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2 edition of Multiple-regression equations for estimating low flows at ungaged stream sites in Ohio found in the catalog.

Multiple-regression equations for estimating low flows at ungaged stream sites in Ohio

G. F. Koltun

Multiple-regression equations for estimating low flows at ungaged stream sites in Ohio

by G. F. Koltun

  • 235 Want to read
  • 25 Currently reading

Published by Dept. of the Interior, U.S. Geological Survey, Books and Open-File Reports [distributor] in Columbus, Ohio, Denver, Colo .
Written in English

    Subjects:
  • Streamflow -- Ohio.,
  • Stream measurements -- Ohio.

  • Edition Notes

    Statementby G.F. Koltun and Ronald R. Schwartz ; prepared in cooperation with the Ohio Environmental Protection Agency.
    SeriesWater-resources investigations report -- 84-4354.
    ContributionsSchwartz, Ronald R., Ohio EPA., Geological Survey (U.S.)
    The Physical Object
    Paginationiv, 39 p. :
    Number of Pages39
    ID Numbers
    Open LibraryOL17833727M

    REGRESSION EQUATIONS. In regression analysis we can predict or estimate the value of one variable with the help of the value of other variable of the distribution after fitting to an equation. Hence there are two regression equations. The regression equation of Y on X is used to predict the value of Y with the value of X, whereas the regression. Note The cost estimation model is different for the high low and the regression from ACCOUNTING ACCT UB.3 at New York University.

    Regression step-by-step using Microsoft Excel® Notes prepared by Pamela Peterson Drake, James Madison University Step 1: Type the data into the spreadsheet The example used throughout this “How to” is a regression model of home prices, explained by: square footage, number of bedrooms, number of bathrooms, number of garages,File Size: KB. Example Car (slide 1 of 2) Objective: To use logarithms of variables in a multiple regression to estimate a multiplicative relationship for automobile sales as a function of price, income, and interest rate. Solution: The data set contains annual data on domestic auto sales in the United States.

    The difference between the multiple regression procedure and simple regression is that the multiple regression has more than one independent variable. The linear regression equation takes the following form. where n is the number of independent variables. There are several different kinds of multiple regressions—simultaneous, stepwise, and. a multiple regression analysis involving 15 independent variables and observations, SST = and SSE = The adjusted coefficient of determination is. A) B) C) D) of the following statements is true? A) Dummy variables are used to incorporate categorical variables into a regression model.


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Multiple-regression equations for estimating low flows at ungaged stream sites in Ohio by G. F. Koltun Download PDF EPUB FB2

This report presents multiple-regression equations for estimating selected low-flow characteristics for most unregulated Ohio streams at sites where little or no discharge data are available. The equations relate combinations of drainage area, main-channel length, main-channel slope, average basin elevation, forested area, average annual precipitation, and an index of infiltration to low flows.

Multiple-regression equations for estimating low flows at ungaged stream sites in Ohio. Columbus, Ohio: Dept. of the Interior, U.S. Geological Survey ; Denver, Colo.: Books and Open-File Reports [distributor], (OCoLC) Material Type: Government publication, National government publication: Document Type: Book: All Authors.

2 Regression Equations for Estimating Flood Flows for Ungaged Streams in Pennsylvania for ungaged streams in Pennsylvania. Flood-flow regression equations were developed by use of peak-flow data from continuous-record2 and 45 crest-stage partial-record3 stream-flow-gaging stations in Pennsylvania and surrounding states—.

WRIRMultiple-regression Equations for Estimating Low Flows at Ungaged Stream Sites in Ohio; Oklahoma. SIRMethods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle. using seasonal and annual low -flow statistics from 58 to 60 continuous-record stream-gaging stations in New Hampshire and nearby areas in neighboring states, and measurements of various characteristics of the drainage basins that contribute flow to those stations.

The estimating equations for the seasonal. This report provides equations for estimating the 1- 7- and day mean low flows for a recurrence interval of 10 years and the harmonic-mean flow at ungaged, unregulated stream sites in Indiana.

PROCEDURE FOR ESTIMATING LOW-FLOW STATISTICS FOR UNGAGED SITES Estimating 7-day, year and 7-day, 2-year low flows for ungaged loca­ tions in the lower Hudson River basin requires a topographic map and a sur- ficial geology map.

