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Alex787 [66]
3 years ago
9

Use the similar shape concept of geometry to find the missing value (x). Round your answer to the nearest tenth if necessary.

Mathematics
1 answer:
trapecia [35]3 years ago
7 0

Step-by-step explanation:

7:3

4:x

-cross multiply

7x = 12

x = 12÷7

x = 1.7

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A psychologist would like to examine the effect of fatigue on mental alertness. An attention test is prepared which requires sub
lianna [129]

Answer:

The calculated value of t= 0.1908 does not lie in the critical region t= 1.77 Therefore we accept our null hypothesis that  fatigue does not significantly increase errors on an attention task at 0.05 significance level

Step-by-step explanation:

We formulate null and alternate hypotheses are

H0 : u1 < u2 against Ha: u1 ≥ u 2

Where u1 is the group tested after they were awake for 24 hours.

The Significance level alpha is chosen to be ∝ = 0.05

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Degrees of freedom is calculated df = υ= n1+n2- 2= 5+10-2= 13

Here the difference between the sample means is x`1- x`2= 35-24= 11

The pooled estimate for the common variance σ² is

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The test statistic is

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The calculated value of t= 0.1908 does not lie in the critical region t= 1.77 Therefore we accept our null hypothesis that  fatigue does not significantly increase errors on an attention task at 0.05 significance level

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3 years ago
The length of human pregnancies from conception to birth follows a distribution with mean 266 days and standard deviation 15 day
Katena32 [7]

Answer:

a) 81.5%

b) 95%

c) 75%

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Standard Deviation, σ = 15 days

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z_{score} = \displaystyle\frac{x-\mu}{\sigma}

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P(236 \leq x \leq 281)\\\\= P(\displaystyle\frac{236 - 266}{15} \leq z \leq \displaystyle\frac{281-266}{15})\\\\= P(-2 \leq z \leq 1)\\\\= P(z \leq 1) - P(z < -2)\\= 0.838 - 0.023 = 0.815 = 81.5\%

b) a) P(last between 236 and 296)

P(236 \leq x \leq 281)\\\\= P(\displaystyle\frac{236 - 266}{15} \leq z \leq \displaystyle\frac{296-266}{15})\\\\= P(-2 \leq z \leq 2)\\\\= P(z \leq 2) - P(z < -2)\\= 0.973 - 0.023 = 0.95 = 95\%

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Then, according to Chebyshev's theorem, at least 1-\dfrac{1}{k^2}  data lies within k standard deviation of mean.

For k = 2

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Step-by-step explanation:

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