"Scatterplots" Example Sentences
1. The instructor created scatterplots to visualize the data.
2. They analyzed the correlation using scatterplots and trend lines.
3. The data lends itself well to visualization through scatterplots.
4. The scatterplots showed a strong positive correlation between the two variables.
5. The scatterplots revealed an interesting nonlinear relationship between height and weight.
6. They drew lines of best fit on the scatterplots to determine the trend.
7. Scatterplots allowed them to see outliers in the data set.
8. By creating scatterplots in Excel, they saw a clear relationship between the variables.
9. Let's make some scatterplots to visualize the data before calculating any correlations.
10. They rotated the scatterplots to get a different perspective on the data.
11. Multivariate scatterplots can show relationships between three variables.
12. The scatterplots implied a possible exponential relationship between the factors.
13. They annotated the scatterplots to highlight key features.
14. She imported the data into R to generate scatterplots.
15. They examined scatterplots for multiple age groups to identify differences.
16. The scatterplots showed a weak correlation between income and amount spent on luxury items.
17. The instructor showed how to label axes and title scatterplots properly.
18. Colored scatterplots helped distinguish between data points for male and female participants.
19. They observed scatterplots for multiple cities to compare the relationships.
20. Scatterplots with best fit lines revealed the strength and direction of correlations.
21. She overlaid scatterplots on the same axes to compare the relationships.
22. The researcher chose to visualize the results through scatterplots.
23. Scatterplots allowed them to spot data entry errors and outliers.
24. They added trendlines and equations to the scatterplots to quantify the relationships.
25. The scatterplots did not reveal a linear relationship between the factors as they had expected.
26. They compared scatterplots of raw data to scatterplots of transformed data.
27. They printed the scatterplots to analyze the patterns offline.
28. The instructor taught how to plot scatterplots in Python.
29. The lecturer discussed the benefits and limitations of scatterplots.
30. They coded interactive scatterplots that allowed dynamic filtering of data.
31. The textbook showed examples of properly constructed scatterplots.
32. Scatterplots for different years revealed changes in the correlation over time.
33. The scatterplots provided a visual representation of the data that tables could not capture.
34. Fitting curves to scatterplots allowed them to identify exponential or logarithmic trends.
35. The software automatically generated scatterplots when the data was imported.
36. They added different symbols to the scatterplots to distinguish sample types.
37. They drew boxplots next to the scatterplots for further comparison.
38. The scientist created bubble scatterplots to show a third dimension in the data.
39. They found histogram bars horizontally above relevant scatterplots for added context.
40. Weak correlations still produced scatterplots with some visible pattern.
41. Animated scatterplots helped illustrate dynamic patterns in the data over time.
42. The textbook described how to avoid common mistakes when creating scatterplots.
43. Generating scatterplots can reveal relationships not apparent from numerical data alone.
44. Box-and-whisker plots alongside scatterplots provided a robust visual analysis.
45. The sample scatterplots in the textbook helped clarify certain key concepts.
46. They used a loess smoothing line on the scatterplot to identify nonlinear trends.
47. The biologist generated individual scatterplots for each experimental group.
48. Scatterplots helped provide a rough idea of whether a relationship existed.
49. The textbook showed how to interpret different shapes of scatterplots correctly.
50. Scatterplots offered a quick visual check before conducting statistical tests.
51. Histograms on the axes of scatterplots added relevant summary statistics.
52. The scatterplots suggested the need for data transformation before correlation.
53. The professor taught how to generate scatterplots using Python's matplotlib library.
54. Larger data points were used in the scatterplots to highlight important observations.
55. Text labels added to scatterplots helped explain certain outlier data points.
56. Nested scatterplots showed relationships within subgroups of the data.
57. The textbook example warned against placing too much weight on a single scatterplot.
58. They printed scatterplots on transparencies to compare different data sets.
59. The researcher discussed how scatterplots can sometimes be misleading.
60. Parallel coordinate plots alongside scatterplots provided a comprehensive visualization.
Common Phases
1. The
scatterplots showed a strong positive correlation.
2. They analyzed the correlation using
scatterplots and trend lines.
3.
Scatterplots revealed an interesting nonlinear relationship.
4. They drew lines of best fit on the
scatterplots.
5.
Scatterplots allowed them to see outliers in the data set.
6.
Scatterplots provided a visual representation of the data.
7. The
scatterplots suggested the need for data transformation.
8. Boxplots alongside
scatterplots provided a robust visual analysis.