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MathGuru
Questions (2)
You have two formulas to use:
A = lw -->area = length times width P = 2l + 2w -->perimeter You know the perimeter, which is 200m
3 answers
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Substitute -1 for x. Simplify.
If f(x) = 4x-3, find the following. f(-1)
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Answers (1,419)
Wow its been 6 years since i answered that question
Formula: CI95 = mean ± 1.96(sd/√n) You will need to calculate the mean and standard deviation for your data. In the formula, n = sample size. Once you have what you need, substitute into the formula and determine the confidence interval. I hope this
I would think either one due to the nature of the tests, but check this to be sure.
Formula to find sample size: n = [(z-value)^2 * p * q]/E^2 ... where n = sample size, z-value is found using a z-table for whatever confidence level is used, when no value is stated in the problem p = .50, q = 1 - p, ^2 means squared, * means to multiply,
Formula to find sample size: n = [(z-value)^2 * p * q]/E^2 ... where n = sample size, z-value is found using a z-table for 99% confidence, p = .50 (when no value is stated in the problem), q = 1 - p, ^2 means squared, * means to multiply, and E = 0.19.
Hint: The mean of the sampling distribution is the population mean.
Formula (if you don't use the binomial probability table): P(x) = (nCx)(p^x)[q^(n-x)] Values: n = 6 p = 0.4 q = 1 - p = 1 - 0.4 = 0.6 i. Find P(5) ii. Find P(4), P(5), and P(6). Add together for your total probability. iii. I'll let you try this one on
Here's one way to do this problem: n = 240 p = .03 q = 1 - p = 1 - .03 = .97 For (a), you will need to find P(0) through P(4). Add together, then subtract the total from 1 for the probability. I'll let you try (b). You can use a binomial probability table
Here's one way you might do this: s/[1 + (2.58/√2n)] to s/[1 - (2.58/√2n)] Note: 2.58 represents 99% confidence Find the standard deviation for your data. Use that value for s. Your sample size is 12. I'll let you take it from here.
Substitute t for z in my statements. Sorry for any confusion.
Since your sample size is small, you can use a one-sample t-test formula. With your data: z = (2.8 - 3)/(0.3/√10) Finish the calculation. Next, check a t-table using 9 degrees of freedom (which is n - 1) for a one-tailed test at .05 level of
CI95 = 51.4 ± 1.96 (16.66) Calculate to determine the interval.
standard error = s/√n Note: The square root of the variance is called the Standard Deviation With your data: 2 = (4.472)/√n Solve for n. I hope this is what you were asking.
I'll help with the last two: 9. The alternate hypothesis shows a specific direction in a one-tailed test. 10. 2.262 (degrees of freedom would be n - 1)
Looks like you have a good handle on this, Jen. Keep up the good work!
You might try a formula like this one: s/(1 + 1.96/√2n) to s/(1 - 1.96/√2n) Substitute and calculate. There may be other variations of similar formulas you can use as well.
I see you figured this out on your own. Good job!
Hi Jen! Just an FYI: I'm a "she" instead of a "he" but thanks for the compliments. I'm always glad to help when and where I can. Let's get started. Both questions are the same type of problem. Question 1: Null: pR = pU Alternate: pR > pU Formula: z = (pR -
PsyDAG answered a similar post. See below under "Related Questions" for the prior posting.
95% confidence interval is equivalent to z = 1.96, so if you round to 2, then your calculations for E are almost correct. I think you missed a 0; I get E = 0.04248. The formula used to determine the margin of error is all you should need to answer the
Try z-scores. In this case, use the following: z = (x - mean)/(sd/√n) With your data: z = (165 - 150)/(90/√25) I'll let you finish the calculation. Next, check a z-table for the probability. Remember the question says "165 friends or more" when you are
You are welcome! I'm glad the explanation helped.
Try the binomial probability formula: P(x) = (nCx)(p^x)[q^(n-x)] n = 15 x = 4 p = .20 q = 1 - p = .80 Substitute the values into the formula and calculate your probability.
Formula: n = {[(z-value) * sd]/E}^2 ...where n = sample size, sd = standard deviation, E = maximum error, and ^2 means squared. Using the values you have in your problem: n = {[(1.96) * 15]/3}^2 Calculate for sample size. Round your answer.
