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Lady bird [3.3K]
3 years ago
8

For each of the situations described below,give an example(if it’s possible) or explain why it’s not possible.

Mathematics
1 answer:
Rina8888 [55]3 years ago
4 0

Step-by-step explanation:

a) If the given set of vectors does not span \mathbb{R}^{3} , it means the number of linearly independent vectors are less than 3. So by adding one or more linearly independent vectors with respect to existing vectors, we can convert the set to basis and basis always spans vector space.

eg. S= \left \{ (1,0,0)^{T},(1,2,0)^{T} \right \} this set does not span \mathbb{R}^{3} . Since it has only two vectors and both vectors are linearly independent, so adding one linearly independent vector with respect to the vectors of S viz. (0,0,1)^{T} can span whole \mathbb{R}^{3}.

b) It is not possible. Since if a set of vectors is linearly dependent then after adding linearly independent vectors it will also linearly dependent i.e. addition of a linearly independent vector does not effect on the set.

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Step-by-step explanation:

54-28=26

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3 years ago
A researcher is evaluating the influence of a treatment using a sample selected from a normally distributed population with a me
Bad White [126]

Answer:

The power of the test is 0.67.

Step-by-step explanation:

The complete question is:

A researcher is evaluating the influence of a treatment using a sample selected from a normally  distributed population with a mean of μ = 80 and a standard deviation σ = 20. The researcher  expects a 12-point treatment effect and plans to use a two-tailed hypothesis test with α = 0.05. Compute the power of the test if the researcher uses a sample of n = 16 individuals .

Solution:

The information provided are:

\mu=80\\\sigma = 20\\n = 16\\\alpha=0.05

The expected mean is:

\mu_{\bar x}=80+12=92

The critical <em>z</em>-score at <em>α</em> = 0.05 for a two-tailed test is:

<em>z</em> = 1.96

*Use a <em>z-</em>table.

Compute the test statistic value as follows:

z_{\bar x}=\frac{\mu_{\bar x}-\mu}{\sigma_{\bar x}}=\frac{92-80}{20/\sqrt{16}}=2.4

The power of statistical test is well-defined as the probability that we reject a false null hypothesis.

Power = Area to the right of the critical <em>z</em> under the assumption that H₀ is false.

Location of  critical <em>z</em> (in H₀ is false distribution) = z_{\bar x}-z

                                                                             = 2.4 - 1.96 \\= -0.44

This is negative because the critical z score is to the left of the mean  of the H₀ in false distribution.

Area above z = -0 .44.

Compute the value of P (Z > -0.44) as follows:

P(Z>-0.44)=1-P(Z

Thus, the power of the test is 0.67.

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3 years ago
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Answer:

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