1.A researcher is investigating the effect of a new drug to lower anxiety. The drug was shown to be safe in humans, and the researcher wants to test what dosage of the drug is needed. He assigned twenty participants with clinical anxiety disorder to four treatment groups, then gave each the treatment regimen for four weeks. At the end of the trial, the participants took an anxiety test. The scores, corrected for initial anxiety score, are reported in the table below. Lower scores indicate lower anxiety levels. Analyze the data to determine if there is any difference in anxiety scores between the groups, and if there is a difference, determine and explain which treatment is most effective

1. What kind of statistical test will you be performing?
2. Will you need to test for equal variance? If so, what are your results and how does that influence the next steps in your analysis?
3. What are your null and alternative hypotheses?
4. Discuss the results of your analysis. Will you accept or reject your null hypothesis? Why? What can you specifically say about the data?

Alcohol withdrawal occurs when a person who uses alcohol excessively suddenly stops the alcohol use. Studies have shown that the onset of withdrawal is experienced a mean of 40.5 hours after the last drink, with a standard deviation of 19 hours. A sample of 38 people who use alcohol excessively is to be taken. What is the probability that the sample mean time between the last drink and the onset of withdrawal will be 39 hours or more?

Carry your intermediate computations to at least four decimal places. Round your answer to at least three decimal places.

the amount of things that are done to understand a physical educational study was based on the data below. Homework number #1

1. The claimed height (which is in inches) and weights (which is in pounds) of animals is significantly correlated, mean row(p) 0. Data has been collected which is below. Test this claim with statkey at a 5% significance level (alpha) and find the 98% confidence interval (using percentiles) for the true correlation between height and weight. Explain the test results consistent with the confidence interval and why? Note there are two statkey involved.

2. What is the Ho and Ha?

3. What is your alpha?

4. What is your p-value?

5. Conclusion?

6. 98% confidence interval?

7. Is it consistent?

8. Can you conclude multiple testing to be a good or bad idea in general and why?

A medical researcher wants to begin a clinical trial that involves systolic blood pressure (SBP) and cadmium (Cd) levels. However, before starting the study, the researcher wants to confirm that higher SBP is associated with higher Cd levels. Below are the SBP and Cd measurements for a sample a participants. What can the researcher conclude with an α of 0.01?

a) What is the appropriate statistic?
---Select--- na Correlation Slope Chi-Square
Compute the statistic selected above:

b) Compute the appropriate test statistic(s) to make a decision about H₀.
(Hint: Make sure to write down the null and alternative hypotheses to help solve the problem.)
critical value =  ; test statistic =
Decision:  ---Select--- Reject H0 Fail to reject H0

c) Compute the corresponding effect size(s) and indicate magnitude(s).
If not appropriate, input and/or select "na" below.
effect size =  ;   ---Select--- na trivial effect small effect medium effect large effect

d) Make an interpretation based on the results.

There was a significant positive relationship between systolic blood pressure and cadmium levels.There was a significant negative relationship between systolic blood pressure and cadmium levels.    There was no significant relationship between systolic blood pressure and cadmium levels.

Two groups of students are selected to test different learning techniques. The test scores of group 1 were: 95, 73, 68, 95, 98, 79, 98, 86, 76, 89, 89, 94. The test scores of group 2 were: 100, 80, 95, 90, 95, 98, 100, 100. Can it be said with 95% confidence that one group outperformed the other?

Q.1: The iris dataset (included with R) contains four measurements for 150 flowers representing three species of iris (Iris setosa, versicolor and virginica). 1. Inspect the Iris data in R. 2. Use the summary code in R to perform descriptive analysis. Paste Summary statistics in your report. 3. Draw a scatter plot, for petal length vs petal width. 4. Find all possible correlation between quantitative variables. 5. Use Function lm for developing a regression model and paste the summary of the regression model in your report----Petal.Width ~ Petal.Lengt and for Sepal.Length ~ Sepal.Width

Suppose µX and µY are the true mean stopping distances when starting at 50 mph for cars of a
certain type equipped with two different braking systems (System X vs. System Y). The
following data was obtained for each braking system:

System X    System Y
nx = 8           ny = 8
x = 85.7 ft    y = 96.3 ft
sx = 4.36 ft sy = 5.18 ft
Consider the following hypotheses:
Ho: µX - µY = -5
Ha: µX - µY < -5
As indicated by the alternative hypothesis, it is believed that cars equipped with System X
are able to stop over a shorter distance than cars equipped with System Y. Does the data
support this hypothesis at the 1% level?

