Answer:
The simplified expression for the area of rectangle ABCD is , and the restriction on x is x≠2 .
Step-by-step explanation:
Side AB = Width of rectangle = (5x + 5)/(x + 3)
Side BC = Length of rectangle = (3x + 9)/(2x - 4)
Area of Rectangle = Length * Width
Putting values:
Solving,
The restriction on x is that x ≠ 2, because if x =2 then denominator will be zero.
So, the answer is:
The simplified expression for the area of rectangle ABCD is , and the restriction on x is x≠2 .
B) Calculate the sample proportion.
C) Explain the relationship between the population proportion and the sample proportion.
Answer:
Given:
n = 1003
q = 1 - 0.4606 = 0.5394
a) The sample size and count.
Here the sample size is the number that took part in the poll. It is denoted as n = 1003.
The count is the number that answered yes. Count = 462
b) The sample proportion.
The formula for sample proportion is:
Therefore, sample proportion =
c) The relationship between population proportion and sample proportion.
Since the sample size is greater than 30 (n>30), the sample size is large. Hence, for a large sample, the population proportion is approximately equal to the sample proportion.
This means the population proportion, p = 0.4606
Answer:
A) Sample size n=1003
Count x=462
B) Sample proportion p=0.46
C) The population proportion can be estimated with a confidence interval, with the information given by the sample proportion.
The 95% confidence interval for the population proportion is (0.429, 0.491).
Step-by-step explanation:
A) The sample size include all the adult that answer the poll. The sample size is then n=1003.
The count is the number of adults that answer Yes in this case. The count is x=462.
B) The sample proportion can be calculated dividing the count by the sample size:
C) The population proportion is not known. It can only be estimated with the information given by samples of that population. The statistical inference is the tool by which the sample information can be used to estimate the population characteristics.
With the sample proportion p we can estimate a confidence interval for the population proportion.
We can calculate a 95% confidence interval.
The standard error of the proportion is:
The critical z-value for a 95% confidence interval is z=1.96.
The margin of error (MOE) can be calculated as:
Then, the lower and upper bounds of the confidence interval are:
The 95% confidence interval for the population proportion is (0.429, 0.491).
We have 95% that the population proportion is within this interval
Answer:
40
Step-by-step explanation:
5(8) = x
use 5 as bags and use 8 as number of goldfish
then times the 2 number
Answer:
The linear problem is to maximize , s.a.
subject to
Step-by-step explanation:
Let the decision variables be:
: number of units of product 1 to produce.
: number of units of product 2 to produce.
Let the contributions be:
The objective function is:
The restrictions are:
The linear problem is to maximize , s.a.
subject to
Apply
6. Nadia is comparing costs for two brands of garden compost. For
Brand A, the cost y for x bags is shown in the table For Brand B,
the cost y can be represented by the equation y = 1,99%, where
x represents the number of bags, Which brand costs less for
6 bags of compost? How much less?
Brand B costs less for 6 packs of compost. It costs $5.99 less.
To discover which brand costs less for 6 sacks of compost, let's calculate the fetched of 6 packs of each brand:
Brand A:
6 sacks * $2.99/bag = $17.94
Brand B:
y = 1.99x
For 6 packs of compost, x = 6:
y = 1.99 * 6 = $11.94
In this manner, Brand B costs less for 6 sacks of compost, and it costs $5.99 less.
Brand B costs less for 6 packs of compost. It costs $5.99 less.
Learn more about compost here:
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get the same value as if we added 6
to that number.
Stuck? Watch a video or use a hint.
Report a problem
7 of 7 ..
nyone, anywhere
Imnact
Math by grace
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Answer:
? what's the question??????????????????
Multiplying a number by 3 usually does not yield the same result as adding 6 to it, except in the case of the number 3. For all other numbers, the results are different.
In mathematics, when we multiply a number by 3 it does not usually yield the same value as when we add 6 to the number. However, there is one instance in which this statement is incorrect. Let's consider the number 3. If we multiply 3 by 3 we get 9, and if we add 6 to 3 we also get 9. In all other instances, multiplying a number by 3 will not yield the same result as adding 6 to that number. For example, if we take the number 4, multiplying it by 3 gives us 12, but adding 6 to it gives us 10, which are different.
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Answer:
hello your question has some missing parts below is the missing part
Given below are the analysis of variance results from a Minitab display. Assume that you want to use a 0.05 significance level in testing the null hypothesis that the different samples come from populations with the same mean.
Identify the p-value.
Source DF SS MS F p
Factor 3 13.500 4.500 5.17 0.011
Error 16 13.925 0.870
Total 19 27.425
A) 0.011 B) 4.500 C) 5.17 D) 0.870
answer : p-value = 0.011 ( A )
Step-by-step explanation:
using this information
Source DF SS MS F P
Factor 3 13.500 4.500 5.17 0.011
Error 16 13.925 0.870
Total 19 27.425
significance level = 0.05
given that the significance level = 0.05
and
F statistics are given as : F = 5.17 , F critical = 3.25
hence the p-value = 0.011
from the analysis the p-value is less than the significance level is lower than the significance level
The p-value in a Minitab analysis of variance (ANOVA) test helps determine whether to reject or accept the null hypothesis that the samples all come from populations with the same mean. You would reject the null hypothesis if your p-value is less than the significance level (α = 0.05). Please refer back to your Minitab results to find this p-value.
In the context of your Minitab analysis of variance (ANOVA) results, the p-value that you should be looking at to determine the null hypothesis is not explicitly mentioned in your question. However, based on your description, you want to test the hypothesis that the different samples come from populations with the same mean (null hypothesis).
The p-value represents the probability that you would obtain your observed data (or data more extreme) if the null hypothesis were true. Therefore, if the p-value is less than the significance level (α = 0.05), you would reject the null hypothesis, suggesting that the samples do not all come from populations with the same mean. Conversely, if the p-value is larger than 0.05, you would fail to reject the null hypothesis, suggesting that the samples could come from populations with the same mean.
Please refer back to your Minitab results to find this p-value. Usually, it's labeled in the ANOVA table output as 'P' or 'Prob > F'.
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