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ahrayia [7]
2 years ago
11

Linear function, pls help

Mathematics
1 answer:
True [87]2 years ago
4 0

Answer:

A

Step-by-step explanation:

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I need help ASAP, with numbers 3-6 above. Please help.
MatroZZZ [7]

Answer:

3. 2 6 30          6. 8:5

   3 9 45

Step-by-step explanation:

6 0
3 years ago
The effect of a monetary incentive on performance on a cognitive task was investigated. The researcher predicted that greater mo
riadik2000 [5.3K]

Answer:

1) H_0:\mu_5=\mu_{25}=\mu_{50}

2) H_a:\mu_{50}>\mu_{25}>\mu_{5}

3) A Type I error happens when we reject a null hypothesis that is true. In this case, that would mean that the conclusion is that there is evidence to support the claim that the greater the incentive, the more puzzles are solved, but that in reality there is no significant difference.

4) A Type II error happens when a false null hypothesis is failed to be rejected. In this case, that would mean that there is no enough evidence to support the claim that the greater the incentive, the more puzzles are solved, but in fact this is true.

5) The probability of a Type I error is equal to the significance level, as this is the chance of having a sample result that will make the null hypothesis be rejected.

Step-by-step explanation:

As the claim is that the greater the incentive, the more puzzles were solved, the null hypothesis will state that this claim is not true. That is that there is no significant relation between the incentive and the amount of puzzles that are solved. In other words, the mean amount of puzzles solved for the different incentives is equal (or not significantly different):

H_0:\mu_5=\mu_{25}=\mu_{50}

The research (or alternative hypothesis) is that the greater the incentive, the more puzzles were solved. That means that the mean puzzles solved for an incentive of 50 cents is significantly higher than the mean mean puzzles solved for an incentive of 25 cents and this is significantly higher than the mean puzzles solved for an incentive of 5 cents.

H_a:\mu_{50}>\mu_{25}>\mu_{5}

A Type I error happens when we reject a null hypothesis that is true. In this case, that would mean that the conclusion is that there is evidence to support the claim that the greater the incentive, the more puzzles are solved, but that in reality there is no significant difference.

A Type II error happens when a false null hypothesis is failed to be rejected. In this case, that would mean that there is no enough evidence to support the claim that the greater the incentive, the more puzzles are solved, but in fact this is true.

The probability of a Type I error is equal to the significance level, as this is the chance of having a sample result that will make the null hypothesis be rejected.

4 0
3 years ago
190 = 200^b, make b the subject​
MArishka [77]

Answer:

b = \frac{ln190}{ln200}

Step-by-step explanation:

Using the rule of logarithms

log x^{n} ⇔ nlogx

Given

190 = 200^{b} ( take the natural log ln of both sides )

ln190 = ln200^{b} = bln200 ( divide both sides by ln200 )

\frac{ln190}{ln200} = b

3 0
2 years ago
Complete factorization of x^3 - 4x^2 + x - 4
Pachacha [2.7K]

Step-by-step explanation:

x³ - 4x² + x - 4

= x²(x - 4) + (x - 4)

= (x² + 1)(x - 4).

Since the quadratic factor (x² + 1) is irreducible any further, this is our answer.

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3 years ago
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Where did Hip-Hop get its start?
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Answer:

New York

Step-by-step explanation:

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2 years ago
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