1 / 20

Lecture (2)

Lecture (2). Presentation of Hydrological Data. Presentation of Hydrological Data. Tabular form:. Graphical form:. Histogram for Grouped Data.

gil
Télécharger la présentation

Lecture (2)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Lecture (2) Presentation of Hydrological Data

  2. Presentation of Hydrological Data Tabular form: Graphical form:

  3. Histogram for Grouped Data we divide a grouped data with many values into several class intervals and give the corresponding "frequency" of the class.. The number of data members that fall in a class interval is called the class frequency and the relative and percentage frequencies are computed as following formulas

  4. How to Make the Histogram - Divide the data into number of classes (13) with certain class width. Class width=0.25 m

  5. Frequency Diagram-Histogram (cont.) - The frequency is computed by counting the number of observations falling in each class, such number is called the absolute class frequency. = # of classes =Class width=0.25 m

  6. Draw a Histogram To draw a histogram, we mark the class intervals on the horizontal axis. On each interval, we erect a vertical rectangle whose area represents the absolute or relative frequency • The total area of a histogram is 1

  7. Empirical rules for the number of classes USGS: Choose class intervals that provides 20-30 well-distributed points on the curve. It is recommended (Spiegel, 1972): the number of classes should be between 5 and 20 depending of the sample size. A rough guide (Brooks and Carruthers, 1953): the number of classes should not exceed five times the logarithm of the number of observations. In principle, class limits or boundaries can be chosen arbitrarily.

  8. Relative Frequency Diagram - The relative frequency is computed by dividing the absolute frequency by the number of observations, n. the absolute frequency of the jth class. the relative frequency of of jth class.

  9. Frequency Polygon - If the adjacent points are connected by straight lines, a frequency polygon is obtained.

  10. Cumulative (Mass) Frequency Diagram The cumulative frequency of variates not exceeding a given value is the sum of all frequencies less than or equal to the given value.

  11. Relative Cumulative Frequency Diagram The cumulative frequency of variates not exceeding a given value is the sum of all frequencies less than or equal to the given value.

  12. Histogram Shapes

  13. Excel Application

  14. Why use Excel? • Software more accessible • Previous familiarity with software • Easy to format output • Better charting facilities than some statistical applications • Access to other key Excel facilities • Easy to use results with other applications

  15. Problems with Excel • Errors due to rounding, missing data or extreme values • Not suitable for very large data sets • Some algorithms are numerically unstable - little or no information about algorithms employed • Analysis ToolPak results are not dynamic and may vary with results generated by functions

  16. Frequency Histogram • Use COUNTIF to count how many times an item appears in a list Cell =COUNTIF(range, criteria) • Use FREQUENCY to calculate how often values occur within a range Cell =FREQUENCY(data_array, bins_array) • Can also use Histogram tool in Analysis Toolpak

  17. Statistical Functions • Frequency Histogram • Mean, Median and Mode • Percentiles and Quartiles • Deviation and Squared Deviation about the Mean • Variance and Standard Deviation • Covariance and the Correlation Coefficient

  18. Excel Example Data of rain gauge errors (mm)

  19. Excel Example (cont.)

  20. Excel Example (Cont.)

More Related