of freedom (100-2), we can safely say that the critical t (or the confidence multiplier) is what?

As mentioned before, this test is two tailed, meaning that the rejection region is divided in two:

Critical values ± = ± = ± 1.984

This means that you'll reject the null hypothesis when the statistic is t ≤ -1.984 or if the statistic is t ≥ 1.984-

F.) Find the confidence interval for the slope.

Using a 95% confidence level, the interval for the slope is:

[m ± Sm]

[0.7963 ± 1.984 * 0.0939]

[0.61; 0.98]

G.) Is there a statistically significant relationship? Answer with the test statistic and the confidence interval.

Yes, there is a significant relationship between the width and weight of the horseshoe crabs.

Using the critical value approach:

The calculated statistic is 8.48 and the critical value is ± 1.984, since the statistic is greater than the positive critical value, the decision is to reject the null hypothesis.

If you pay attention to the confidence interval, which was made at a confidence level complementary to the significance level of the hypothesis test, this interval [0.61; 0.98] doesn't include the "zero". Since the interval doesn't include the value of the parameter stated in the null hypothesis, you can conclude that this hypothesis is not true and therefore reject it.

Answer:

the answer is 56 cubic units

Step-by-step explanation:

here is your answer it may help you

There are 700 don't like to admit in both faculties.

The process of combining two or more numbers is called the Addition. The 4 main properties of addition are commutative, associative, distributive, and additive identity.

Given that;

In a survey, survey of 1200 students who have passed SEE 150 like to admit in science Faculty 600 in humanity first and 240 like to admit either of faculties and the rest were found not to be admitted in both faculties​

Hence, not to be admit in any faculty =   N

Science +  Humanity -  Either   + None  = 200

=> 150 + 600 - 240 + N = 1200

=> 750 - 240 + N = 1200

=> N = 700

Hence, 700 don't like to admit in both faculties.

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Answer:

a. mg/c b. 4. It is the speed the object approaches as time goes on.

Step-by-step explanation:

a. Calculate lim v  as t→[infinity]

Since v = mg/c(1 - e^ -ct/m)

[tex]\lim_{t \to \infty} v = \lim_{t \to \infty} (\frac{mg}{c}[1 - e^{-\frac{ct}{m} } ] )[/tex]

[tex]\lim_{t \to \infty} v =[/tex] mg/c(1 - e^(-c(∞)/m))

[tex]\lim_{t \to \infty} v =[/tex] mg/c(1 - e^(-∞/m))

[tex]\lim_{t \to \infty} v =[/tex] mg/c(1 - e^(-∞))

[tex]\lim_{t \to \infty} v =[/tex] mg/c(1 - 0)

[tex]\lim_{t \to \infty} v =[/tex] mg/c(1)

[tex]\lim_{t \to \infty} v =[/tex] mg/c

b. What is the meaning of this limit?

4. It is the speed the object approaches as time goes on.

This is because, since t → ∞ implies a long time after t = 0, the limit of v as t → ∞ implies the speed of the object after a long time. So, the limit of v as t → ∞ is the speed the object approaches as time goes on.

Answer:

There isn't sufficient evidence that support the claim that mean cost differs from $117.91

Step-by-step explanation:

Given that :

Population Mean cost, μ = 117.91

Sample size, n = 36

Sample mean, xbar = 122.57

Sample standard deviation, s = 20

The hypothesis :

H0 : μ = 117.91

H0 : μ ≠ 117.91

Using the one sample t test :

Test statistic

(xbar - μ) ÷ s/sqrt(n)

T = (122.57 - 117.91) ÷ 20/sqrt(36)

T = 4.66 / 3.333

T = 1.398

Decision region :

Reject H0 ; If Pvalue < α

α = 0.10

Degree of freedom, df = n - 1 = 36 - 1 = 35

Pvalue(1.398, 35) = 0.1709

Since 0.1709 > 0.10 ; WE fail to reject H0 ; therefore there isn't sufficient evidence that support the claim that mean cost differs from $117.91