Histogram example sentences

"Histogram" Example Sentences

1. The histogram plots the frequency distribution of a variable.
2. The horizontal axis of the histogram shows the different intervals or bins, while the vertical axis shows the frequency.
3. The histogram gives a visual representation of the distribution of data.
4. We created a histogram to analyze the distribution of test scores.
5. The histogram helps us see if the data is concentrated in any particular interval.
6. The histogram shows us the shape of the data distribution - skewed, normal, uniform, etc.
7. The bins of the histogram can be equal width or dependent on the data.
8. The bins were too wide on the histogram, not giving us enough detail.
9. A frequency table is required to create the histogram.
10. We adjusted the bin width on the histogram to get a clearer picture.
11. We used the histogram to identify any potential outliers in the data.
12. The histogram revealed that the data was rightskewed.
13. The histogram showed that most of the data was concentrated in a narrow interval.
14. We computed the mean and standard deviation from the histogram data.
15. The histogram revealed multiple modes in the data distribution.
16. The histogram showed that the data was positively skewed.
17. The histogram helps us visualize empirical probability distributions.
18. We used different bin widths to create alternative histograms of the data.
19. The histogram clearly showed that the data was not normally distributed.
20. The histogram showed a fairly uniform distribution of the data.
21. Bar charts and histograms plot the same information but in different visual formats.
22. The histogram captured the shape of the distribution in a visually appealing way.
23. By looking at the histogram, we could see how the data was distributed.
24. The histogram helped us identify normally distributed data by its bell-shaped curve.
25. We used the histogram to verify that the data approximated a normal distribution.
26. The instructor used the histogram to illustrate different distributions.
27. Non-uniform histograms indicate non-random data.
28. The histogram plot was useful in describing the overall profile of the data.
29. The histogram gave us an initial sense of how different parameters varied.
30. We used the histogram to identify any potential gaps in the data.
31. The histogram clearly showed that the data was concentrated in a narrow interval.
32. The histogram was a useful visualization tool for summarizing the data.
33. The instructor used the histogram to explain statistical dispersion.
34. The histogram clearly showed there was an outlier skewing the distribution.
35. The histogram gave a good summary of the general tendencies in the data set.
36. We used the histogram to gain insights into the structure of the data.
37. The instructor altered the bin widths on the histogram to highlight different features.
38. The histogram illustrated the clustering present in the data.
39. We evaluated several bin widths when creating the histograms.
40. The histogram revealed that most data points were concentrated around the mean.
41. The histogram plot showed a distinctly non-normal distribution.
42. The histogram provided an overall picture of how data points were distributed.
43. The instructor used the histogram to discuss measures of dispersion.
44. The histogram showed that some intervals contained no data points at all.
45. The histogram plot helped us identify potential grouping structures in the data.
46. The histogram illustrated the unimodal distribution of the data.
47. The different histograms created with various bin widths revealed different insights.
48. We studied how the histogram changed as we varied the bin widths.
49. Different bin widths on the histogram highlighted different aspects of the data.
50. The histogram plot helped us visualize possible clusters in the data.
51. The histogram captured both central tendency and dispersion of the data.
52. We studied how the shape of the histogram changed for different distributions.
53. The histogram gave a quick overview of the data without examining every data point.
54. The histogram plot provided an indication of the sample's underlying probability distribution.
55. The instructor used the histogram to explain principles of data aggregation.
56. The histogram provided visual confirmation of the nature of the underlying distribution.
57. The histogram plot helped us identify potential outliers in the data set.
58. The instructor varied the bin widths on the histogram to illustrate different effects.
59. The histogram showed whether the data was skewed or symmetric.
60. The histogram gave us a good overall picture of the data's characteristics and tendencies.

Common Phases

1. The histogram shows the distribution of scores for the test.
2. We constructed a histogram of the random numbers to check for any bias.
3. The engineer studied the histogram of part dimensions to identify sources of variability.
4. The histogram allows us to visualize the shape of the data distribution.
5. The counts of values in a particular class interval are represented by the heights of histogram bars.
6. The horizontal axis of a histogram shows the range of data values, while the vertical axis shows the frequency or count.
7. Data should be grouped into suitable class intervals before constructing a histogram.
8. The uniformity of bar widths is important for an accurate histogram.
9. A skewed histogram indicates an asymmetrical data distribution.
10. A bell-shaped histogram points to a normal or Gaussian distribution.
11. The histogram can help detect outliers in the data.
12. Small samples often produce jagged histograms.
13. The histogram relies on binning or grouping data into classes.
14. We obtained a bimodal distribution from the histogram of income levels.
15. Histograms enable the comparison of distributions for different data sets.
16. The scientist examined the histogram of molecular weights to verify the results.
17. We generated histograms for the various test conditions for comparison.
18. The spread of data values is represented by the shape of the histogram.
19. The histogram of test scores shows a substantial positive skew.
20. The height of each histogram bar indicates how frequent a value occurs in that class.
21. The histogram revealed that most of the values clustered around the middle class intervals.
22. Choosing narrower class intervals produces a smoother histogram.
23. Software can automatically generate histograms from data sets.
24. The histogram plot quickly showed that the data were not normally distributed.
25. The histogram can help identify gaps or clustering in numerical data.
26. By analyzing the histogram, we were able to detect measurement errors in the experiment.
27. Histograms clarify whether data follow a symmetric or skewed distribution.
28. The histogram bars create a visual representation of grouped data.
29. The histogram summarizes key information about quantitative variables.
30. A smooth and symmetrical histogram indicates evenly spread data.
31. Histograms can reveal multiple peaks in the data distribution.
32. Frequency tables provide the raw data for generating histograms.
33. Narrow bars in the histogram tails point to outlier values.
34. Overlaying histograms allows comparison of data from different groups.
35. The spreadsheet application automatically generated the histogram for us.
36. The histogram of test submissions shows a strong negative skew.
37. The position of the histogram's peak indicates where most of the values lie.
38. Histograms enable convenient visualization of data distributions.
39. We chose histogram bars of equal width for better comparison of frequencies.
40. Histograms help statisticians decide which probability distributions best represent the data.
41. Because of the small sample size, the pattern suggested by the histogram may not be accurate.
42. The shape of the histogram revealed problems with the calibration of the instrument.
43. An appropriate class width for the histogram usually ranges between 1/12 and 1/40 of the data range.
44. The histogram helped demonstrate that the data followed a normal distribution.
45. We had to adjust the class intervals to create a histogram without any empty bins.
46. The bars on a histogram give an approximate indication of frequency.
47. The histogram revealed that the data were heavily right-skewed.
48. We will generate histograms for each variable to explore the data structure.
49. Irregular histogram bars point to outliers or measurement errors in the data.
50. I consulted the histogram to determine which central tendency measure best represented the data set.
51. The double-peaked histogram indicated that the data had a bimodal distribution.
52. The heights and widths of bins in a histogram reflect the concentration of data points.
53. Data points in the tails of the histogram represent extreme or unusual values.
54. The variable groupings for histograms are essentially arbitrary.
55. Histograms provide an immediate picture of data variability.
56. The histogram confirmed that the raw data met the normality assumption for statistical tests.
57. The histograms showed distinct differences between the data collected at the two sites.
58. Vertical gaps between histogram bars represent ranges with no data values.
59. The board studied the histogram of test scores to evaluate students' performance.
60. The histogram showed that the scores were unevenly distributed across the possible range of values.