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valentina_108 [34]
3 years ago
9

Use the given information to construct a 99% confidence interval estimate of the mean of the population. n = 39, σ = 3.86, Xbar

= 41.7
40.1 < μ < 43.3



40.5 < μ < 42.9



–9.4 < μ < 92.8



41.4 < μ < 42.0
Mathematics
1 answer:
horsena [70]3 years ago
4 0

Answer:

40.1 < μ < 43.3

Step-by-step explanation:

Margin of error = critical value × standard error

ME = CV × SE

At 99% confidence and 38 degrees of freedom, CV = 2.712.

SE = s / √n

SE = 3.86 / √39

SE = 0.618

So the margin of error is:

ME = 2.712 × 0.618

ME = 1.7

So the 99% confidence interval of the population mean is:

μ = 41.7 ± 1.7

40.0 < μ < 43.4

Closest answer is the first one.

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Julie and Eric row their boat (at a constant speed) 55 miles downstream for 5 hours, helped by the
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Answer: C = 3 mph is the rate of the current.

Step-by-step explanation:

Hey there!

<u />

<u>You can use the distance formula (d = rt) to solve this problem.  </u>

Downstream trip: d = 55 miles, t = 5 hours, and r = R + C  

R + C is their rowing speed (R) plus the rate of the current (C).  

Upstream trip: d = 55 miles, t = 11 hours, and r = R - C

R - C is their rowing speed (R) minus the rate of the current (C).  

Downstream: 55 mi. = (R + C)5 hrs. Divide both sides by 5: R + C = 11

Upstream: 55 mi. = (R - C)11 hrs. Divide both sides by 11: R - C = 5

<u>Calculations</u>

R + C = 11 Subtract C from both sides.

R = 11 - C Substitute this into: R - C = 5

(11 - C) - C = 5 Simplify and solve for C

11 - 2C = 5 Add 2C to both sides.

11 = 2C + 5 Subtract 5 from both sides.

6 = 2C Divide both sides by 2

C = 3 mph is the rate of the current.

~I hope I helped you! :)~

5 0
3 years ago
IMAGE BELOW I WILL GIVE BRAINLIEST
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Answer:

for L 2, 2 and for LM it is -8,8

Step-by-step explanation:

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Which system of equations has no solutions?
baherus [9]

Answer:

A

Step-by-step explanation:the lines do not cross therefor the two equations do not share a solution.

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tensor-on-tensor regression: riemannian optimization, over-parameterization, statistical-computational gap and their interplay
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The purpose of the tensor-on-tensor regression, which we examine, is to relate tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without being aware of its intrinsic rank beforehand.

By examining the impact of rank over-parameterization, we suggest the Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN) methods to address the problem of unknown rank. By demonstrating that RGD and RGN, respectively, converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized scenarios, we offer the first convergence guarantee for the generic tensor-on-tensor regression. According to our theory, Riemannian optimization techniques automatically adjust to over-parameterization without requiring implementation changes.

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2 years ago
Select the correct answer. A triangle has vertices (-1, 2), (3, 1), and (7, 2). What is the approximate perimeter of th...
Eva8 [605]

Answer:

16.24 Units

Step-by-step explanation:

The perimeter of the triangle is the sum of the length of the sides of the triangle. Given the points on the vertices, the length of each side may be found using the formula

Length = √(x2 - x1)^2 + (y2 - y1)^2

Considering the pair (-1, 2), (3, 1), the length of that side

= √(3 -- 1)^2 + (1-2)^2)

= √(16 + 1)

= √17 units

Considering the pair (-1, 2), (7, 2), the length of that side

= √(7 -- 1)^2 + (2-2)^2)

= √(64)

= 8

Considering the pair  (3, 1),  and (7, 2), the length of that side

= √(7 - 3)^2 + (2  - 1)^2

= √(16 + 1)

= √17

Hence the perimeter of the triangle

= √17 + 8 + √17

= 4.12 + 8 + 4.12

= 16.24 Units

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3 years ago
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