Identify the advantages and disadvantages of monetary-unit sampling.

The advantages include that monetary-unit-sampling will result in a smaller sample size than classical variable sampling. It also results in a stratified sample item when samples are selected using MUS. The results of the calculation of sample size and the evaluation of the sample aren't based on the standard deviation. The disadvantages include the overstatement of the allowance of sampling risk if MUS is used to detect misstatements. Special design consideration is required using a selection of zero or negative balances. The sample error is assumed to be no more than 100%.

Can you expound further on this? Thanks!

Construct the confidence interval for the population mean

muμ.

cequals=0.980.98 ,

x overbar equals 9.1x=9.1 ,

sigmaσequals=0.40.4 ,

and

nequals=4141

A 9898 % confidence interval for muμ is (___,___) (Round to two decimal places as needed.)

Kaneko has two groups. She randomly assigns subjects to her two groups. She has one group read a news article about the importance of a public speaking classes to one’s ability to get a job, while the other group reads a news article about a recent college football game that took place at her university. Then she asks both groups to rate how important they believe a public speaking class is to one’s ability to get a job(0-50). She thinks the groups will be different but does not make a specific prediction about the direction of the difference. She wants to use α = .01. Public SpeakingGroupFootball GroupMean4530s64n4035a.What test should Kaneko use?b.Write out Kaneko’s null and alternative hypotheses in formula form.c.What is the calculated value?d.What is the critical value using α = .01?e.Make a statistical and substantive conclusion for the above problem.f. Calculate eta-square and omega-square for this problem. Interpret what these numbers mean.

The ideal (daytime) noise-level for hospitals is 45 decibels with a standard deviation of 12 db; which is to say, this may not be true. A simple random sample of 75 hospitals at a moment during the day gives a mean noise level of 47 db. Assume that the standard deviation of noise level for all hospitals is really 12 db. All answers to two places after the decimal.
(a) A 99% confidence interval for the actual mean noise level in hospitals is   db,   db).

(b) We can be 90% confident that the actual mean noise level in hospitals is   db with a margin of error of db.

(c) Unless our sample (of 81 hospitals) is among the most unusual 2% of samples, the actual mean noise level in hospitals is between db and   db.

(d) A 99.9% confidence interval for the actual mean noise level in hospitals is   db,    db).

(e) Assuming our sample of hospitals is among the most typical half of such samples, the actual mean noise level in hospitals is between   db and   db.

(f) We are 95% confident that the actual mean noise level in hospitals is db, with a margin of error of db.

(g) How many hospitals must we examine to have 95% confidence that we have the margin of error to within 0.5 db?  

(h) How many hospitals must we examine to have 99.9% confidence that we have the margin of error to within 0.5 db?

I am having trouble differentiating these. Can someone provide steps for these? I only need 1-3, 4 is always rejecting or failing to reject null.

- Hypothesis testing (4 steps) for comparing variances when sample sizes are equal.

- Hypothesis testing (4 steps) for comparing variances when sample sizes are not equal.

- Hypothesis testing (4 steps) with the independent-measures t statistic (variances assumed unequal). Be able to compute effect size using r^2.

Thank you :)

According to the National Automobile Dealers Assoc., 75% of U.S. car dealers' profits comes from repairs and parts sold. However, many of the dealerships' service departments aren't open evenings or weekends. The percentage of dealerships opened during the evenings and weekends are as follows:

Suppose a random sample of size 59 is selected from a population with σ = 10. Find the value of the standard error of the mean in each of the following cases (use the finite population correction factor if appropriate).

a. The population size is infinite (to 2 decimals).

b. The population size is N = 50,000 (to 2 decimals).

c. The population size is N = 5000 (to 2 decimals).

d. The population size is N = 500 (to 2 decimals).