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zmey [24]
2 years ago
8

Which problem could be solved by evaluating the expression 4×(−6)?

Mathematics
2 answers:
Olenka [21]2 years ago
5 0

Answer:

C).Susan missed 4 questions on a quiz. Each question is worth 6 points. What is the change in points in her score?

Step-by-step explanation:

Let's check all the equations again, we need something equals -24:

a) If Joseph owes 6$ to his mother he has -6$balance or 6$ payable. Then he pays 4 dollars, he will still have -2$ balance or 2 dollars payable. So this variant is not appropriate.

b)If the bathtub rises 6 millimeters in a minute, then 4 minutes will equal 24. So this variant is not appropriate

c) If we consider Susan could gain maximum A points and 1 question worth 6 points, then by missing 4 questions, it will equal 4*6=24 and she will have A-24 points total. It means she has 24 points less. That's why this missing will impact -24 points to her. This is true variant

d) if one batch requires 4 cups of blueberries, 6 batches will equal 24 cups totally. This variant is not appropriate.

natima [27]2 years ago
4 0
THat would be option c  Her cahnge in points would be 4 * -6 = -24.
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One stats class consists of 52 women and 28 men. Assume the average exam score on Exam 1 was 74 (σ = 10.43; assume the whole cla
Svetllana [295]

Answer:

(A) What is the z- score of the sample mean?

The z- score of the sample mean is 0.0959

(B) Is this sample significantly different from the population?

No; at 0.05 alpha level (95% confidence) and (n-1 =79) degrees of freedom, the sample mean is NOT significantly different from the population mean.

Step -by- step explanation:

(A) To find the z- score of the sample mean,

X = 75 which is the raw score

¶ = 74 which is the population mean

S. D. = 10.43 which is the population standard deviation of/from the mean

Z = [X-¶] ÷ S. D.

Z = [75-74] ÷ 10.43 = 0.0959

Hence, the sample raw score of 75 is only 0.0959 standard deviations from the population mean. [This is close to the population mean value].

(B) To test for whether this sample is significantly different from the population, use the One Sample T- test. This parametric test compares the sample mean to the given population mean.

The estimated standard error of the mean is s/√n

S. E. = 16/√80 = 16/8.94 = 1.789

The Absolute (Calculated) t value is now: [75-74] ÷ 1.789 = 1 ÷ 1.789 = 0.559

Setting up the hypotheses,

Null hypothesis: Sample is not significantly different from population

Alternative hypothesis: Sample is significantly different from population

Having gotten T- cal, T- tab is found thus:

The Critical (Table) t value is found using

- a specific alpha or confidence level

- (n - 1) degrees of freedom; where n is the total number of observations or items in the population

- the standard t- distribution table

Alpha level = 0.05

1 - (0.05 ÷ 2) = 0.975

Checking the column of 0.975 on the t table and tracing it down to the row with 79 degrees of freedom;

The critical t value is 1.990

Since T- cal < T- tab (0.559 < 1.990), refute the alternative hypothesis and accept the null hypothesis.

Hence, with 95% confidence, it is derived that the sample is not significantly different from the population.

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3 years ago
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Step-by-step explanation:

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3 years ago
Consider the following hypothesis test: H0: μ1 - μ2 = 0 Ha: μ1 - μ2 ≠ 0 There are two independent samples taken from the two pop
nlexa [21]

Answer:

The value of the test statistic is z = 1.78

Step-by-step explanation:

Before finding the test statistic, we need to understand the central limit theorem and subtraction of normal variables.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

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Sample 2:

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The test statistic is:

z = \frac{X - \mu}{s}

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0 is tested at the null hypothesis:

This means that \mu = 0

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