The user should verify that low flow is not influenced by regulation or by: This report provides a method for estimating the magnitude and frequency of floods for small streams in the Philadelphia, Pennsylvania area.

Data collected at 21 streamflow gaging stations were used in a multiple-regression analysis to develop equations for computation of peak-flow characteristics. The flood equations were determined by relating flood-frequency. Multiple regression analysis is more amenable to ceteris paribus analysis because it allows us to explicitly control for many other factors which simultaneously affect the dependent variable.

In present paper rainfall and runoff at various time step recorded at the Balaghat (i. P t, P t-1, P t-2, P t-3, P t-4, P t-5, P t-6, Q t-1, Q t-2, Q t) shall be considered as independent variable for estimation of the stream flow of the day (Q t) being dependent variable.

To get different regression models out of days length of the record, 60% of it (i. days) has been. Equations of the relations between flood-frequency and drainage-basin characteristics were developed by multiple-regression analyses.

Flood-frequency characteristics for ungaged sites on unregulated, rural streams can be estimated by use of these equations. The state was divided into five areas with similar physiographic characteristics.

The bankfull geometry regressions will be useful to predict design flows and to compare flow estimates developed by more traditional methods.

Bankfull geometry regressions may provide a useful and reliable method for estimating flows of different return periods at ungaged sites in the Red River by: 3. Breiman and Friedman: Estimating Optimal Transformations where p is the product-moment-correlation coefficient.

The quantity p*(X, Y) is known as the maximal correlation between X and Y, and it is used as a general measure of dependence (Gebelein ; also see RenyiSarmanov a, b, and Lancaster ).

A multiple regression study was also conducted by Senfeld () to examine the relationships among tolerance of ambiguity, belief in commonly held misconceptions about the nature of mathematics, self-concept regarding math, and math anxiety.

In Shakil (), the use of a multiple linear regression model has been examined inFile Size: KB. The regression model exists without data.

It specifies the form of (a) the deterministic component of the relationship and (b) the form, and perhaps also the. Baseflow plays an important role in maintaining streamflow.

Seventeen gauged watersheds and their characteristics were used to develop regression models for annual baseflow and baseflow index (BFI) estimation in Michigan. Baseflow was estimated from daily streamflow records using the two-parameter recursive digital filter method for baseflow separation of the Web-based Cited by: Software Sites Tucows Software Library Shareware CD-ROMs Software Capsules Compilation CD-ROM Images ZX Spectrum DOOM Level CD Featured image All images latest This Just In Flickr Commons Occupy Wall Street Flickr Cover Art USGS Maps.

Journal of Data Science 2(), Estimating Optimal Transformations for Multiple Regression Using the ACE Algorithm Duolao Wang1 and Michael Murphy2 1London School of Hygiene and Tropical Medicine and 2London School of Economics Abstract: This paper introduces the alternating conditional expectation (ACE) algorithm of Breiman and Friedman.

Variations in the level of a single activity (the cost driver) explain the variations in the related total costs, and 2. Cost behavior is approximated by. Exhibit You are given the following information about y and x.

y Dependent Variable 12 3 7 6 x Independent Variable 4 6 2 4 Refer to Exhibit The coefficient of determination equals. Multiple regression analysis can be used to assess effect modification. This is done by estimating a multiple regression equation relating the outcome of interest (Y) to independent variables representing the treatment assignment, sex and the product of the two (called the treatment by sex interaction variable).For the analysis, we let T = the treatment assignment (1=new drug .Ungaged watersheds Streamflow fractionation Precipitation fraction Coastal sub-basins Dissertations, Academic -- Civil Engineering -- Masters -- USF Title Investigation of normalized streamflow in West Central Florida and extrapolation to ungaged coastal fringe tributaries Aggregation USF Electronic Theses and Dissertations Format Book.confidence intervals, and quantitative equation modeling using MS Excel’s Multiple Regression tool.

These concepts, provided and explained in a straightforward manner, coupled with the common tools within MS Excel, may allow managers greater understanding of the risk and uncertainty involved in many daily decisions.

INTRODUCTIONFile Size: KB.