One cup of popcorn kernels makes four cups of popped corn. There are eight cups of kernels in a bag. How many cups of popped corn will one bag of kernels make?​

Answer:

x is any Real number

Step-by-step explanation:

We first apply distributive property on the left to get rid of the grouping symbol, and then combining the like terms (linear terms on one side and numerical terms on the other side of the equation):

This equation results on a TRUE equality Zero = Zero, which means that any value we use for "x" in the original expression will always render an equality.

Therefore x is any Real number.

Answer:

Categorical is the correct answer to this question.

Step-by-step explanation:

The variable class standing is "Categorial".

1. As a categorical variable, it is a factor that can accept one of a small, and typically set, range of additional values, assigned each person, and another unit of measurement to a specific group or marginal class on the grounds of some long-lasting.
2. The data obtained may be either prescriptive or numeric.
3. Numbers also make no sense when you allocate significance to certain numbers.
4. Categorical data will help you go there. Classic data is when statistics are obtained in classes or categories.

Answer:

5.91 kilograms

591,000 centigrams

Step-by-step explanation:

These two are WRONG:

59,100 kilograms

59.1 centigrams

Answer:

$26.8

Step-by-step explanation:

To find the discount, you turn the percentage of the discount into a decimal (.33) then multiply it by the original price (40)

$40 x .33 = 13.2

You then subtract the total to the original price of the item (40)

$40 - 13.2 = 26.8

So the discount price of the jacket would be $26.8

Hope this helps!!

-Jerc

Answer:

No! Both are wrong!

Step-by-stepExplanation:

the value of absolute function, (|.|) is always positive

that means -6x always has to be positive

and for -6x to be positive x has to be negative

(since, two negatives make a positive)

therefore, the value of x to the equation will be a negative value and not a positive one.

Ana thinks both positive and negative values of x will be the solution, whereas Ling thinks negative value of x can't be a solution. So, they're both wrong.

ONLYnegativevalue of x will be the solution.

Final answer:

A hypothesis test was conducted to evaluate the treatment's effect. For both variances, we failed to reject the null hypothesis, so we can't conclude that the treatment had a significant effect. The variability of scores plays a crucial role, as more variability makes it harder to identify a significant effect.

Explanation:

To determine if the treatment has a significant effect, we perform a hypothesis test using the sample mean (M), sample variance (s^2), and population mean (μ). The null hypothesis is that there's no effect from the treatment (μ=M), while the alternative hypothesis is that there is an effect (μ≠M).

a. For sample variance s^2=32, we can use the formula for the t score: t = (M - μ)/(s/√n) = (35 - 40)/(√32/√8) = -2.24. Based on a two-tailed t-distribution table, the critical t values for α=.05 and 7 degrees of freedom (n-1) are approximately -2.365 and 2.365. Our t value (-2.24) lies within this range, so we fail to reject the null hypothesis. We cannot conclude that the treatment has a significant effect.

b. Repeat the same process with sample variance s^2=72. The t value is now (35 - 40)/(√72/√8) = -1.48, again falling within the range of the critical t values. We can't conclude that the treatment has a significant effect.

c. As the variability (s^2) of the sample scores increases, it becomes more difficult to find a significant effect. Higher variability introduces more uncertainty, which can mask actual changes caused by the treatment.

Learn more about Hypothesis Testing here:

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Final answer:

To evaluate the effect of a treatment using a two-tailed test with alpha = 0.05, we compare the calculated t-value to the critical t-value. The sample variance influences the outcome of the hypothesis test, with a larger variance leading to a wider critical region.

Explanation:

a. To test if the treatment has a significant effect, we will conduct a two-tailed hypothesis test using the t-distribution. The null hypothesis states that the treatment has no effect (μ = 40), while the alternative hypothesis states that the treatment has an effect (μ ≠ 40). With a sample size of 8, degrees of freedom (df) will be n-1 = 7. We will use the t-test formula to calculate the t-value, and compare it to the critical t-value from the t-table with α = 0.05/2 = 0.025. If the calculated t-value falls outside the critical region, we reject the null hypothesis and conclude that the treatment has a significant effect.

b. Similar to part a, we will conduct a two-tailed t-test using the same null and alternative hypotheses. With a sample size of 8, df = n-1 = 7. We will calculate the t-value using the sample mean, population mean, and sample variance. Comparing the calculated t-value to the critical t-value with α = 0.05/2 = 0.025, if the calculated t-value falls outside the critical region, we reject the null hypothesis and conclude that the treatment has a significant effect.

c. The variability of the scores in the sample, as indicated by the sample variance, influences the outcome of the hypothesis test. In both parts a and b, the sample variance is given. A larger sample variance (s^2 = 72 in part b) indicates more variability in the data, meaning the scores in the sample are more spread out. This leads to a larger t-value and a wider critical region. Therefore, it becomes easier to reject the null hypothesis and conclude that the treatment has a significant effect.

Learn more about Effect of treatment on sample mean here:

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