This is an application of bivariate correlation and involves a fictitious replication of Bell, Robertson, and Wagner's (1995) study, which examined the relationship between pilot experience and aviation safety. Bell et al. defined pilot experience as pilots' total flight hours in all aircraft, and they defined aviation safety as scores on the Aviation Safety Index (ASI), which consisted of five safety-related statements that correspond to behaviors associated with preflight preparation. The five statements are given below.

1. I do a thorough walk-around inspection of an aircraft before I fly it.

2. I check the weather thoroughly before I fly (even on local flight).

3. I compute fuel requirements with an eye toward a 30- or 45-minute fuel reserve.

4. I use a checklist for interior and exterior inspection of an aircraft.

5. I compute takeoff and landing distances as well as runway lengths at airports of intended use for each flight.

Respondents were asked to self-assess the degree to which they performed each behavior using a 7-item bipolar adjective scale with "Never" at one end of the scale, which was scored 1, and "Always" at the other end of the scale, which was scored 7. Thus, aggregate scores could range from 5 to 35, with higher scores indicating more safety conscious pilots. A fictitious set of data related to this study is given in the Excel file "Data R." Import this data set into your statistical software program and do the following.

A. Data Analysis

Using the data from the Excel file, conduct a hypothesis test as follows:

1. Formulate the null and alternative hypotheses in symbols and words.

2. Determine the test criteria.

3. Test for the assumptions of correlation. (Note: For outlier analysis, there should be six outliers using Jackknife distances.)

4. Run the analysis and record the results.

5. Make a decision to reject or fail to reject the null hypothesis and write a concluding statement.

B. Post-Data Analysis

1. Determine and interpret the effect size, r2.

2. Determine and interpret the power of the study.

3. Determine and interpret the 95% confidence interval.

4. Present at least three plausible explanations for the results.