Describe a scenario where a researcher could use a One-Way ANOVA to answer a research question. Fully describe the scenario and the variables involved and explain the rationale for your answer. Why is that test appropriate to use? Please do not provide answer with "Goodness of fit" scenario.

Par Inc. is a small manufacturer of golf equipment. It produces two types of golf bags: Standard and Deluxe. Each bag type requires the following operations (and production times) to produce one unit:

Time available refers to the production capacity for each of the above operations. For example, 630 total hours a month are available for cutting and dyeing, which will be distributed for the production of the two types of bags.

Every standard bag makes a profit of $10, and every deluxe bag makes a profit of $9.

The problem is to determine the optimal number of standard bags and deluxe bags to produce every month to maximize the profit contribution.

Define the variables, formulate the problem, and SOLVE it in Excel (generate the Answer and Sensitivity reports)

Please use excel and post pictures so I can see how to do this on Excel

Total plasma volume is important in determining the required plasma component in blood replacement therapy for a person undergoing surgery. Plasma volume is influenced by the overall health and physical activity of an individual. Suppose that a random sample of 46 male firefighters are tested and that they have a plasma volume sample mean of x = 37.5 ml/kg (milliliters plasma per kilogram body weight). Assume that σ = 7.30 ml/kg for the distribution of blood plasma.

(a) Find a 99% confidence interval for the population mean blood plasma volume in male firefighters. What is the margin of error? (Round your answers to two decimal places.)

(b) What conditions are necessary for your calculations? (Select all that apply.)

σ is knownn is largethe distribution of weights is uniformthe distribution of weights is normalσ is unknown

(c) Interpret your results in the context of this problem.

99% of the intervals created using this method will contain the true average blood plasma volume in male firefighters.The probability that this interval contains the true average blood plasma volume in male firefighters is 0.01.    The probability that this interval contains the true average blood plasma volume in male firefighters is 0.99.1% of the intervals created using this method will contain the true average blood plasma volume in male firefighters.

(d) Find the sample size necessary for a 99% confidence level with maximal margin of error E = 2.10 for the mean plasma volume in male firefighters. (Round up to the nearest whole number.)
male firefighters

******Show work, no Exel/Spss please!!!!******

1. To answer this question, refer to the following hypothetical data collected using replicated measures design.

a) In a two-tailed test of H 0 using α=0.05, what is p(obtained) for the results shown? *Hint: "two-tailed" means the same thing as nondirectional. ANSWER = 0.0216

b) What would you conclude regarding H0 using a = 0.05 2-tail? ANSWER = Reject H0

2. Conduct a one-way ANOVA on the following data.

What is your conclusion (use an alpha)? ANSWER = Reject H0

3. What is the value of r for the following relationship between height and weight? ANSWER = 0.87

Height: 60, 64, 65, 68 Mean=62.25

Weight: 103, 122, 137, 132 Mean=123.5

4. Given the following data, what is the value of F crit for the column effect (variable b) *Hint: don't forget to evaluate a numerator (for variable B) and denominator df (within): ANSWER= 3.40

5. Given the following data, what is the value of the SS total? ANSWER = 121.8667

6. What is the value of the MS rows in the following ANOVA table? ANSWER = 225.25

1. You know the true properties of the data so you can answer the two following questions: (and you do not need to know the Y values to answer)

Expected value= 0 Variance= 10

a. What is the true variance of beta hat 1?

b. What is the true variance of beta hat 2?

Suppose we suspect a coin is not fair — we suspect that it has larger chance of getting tails than heads, so we want to conduct a hypothesis testing to investigate this question.

a: Let p be the chance of getting heads, write down the alternative hypothesis Ha and the null hypothesis H0 in terms of p.

b: In order to investigate this question, we flip the coin 100 times and record the observation. Suppose we use T = the number of heads as our test statistic, consider two potential rejection regions, 1. T ≥ 80 2. T ≤ 20 Which rejection region will help us reject H0 in favor of Ha?

c: Consider different cases where we observe T = 10, T = 90 and T = 50, what will be our corresponding conclusions given the choice of RR we made in part b?

1. in the statistical analysis section, the authors write: “Post hoc power calculations of our sample size with 107 per group was 95% (5% significance) to detect an increase readmission rate as found in the study;…..”.

Comment. [2 marks]

It's an open question and I have no idea what to answer, so u have any idea please feel free to answer!

The shares of the U.S. automobile market held in 1990 by General Motors, Japanese manufacturers, Ford, Chrysler, and other manufacturers were, respectively, 34%, 32%, 19%, 9%, and 6%. Suppose that a new survey of 1,000 new-car buyers shows the following purchase frequencies: GM Japanese Ford Chrysler Other 397 259 231 80 33 (a) Show that it is appropriate to carry out a chi-square test using these data. Each expected value is ≥ (b) Test to determine whether the current market shares differ from those of 1990. Use α = .05. (Round your answer to 3 decimal places.) x2 H0 . Conclude current market shares from those of 1990.

