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Hypothesis Testing of Mean & Proportion

See attached two files.

In first file there are 2 questions. MOST IMPORTANT is these problems need be done using the second utility file.

The following data represent the actual amount of soda in a sample of fifty 2-liter bottles. You are in charge of production and must assure that the bottles don't have too much or too little in them. At the 0.05 level of significance, is there evidence to suggest that the mean amount of soda filled is different from 2.0 liters?

Q-1a: State the null hypothesis.

Q-1b: State the alternate hypothesis.

Q-1c: Perform the hypothesis test and state your conclusions and evidence.

Suppose you didn't care if the bottles had too much, you only cared if the quantity in the bottles was less than 2.0 liters.

Q-1d: State the new null hypothesis.

Q-1e: State the new alternative hypothesis.

Q-1f: Perform the hypothesis test and state your conclusions and evidence.

Late payment of medical claims can add to the cost of health care. The auditing firm of Dewey, Cheatham, and Howe has discovered that for one insurance company, 85.1% of the claims were paid in full when first submitted based on a sample of 200 claims. Suppose that the insurance company developed a new payment system in an effort to increase this percentage. A sample of 200 claims processed under this new system revealed that 180 of the claims were paid in full when first submitted. At the 5% level of significance, is there evidence that the population proportion of claims paid in full under this new system is higher than the proportion of claims paid in full under the old system?

Q-2a: State the null hypothesis

Q-2b: State the alternative hypothesis.

Q-2c: Perform the hypothesis test and state your conclusions and evidence.

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Solution Summary

The solution provides step by step method for the calculation of testing of hypothesis. Formula for the calculation and Interpretations of the results are also included. Interactive excel sheet is included. The user can edit the inputs and obtain the complete results for a new set of data.

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