A data set with whole numbers has a low value of 20 and a high value of 89. Find the class width for a frequency table with seven classes.

If possible, Please show work in notebook.

He decides go through with making his experiment a repeated-measures design. Each of the 20 subjects take part in both conditions, and he finds a mean of the difference scores is 12 (M_D = 12) with a standard deviation of the difference scores of 8          (S_D = 8). Please find the t-critical and the test statistic (assuming alpha = .05_{2 tail)}. Then describe what the found.

                        t_crit =

                        t-statistic =

                        Conclusion:

Finally, he wants to know the magnitude of the effect of the drug. Please calculate the Cohen’s d and interpret what it means.

                        Cohen’s d =

                        Interpretation:

please assist with data cleaning problems

just want article on data cleaning problems

An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation.

Compass maritime services llc valuing ships solution

How much is the Bet Performer worth based on comparable transactions? Which ship is the best reference transaction?

1. How is the variance affected when you add a predictor to a multiple regression model?

2. Why does multiple-regression modeling require subject-matter expertise?

3. Can overfitting occur in a model with a high coefficient of determination value? What would that mean for that model?

4. What is the process of assessing a model’s capacity to make accurate predictions?

Write a two to three (2-3) page report

Assignment 1: Bottling Company Case Study< Due Week 10 and worth 140 points Imagine you are a manager at a major bottling company. Customers have begun to complain that the bottles of the brand of soda produced in your company contain less than the advertised sixteen (16) ounces of product. Your boss wants to solve the problem at hand and has asked you to investigate. You have your employees pull thirty (30) bottles off the line at random from all the shifts at the bottling plant. You ask your employees to measure the amount of soda there is in each bottle. Note: Use the data set provided by your instructor to complete this assignment.

Write a two to three (2-3) page report in which you: 1. Calculate the mean, median, and standard deviation for ounces in the bottles. 2. Construct a 95% Confidence Interval for the ounces in the bottles. 3. Conduct a hypothesis test to verify if the claim that a bottle contains less than sixteen (16) ounces is supported. Clearly state the logic of your test, the calculations, and the conclusion of your test. 4. Provide the following discussion based on the conclusion of your test: a. If you conclude that there are less than sixteen (16) ounces in a bottle of soda, speculate on three (3) possible causes. Next, suggest the strategies to avoid the deficit in the future. Or b. If you conclude that the claim of less soda per bottle is not supported or justified, provide a detailed explanation to your boss about the situation. Include your speculation on the reason(s) behind the claim and recommend one (1) strategy geared toward mitigating this issue in the future. Your assignment must follow these formatting requirements: • Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides. No citations and references are required, but if you use them, they must follow APA format. Check with your professor for any additional instructions. • Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length. The specific course learning outcomes associated with this assignment are: • Calculate measurements of central tendency and dispersal. • Determine confidence intervals for data. • Describe the vocabulary and principles of hypothesis testing. • Discuss application of course content to professional contexts. • Use technological tools to solve problems in statistics. • Write clearly and concisely about statistics using proper writing mechanics

As you just saw, we used a Tukey post hoc test to look at the differences between the sloppy, dressy, and casual conditions. So why not just use three t-Tests (one comparing sloppy to dressy, one comparing sloppy to casual, and one comparing dressy to casual)? For this Pause-Problem, tell me why you wouldn’t want to run multiple t-Tests.

A television station wishes to study the relationship between viewership of its 11 p.m. news program and viewer age (18 years or less, 19 to 35, 36 to 54, 55 or older). A sample of 250 television viewers in each age group is randomly selected, and the number who watch the station’s 11 p.m. news is found for each sample. The results are given in the table below. Age Group Watch 11 p.m. News? 18 or less 19 to 35 36 to 54 55 or Older Total Yes 42 57 61 82 242 No 208 193 189 168 758 Total 250 250 250 250 1,000 (a) Let p1, p2, p3, and p4 be the proportions of all viewers in each age group who watch the station’s 11 p.m. news. If these proportions are equal, then whether a viewer watches the station’s 11 p.m. news is independent of the viewer’s age group. Therefore, we can test the null hypothesis H0 that p1, p2, p3, and p4 are equal by carrying out a chi-square test for independence. Perform this test by setting α = .05. (Round your answer to 3 decimal places.) χ2χ2 = so (Click to select)Do not rejectReject H0: independence (b) Compute a 95 percent confidence interval for the difference between p1 and p4. (Round your answers to 3 decimal places. Negative amounts should be indicated by a minus sign.) 95% CI: [ , ]

In 1950​, an organization surveyed 1100 adults and​ asked, "Are you a total abstainer​ from, or do you on occasion​ consume, alcoholic​ beverages?" Of the 1100 adults​ surveyed, 363 indicated that they were total abstainers. In a recent​ survey, the same question was asked of 1100 adults and 319 indicated that they were total abstainers. Has the proportion of adults who totally abstain from alcohol​ changed? Use the α = 0.01 level of significance. Normality criteria have been satisfied.