Jon Hoke owns a bicycle shop. He stocks three types of bicycles: road-racing, cross-country

and mountain. A road-racing bike costs $1,200, a cross-country bike costs $1,700 and a

mountain bike costs $900. He sells road-racing bikes for $1,800, cross-country bikes for $2,100

and mountain bikes for $1,200. He has $12,000 available this month to purchase bikes. Each

bike must be assembled: a road-racing bike requires 8 hours to assemble, a cross-country bike

requires 12 hours and a mountain bike requires 16 hours. He estimates that he and his

employees have 120 hours available to assemble bikes. He has enough space in his store to

order 20 bikes this month. Based on past sales, John wants to stock at least twice as many

mountain bikes as the other two combined because mountain bikes sell better. Formulate

(develop the objective function and constraints) a linear programming model for this problem

where the Jon’s objective is to maximize total profits. Generate the solution using Excel Solver.

Please include Solver explanation. (step by step)

Standard deviation is a useful concept in performance management. Let us say that a director in a local fire department wants to know any variation between the performance of this year and that of the last year. He draws a sample of 10 response times of this year ( in minutes): 3.0, 12.0, 7.0, 4.0, 4.0, 6.0, 3.0, 9.0, 11.0, and 15.0, comparing them with a sample of 10 response times last year ( in minutes): 8.0, 7.0, 8.0, 6.0, 6.0, 9.0, 7.0, 9.0, 8.0, and 6.0.

a. Does he see a performance variation by the mean? ( 10 points)

b. Does he see a performance variation by the standard deviation? If he does, is it performance improvement or deterioration from the last year? Why? ( 10 points)

c. Now, imagine that your are a citizen receiving fire protection services from the local fire department. How do you evaluate the response times of the fire department, by the mean, by the standard deviation, or by both? Please explain

A random variable is normally distributed with a mean of 24 and a standard deviation of 6. If an observation is randomly selected from the​ distribution,

a. What value will be exceeded 5​% of the​ time?

b. What value will be exceeded 90% of the​ time?

c. Determine two values of which the smaller has 20% of the values below it and the larger has 20​% of the values above it.

d. What value will 10​% of the observations be​ below?

find z0.48

Assume the following data represent the cost of a gallon of gasoline ($) at all the various gas stations around town on a given day. Take a random sample of size 5 from this population.

2.59 3.01 3.15 2.83 2.79 2.59 2.96 3.05 3.19 3.03 2.65 2.74 2.83 2.69 3.05 3.10 2.89 2.84 2.63 3.11 2.76 2.89 2.90 3.09 3.05 2.71 2.84 2.90 2.75 2.90 2.56 2.89 2.76 2.87 2.92 3.05 3.09 2.57 3.20 2.76

a) Describe the individual, variable, population and sample.

b) A description of the process you went through to actually collect the random sample.

c) The work showing the calculation of the mean and standard deviation by hand. (You may use a basic calculator for the arithmetic.)

d) A sentence explaining the meaning of the standard deviation in terms of the gasoline prices.

Consider the following situation: Electrical engineers have a device that tests for battery life (in minutes) by placing a battery under a controlled electrical load and measuring how long it lasts. They are interested in comparing the performance of 4 brands of batteries. They replicated the experiment 4 times by randomly assigning a battery brand to be used in the electrical load device each time they measured battery life. In other words, they made 16 ‘runs’ and randomized the order in which the battery brands were used.

The data they obtained was:

1. State the Null and Alternative Hypothesis in words (both hypotheses) and using statistical notation (null hypothesis only).

2. Compute the means and sample standard errors for the brands. Upload the file. This can be an Excel spreadsheet, a photo/screenshot/scanned image.

3. Compute the sums of squares for Treatment, Error, and Total, and complete the ANOVA table below. For each SS, MS, and F calculation round to nearest whole number. For example, calculate SS > round to nearest whole number. Use that SS to calculate MS > round to nearest whole number. Use that MS to calculate F > round to nearest whole number.

4. Using the F - table and your ANOVA table results, what is the critical F-value for a test of the hypothesis at the 5% level of significance?

5. Based on your F-test statistic and F-critical value, write a complete conclusion of your hypothesis test.

6. What is the value from the Tukey table you will used to calculate the Tukey comparisons?  

7. Construct mean comparisons using the Tukey method and upload your results. Be sure to provide the Tukey W value (i.e. the value you calculated by which you compared the treatment means) and the letter grouping for each of the treatment means. Based on these Tukey comparisons, what battery brand(s) would you conclude differ in mean lifetime? This upload can be, for example, a photo taken of your work and an upload of that image.

8. Now use Minitab to conduct this analysis. Upload an image of your your Minitab output that shows your ANOVA table and grouping information of Tukey mean comparisons.

An engineer is comparing voltages for two types of batteries (K and Q) using a sample of 74 type K batteries and a sample of 46 type Q batteries. The mean voltage is measured as 8.65 for the type K batteries with a standard deviation of 0.832, and the mean voltage is 9.02 for type Q batteries with a standard deviation of 0.732. Conduct a hypothesis test for the conjecture that the mean voltage for these two types of batteries is different. Let μ1 be the true mean voltage for type K batteries and μ2 be the true mean voltage for type Q batteries. Use a 0.05 level of significance.

Compute the value of the test statistic. Round your answer to two decimal places.

Determine the decision rule for rejecting the null hypothesis H0. Round the numerical portion of your answer to two decimal places. (reject null if z is greater than or less than the decision rule)

Make the decision for the hypothesis test.