Recently, the factory began a new production line that is more efficient than the existing production line. However, the factory still needs ball bearings to meet the same specifications. To compare the accuracy of the new process against the existing process, the factory decides to take two random samples of ball bearings. The first sample is of 50 randomly selected ball bearings from the existing production line, and the second sample is of 50 randomly selected ball bearings produced from the new production line. For each sample, the diameters of the ball bearings were measured.The two samples will be generated using Python’s numpy module. These data sets will be unique to you, and therefore your answers will be unique as well. Run Step 1 in the Python script to generate your unique sample data.Suppose that the factory claims that the proportion of ball bearings with diameter values less than 2.20 cm in the existing manufacturing process is the same as the proportion in the new process. At alpha=0.05, is there enough evidence that the two proportions are the same? Perform a hypothesis test for the difference between two population proportions to test this claim.In your initial post, address the following items:Define the null and alternative hypotheses in mathematical terms as well as in words.Identify the level of significance.Include the test statistic and the P-value. See Step 2 in the Python script. (Note that Python methods return two tailed P-values. You must report the correct P-value based on the alternative hypothesis.)Provide a conclusion and interpretation of the test: Should the null hypothesis be rejected? Why or why not? My dataDiameters data frame of the first sample (showing only the first five observations) diameters 0 2.52 1 3.30 2 2.97 3 2.09 4 1.83 Diameters data frame of the second sample (showing only the first five observations) diameters 0 2.18 1 3.61 2 3.11 3 2.55 4 1.65test-statistic = 0.86 two tailed p-value = 0.3912

Using the example from Ottomanelli and colleagues (2012), participants were randomized to receive Supported Employment or treatment as usual. A third group, also a treatment as usual group, consisted of a non-randomized observational group of participants. A simulated subset was selected for this example so that the computations would be small and manageable. The independent variable in this example is treatment group (Supported Employment, Treatment as Usual- Randomized, and Treatment as Usual-Observational/Not Randomized), and the dependent variable was the number of hours worked post-treatment. Supported employment refers to a type of specialized interdisciplinary vocational rehabilitation designed to help people with disabilities obtain and maintain community-based competitive employment in their chosen occupation (Bond, 2004).These data are presented in HW7.sav. The null hypothesis is: “There is no difference between the treatment groups in post-treatment number of hours worked among veterans with spinal cord injuries.”Download the file: see attached fileQuestions to be answered in word doc - Open in SPSS. Use the dataset to answer the following questions.Do the data meet criteria for homogeneity of variance? Provide a rationale for your answer.What is the F value, and the numerator and denominator degrees of freedom?Compute the ANOVA using SPSS and paste the SPSS table containing the F into your homework.Is the F significant at a=0.05? Specify how you arrived at your answer.What is the exact likelihood of obtaining our F value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true?Write your interpretation of the results, as you would in an APA-formatted journal.Is there a difference in your final interpretation when comparing the results of the LSD post-hoc test versus Tukey HSD test? Provide a rationale for your answer.If the researcher decided to combine the two Treatment as Usual groups to represent a overall “Control” group, then there would be two groups to compare: Supported Employment versus Control. What would be the appropriate statistic to address the difference in hours worked between the two groups? Provide a rationale for your answer.