**Independent-Measures Experiment Results**The following data represent the results from an independent-measures experiment comparing three treatment conditions. Use StatCrunch (or other technology) to conduct an analysis of variance with α=0.05 to determine whether these data are sufficient to conclude that there are significant differences between the treatments.| Treatment A | Treatment B | Treatment C ||-------------|-------------|-------------|| 8 | 6 | 15 || 7 | 8 | 9 || 7 | 11 | 13 || 6 | 7 | 12 || 7 | 8 | 11 |To perform the analysis:1. **Input Data:** Enter the data for each treatment into StatCrunch or a similar statistical analysis tool.2. **Select Analysis of Variance (ANOVA):** Choose the option to perform an ANOVA test to determine if there are any statistically significant differences between the means of the three treatment groups.3. **Set Significance Level:** Ensure that the significance level (α) is set to 0.05.4. **Interpret Results:** After running the test, the output will indicate whether the differences between treatment means are statistically significant. By following these steps, researchers can determine if the observed variations between treatments are due to chance or if they reflect true differences in treatment effects.

The following information has been given: Treatment A Treatment B Treatment C 8 6 15 7 8 9…

The following data represent the results from an independent-measures experiment comparing three treatment conditions. Use StatCrunch (or other technology) to conduct an analysis of variance with a=0.05 to determine whether these data are sufficient to conclude that there are significant differences between the treatments. Treatment A Treatment B Treatment C 6 15 9 13 12 11 87 N 7 6 7 8 11 7 8