Mr. David O. Nwafor
Statistical Concepts Series
Radiology
Seema S. Sonnad, PhD
Describing Data: Statistical and Graphical Methods1
An important step in any analysis is to describe the data by using descriptive and graphic methods. The author provides an approach to the most commonly used numeric and graphic methods for describing data. Methods are presented for summarizing data numerically, including presentation of data in tables and calculation of statistics for central tendency, variability, and distribution. Methods are also presented for displaying data graphically, including line graphs, bar graphs, histograms, and frequency polygons. The description and graphing of study data result in better analysis and presentation of data.
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Index terms: Data analysis Statistical analysis Published online before print 10.1148/radiol.2253012154 Radiology 2002; 225:622– 628
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From the Department of Surgery, University of Michigan Medical Center, Ann Arbor. Received January 14, 2002; revision requested March 2; revision received May 20; accepted June 14. Address correspondence to the author, Department of Surgery, University of Pennsylvania Health System, 4 Silverstein, 3400 Spruce St, Philadelphia, PA 19104-4283 (e-mail: seema.sonnad@uphs .upenn.edu). RSNA, 2002
RSNA, 2002
©
A primary goal of statistics is to collapse data into easily understandable summaries. These summaries may then be used to compare sets of numbers from different sources or to evaluate relationships among sets of numbers. Later articles in this series will discuss methods for comparing data and evaluating relationships. The focus of this article is on methods for summarizing and describing data both numerically and graphically. Options for constructing measures that describe the data are presented first, followed by methods for graphically examining your data. While these techniques are not methodologically difficult, descriptive statistics are central to the process of organizing and summarizing
Radiology
Seema S. Sonnad, PhD
Describing Data: Statistical and Graphical Methods1
An important step in any analysis is to describe the data by using descriptive and graphic methods. The author provides an approach to the most commonly used numeric and graphic methods for describing data. Methods are presented for summarizing data numerically, including presentation of data in tables and calculation of statistics for central tendency, variability, and distribution. Methods are also presented for displaying data graphically, including line graphs, bar graphs, histograms, and frequency polygons. The description and graphing of study data result in better analysis and presentation of data.
©
Index terms: Data analysis Statistical analysis Published online before print 10.1148/radiol.2253012154 Radiology 2002; 225:622– 628
1
From the Department of Surgery, University of Michigan Medical Center, Ann Arbor. Received January 14, 2002; revision requested March 2; revision received May 20; accepted June 14. Address correspondence to the author, Department of Surgery, University of Pennsylvania Health System, 4 Silverstein, 3400 Spruce St, Philadelphia, PA 19104-4283 (e-mail: seema.sonnad@uphs .upenn.edu). RSNA, 2002
RSNA, 2002
©
A primary goal of statistics is to collapse data into easily understandable summaries. These summaries may then be used to compare sets of numbers from different sources or to evaluate relationships among sets of numbers. Later articles in this series will discuss methods for comparing data and evaluating relationships. The focus of this article is on methods for summarizing and describing data both numerically and graphically. Options for constructing measures that describe the data are presented first, followed by methods for graphically examining your data. While these techniques are not methodologically difficult, descriptive statistics are central to the process of organizing and summarizing
References: 1. Heverhagen JT, Muller D, Battmann A, et al. MR hydrometry to assess exocrine function of the pancreas: initial results of noninvasive quantification of secretion. Radiology 2001; 218:61– 67. 2. Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R. Automated segmentation of MR images of brain tumors. Radiology 2001; 218:586 –591. 3. Deliganis AV, Fisher DJ, Lam AM, Mara- 6. 7. villa KR. Cerebrospinal fluid signal intensity increase on FLAIR MR images in patients under general anesthesia: the role of supplemental O(2). Radiology 2001; 218: 152–156. Jackman RJ, Burbank F, Parker SH, et al. Stereotatic breast biopsy of nonpalpable lesions: determinants of ductal carcinoma in situ underestimation rates. Radiology 2001; 218:497–502. Fain SB, King BF, Breen JF, Kruger DG, Riederer SJ. High-spatial-resolution contrastenhanced MR angiography of the renal arteries: a prospective comparison with digital subtraction angiography. Radiology 2001; 218:481– 490. Bielak LF, Sheedy PF II, Peyser PA. Automated segmentation of MR images of brain tumors. Radiology 2001; 218:224 –229. Tombach B, Bremer C, Reimer P, et al. Renal tolerance of a neutral gadolinium chelate (gadobutrol) in patients with chronic renal failure: results of a randomized study. Radiology 2001; 218:651– 657. 628 Radiology December 2002 Sonnad