Write the Null and Alternative Hypothesis:

Give the Test statistic and P value:

State the conclusion in context:

When you perform a test of hypothesis, you must always use the 4-step approach: i. S1:the “Null” and “Alternate” hypotheses, ii. S2: manually calculate value of the test statistic, iii. S3: specify the level of significance and the critical value of the statistic, iv. S5: use appropriate decision rule and then reach a conclusion about not rejecting or rejecting the null hypothesis. S5: If asked to calculate p–value,do so and relate the p-value to the level of significance in reaching your conclusion. If you use MiniTab to perform the hypothesis test, you must paste the relevant output into your assignment. This output simply verifies and occasionally replaces the manual computation of the test statistic, p-value or the confidence interval. You must supply all the required steps, mentioned above, to make your testing procedure standard and complete. If Confidence Coefficient (CC) and Level of Significance (LS) are not specified, assume the default values of 95% and 5% respectively. Use precision level of only 4 Decimal Digits (DD) and no more or no less, when calculations are done with a calculator.

Sample_BMI

34.74
31.95
30.34
16.79
38.57
33.55
25.29
26.70
26.49
30.98
22.12
22.28
29.73
33.18
25.63
20.78
24.14
29.35
26.15
23.56
30.45
28.49
22.35
28.58
22.11

Test the hypothesis that the population median of the BMI is other than 29.50. Also, find the 95% Confidence Interval for this population median. Is it consistent?

Please use Minitab to show your work and tell me the steps in Minitab

We have learned hypothesis tests

1. for the mean (when population variance is known and when it is unknown)
2. for a percentage
3. to see if the means of two sets of data are the same
4. goodness of fit test
5. test for independence

For each type give a brief example. You do not have to solve the problem you give. Try to come up with a problem on your own

ou wish to determine if there is a negative linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.05 and round all values to 4 decimal places.

Ho: ρ = 0
Ha: ρ < 0

Find the Linear Correlation Coefficient
r =

Find the p-value
p-value =

The p-value is

• Less than (or equal to) αα
• Greater than αα

The p-value leads to a decision to

• Reject Ho
• Accept Ho
• Do Not Reject Ho

The conclusion is

• There is a significant linear correlation between driver age and number of driver deaths.
• There is a significant negative linear correlation between driver age and number of driver deaths.
• There is a significant positive linear correlation between driver age and number of driver deaths.
• There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths.

Two plots at Rothamsted Experimental Station were studied for production of wheat straw. For a random sample of years, the annual wheat straw production (in pounds) from one plot was as follows.

Use a calculator to verify that, for this plot, the sample variance is s² ≈ 0.301.

Another random sample of years for a second plot gave the following annual wheat production (in pounds).

Use a calculator to verify that the sample variance for this plot is s² ≈ 0.498.

Test the claim that there is a difference (either way) in the population variance of wheat straw production for these two plots. Use a 5% level of signifcance.

(a) What is the level of significance?

State the null and alternate hypotheses.

H_o: σ₁² = σ₂²; H₁: σ₁² > σ₂²H_o: σ₁² > σ₂²; H₁: σ₁² = σ₂²    H_o: σ₂² = σ₁²; H₁: σ₂² > σ₁²H_o: σ₁² = σ₂²; H₁: σ₁² ≠ σ₂²

(b) Find the value of the sample F statistic. (Use 2 decimal places.)


What are the degrees of freedom?

What assumptions are you making about the original distribution?

The populations follow independent chi-square distributions. We have random samples from each population.The populations follow independent normal distributions.    The populations follow independent normal distributions. We have random samples from each population.The populations follow dependent normal distributions. We have random samples from each population.

(c) Find or estimate the P-value of the sample test statistic. (Use 4 decimal places.)

p-value > 0.2000.100 < p-value < 0.200    0.050 < p-value < 0.1000.020 < p-value < 0.0500.002 < p-value < 0.020p-value < 0.002

(d) Based on your answers in parts (a) to (c), will you reject or fail to reject the null hypothesis?

At the α = 0.05 level, we reject the null hypothesis and conclude the data are not statistically significant.At the α = 0.05 level, we reject the null hypothesis and conclude the data are statistically significant.    At the α = 0.05 level, we fail to reject the null hypothesis and conclude the data are not statistically significant.At the α = 0.05 level, we fail to reject the null hypothesis and conclude the data are statistically significant.

(e) Interpret your conclusion in the context of the application.

Fail to reject the null hypothesis, there is sufficient evidence that the variance in annual wheat production differs between the two plots.Reject the null hypothesis, there is insufficient evidence that the variance in annual wheat production differs between the two plots.    Reject the null hypothesis, there is sufficient evidence that the variance in annual wheat production differs between the two plots.Fail to reject the null hypothesis, there is insufficient evidence that the variance in annual wheat production differs between the two plots.