R 22 example sentences

"R 22" Example Sentences

1. The model's R² value was surprisingly high at 0.95.
2. He calculated the R² and found it to be statistically significant.
3. My R² is only 0.6; further data collection is needed.
4. What is the interpretation of this low R² value?
5. The R² of 0.8 shows a strong correlation.
6. A high R² doesn't always imply causality.
7. We need to improve the R² of the regression model.
8. The adjusted R² is a better indicator.
9. His report focused heavily on the R² statistic.
10. Ignoring the low R² would be a mistake.
11. How can we increase the R² in our prediction model?
12. The R² value is a key metric in this study.
13. They dismissed the results due to a poor R².
14. An R² of 0.01 shows almost no correlation.
15. She presented her findings, emphasizing the R² score.
16. The R² indicates a good fit to the data.
17. Despite the high R², there are limitations.
18. Understanding R² is crucial for regression analysis.
19. A perfect fit would yield an R² of 1.0.
20. The R² was unexpectedly low for this dataset.
21. They achieved a remarkable R² in their experiment.
22. This model's R² is significantly better than the previous one.
23. Is the R² a reliable measure in this context?
24. The software automatically calculates the R² value.
25. He misinterpreted the meaning of the R² statistic.
26. The low R² suggests a need for model refinement.
27. We focused on improving the R² through feature engineering.
28. The R² score is only one factor to consider.
29. Analyzing the R² helped us understand the data better.
30. They obtained a surprisingly high R² given the noise.
31. The R² results are presented in Table 3.
32. Focus on increasing the R² to achieve better accuracy.
33. A strong correlation is indicated by a high R² value.
34. We will revisit the R² analysis in the discussion.
35. The R² value is displayed in the graph.
36. They questioned the validity of the R² due to outliers.
37. Despite a low R², the model performed reasonably well.
38. Improving the model resulted in a higher R² value.
39. Let's discuss the implications of this low R² value.
40. The R² should be interpreted cautiously.
41. High R² doesn't guarantee a good model.
42. She carefully examined the R² before drawing conclusions.
43. The R² provides a measure of goodness of fit.
44. His thesis extensively covered the interpretation of R².
45. Are there any hidden factors affecting the R² value?
46. They aimed for an R² above 0.8.
47. The R² results were consistent across all experiments.
48. This analysis highlights the importance of monitoring the R².
49. The paper's main finding was a significantly increased R².
50. Understanding R² is fundamental for statistical modeling.