Assignment 4 – Selecting appropriate test
Objectives of this assignment
1. To verify that the data meet the criteria for ANOVA, run the test, and interpret SPSS’s output for ANOVA and Kruskal-Wallis test.
Important Reminders
1. It’s a good idea to SAVE A COPY of your completed assignment for yourself. Sometimes assignments do not get submitted properly so you should have a backup copy.
2. You will NOT be able to open up an SPSS dataset on a computer that does not have SPSS installed on it (e.g., your home computer, your laptop). However, you can save the dataset to your USB drive to open it up at a later time.
3. SPSS datasets are .sav files (like the way Word documents are .docx files). In this week’s section, you will need to create a dataset given the information on each question.
Instructions to Complete the Assignment
1. A chemical engineer had three different formulas for a gasoline additive that she thought would significantly change automobile gas mileage. She had three groups of 15 test standard eight-cylinder engines that simulated normal driving conditions. Each group received a different gasoline formulation (A1, A2, and A3) and was run for several hours. Simulated mileage for the A1 group was 35.60, 34,50, 36.20, 33.10, 36.10, 34.80, 33.90, 34.70, 35.20, 35.80, 36.60, 35.10, 34.90, 36.00, and 34.10. Mileage for A2 was 36.80, 35.30, 37.00, 32.90, 36.80, 35.60, 35.10, 35.80, 36.90, 36.60, 36.80, 36.60, 35.80, 36.30, and 36.00. Mileage for the A3 group was 37.79, 36.29, 38.01, 33.80, 37.79, 36.58, 36.03, 36.79, 37.89, 37.57, 37.79, 37.59, 36.78, 37.29, and 37.01.
Your job is to investigate the mileage numbers and investigate if there is a significant difference on the gas mileage between the three groups.
• Write the null and alternative hypotheses.
• What is the test statistic value?
• Copy and paste the output and provide your conclusion on the result.
• If you find a difference, you must identify the groups contributing to the significant test statistic. (Copy and paste the output).
• Run the nonparametric test, copy and paste the output and provide your interpretation of the result.
2. Bacteria counts were taken at the four Southern California beaches of Santa Monica, Malibu, Zuma, and Ventura. The researcher’s idea was that the different beaches would yield significantly different bacteria counts. The Santa Monica beach count was 16.2, 12.0, 16.4, 15.5, 16.5, 22.0, 23.0. The Malibu count was 18.3, 18.2, 18.3, 17.4, 18.4, 24.1, and 25.2. The Zuma count was 17.2, 17.3, 17.2, 16.4, 17.3, 23.0, and 24.3. The Ventura count was 20.2, 20.9, 21.1, 20.3, 20.2, 26.1, and 28.4.
Run the appropriate inferential statistics, copy and paste the output, and interpret the output. What’s your conclusion?
3. Data was collected from 10 major oil well drilling operators that recorded the number of hours lost per week due to work-related accidents. A rigorous safety program was instituted, and the number of lost hours once again recorded following introduction of the safety program. The pre-safety program values are 41, 55, 63, 79, 45, 120, 30, 15, 24, and 24. The post-safety program values are 32, 49, 50, 73, 43, 115, 32, 9, 22, and 19.
A research consultant was hired to examine the data and determine if the safety program significantly changed the weekly hours lost from on-job injuries.
• Write the null and alternative hypothesis, select the appropriate test to seek evidence that the program was successful.
• Copy and paste the output and provide your interpretation of the result.
• Run the nonparametric test, copy and paste the output, and interpret the output.
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