Some notes on finite population correction factor: If the population is small and the sample is large (more than 5% of the small population), use the finite population correction factor. For standard error of the mean, use: sd/√n If you need to adjust
Let's try a binomial proportion one-sample z-test. Ho: p = 0.65 Ha: p does not equal 0.65 Test statistic: z = (0.49 - 0.65)/√[(0.65)(0.35)/100] z = -3.35 The null would be rejected at the .05 level for a two-tailed test (p does not equal 0.65). Use a
Formula: n = {[(z-value) * sd]/E}^2 ...where n = sample size, sd = standard deviation, E = maximum error, and ^2 means squared. Using the values you have in your problem: n = {[(1.96) * 15]/3}^2 Calculate for sample size. Round your answer.
The closer it is to 1 or -1, the stronger the correlation. A correlation of 0.3 is a weaker relationship between variables.
Standard deviation = √npq 1) n = 157 p = .16 (for 16%) q = 1 - p = 1 - .16 = .84 2) n = 209 p = .12 (for 12%) q = 1 - p = 1 - .12 = .88 Substitute and calculate.
Try z-scores: z = (x - mean)/sd Your data: 2.33 = (x - 40)/1 Solve for x. Note: 2.33 represents the z-score corresponding to the 1% in your problem.
Formula: P(x) = (nCx)(p^x)[q^(n-x)] For (a): x = 5 p = .16 q = 1 - p = 1 - .16 = .84 n = 29 Substitute and calculate for your probability. For (b): x = 0,1,2,3,4 p,q,n stay the same. Add each probability you calculate for the total probability. For (c):
Formula to find sample size: n = [(z-value)^2 * p * q]/E^2 ... where n = sample size, z-value is 1.96 and is found using a z-table for 95% confidence, p = .5 (when no value is stated), q = 1 - p, ^2 means squared, * means to multiply, and E = .02 (or 2%).
Formula: P(x) = (nCx)(p^x)[q^(n-x)] Your data: x = 2 p = .05 q = 1 - p = 1 - .05 = .95 n = 20 Substitute and calculate for your probability.
You will need a confidence interval formula for the difference of two population means. Use 1.96 for z (representing 95% confidence). Substitute what you know into the formula and calculate.
Using a Poisson distribution: P(x) = (e^-μ) (μ^x) / x! e = 2.71828 µ = 1.4 x = 4 Substitute and calculate.
Correction: Your calculation is correct. z = 5.156 You would still reject the null and accept the alternate hypothesis. Sorry for any confusion.
Use a one-sample z-test for proportions. With your data: z = (.43 - .50)/√(.50)(.50)/1000) = -4.358 Reject the null and accept the alternate hypothesis (p < .50).
Formula: z = (sample mean - population mean)/(standard deviation divided by the square root of the sample size) With your data: z = (26600 - 25000)/(3800/√150) = 4.96 You can reject the null and accept the alternate hypothesis (µ > 25000).
(B) T cells
Using this formula: P(x) = (nCx)(p^x)[q^(n-x)] Note: q = 1 - p We have: P(x) = (3Cx)(.52^x)[.48^(3-x)]
z = 2.575 for a 99% confidence. Since your sample sizes are large, I would use a two-sample confidence interval formula for large sample sizes. Critical value at .05 using a z-table for a one-tailed test is z = 1.645 If you use a t-table instead to find
Hypotheses: Ho: µ = 10 -->null hypothesis Ha: µ < 10 -->alternate hypothesis
A. elastin B. collagen C. telomeres Check this!
a) ± 3.1 b) 56.7 - 3.1 = 53.6 56.7 + 3.1 = 59.8 Interval is 53.6 to 59.8
Use z-scores. For this problem: z = (x - mean)/(sd/√n) With your data: z = (8.2 - 8)/(2/√100) = 1.00 z = (8.8 - 8)/(2/√100) = 4.00 Answer: .1586 is the probability (check a z-table between z = 1.00 and z = 4.00)
50th percentile
Welch's t-test for unequal variances: t = (mean1 - mean2)/√(s^2/n1 + s^2/n2) t = (4.31 - 3.68)/√(0.17^2/10 + 0.22^2/10) t = 0.63/0.08792 = 7.166 (rounded)
If you do a proportional z-test with this data, it would look something like this: z = (.258 - .20)/√[(.20)(.80)/120] Note: 31/120 = .258 Calculating: z = .058/.0365 = 1.589 The null would not be rejected and the conclusion would be that there is no
Answer: That Y increases by 20 units for each unit increase in X.
Formula: n = {[(z-value)(sd)]/E}^2 a) n = [(1.96 * 7)/2]^2 b) n = [(2.575 * 7)/2)^2 Calculate. Round to the next highest whole number.
