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Standard error for correlation coefficients

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  • The present study is on Physical and Morphometric characteristics of 248 (235 milking cows, 09 dry cows, 04 bulls) Khillar cattle breed at Bull Mother Farm, Junoni, Solapur. The Khillar cattle were compact and tight white skinned breed with significant variation among cows, bullocks and bulls and muzzle color was mostly black, mottled and carroty.

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  • Infiltration is a dynamic process, variable in time and space and plays a vital role in the replenishment of soil water which is responsible for the growth and development of crops. Modelling of infiltration equation involves in finding out coefficients in the expressions for the curves infiltration rate and accumulated infiltration verses time and other parameters. The present study is aimed at determining the best fit infiltration model.

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  • The present investigation was conducted on records of 2959 buffaloes, progeny of 219 sires over a period of 24 years(1992-2015) maintained at Buffalo research centre(BRC), LUVAS, Hisar and Animal farm ICAR-CIRB, Hisar, Four sire evaluation procedures such as ordinary least squares (OLS), regressed least squares (RLS), Derivative free restricted estimated maximum likelihood (DFREML) and best linear unbiased prediction (BLUP) based on estimated breeding value of performance traits such as first lactation milk yield (FLY), first lactation peak yield (FPY), first lactation milk yield per day of...

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  • Precipitation records often suffer from missing data values for certain time periods due to various reasons, one of them being the malfunctioning of rain gauges. This is an important issue in practical hydrology as it affects the continuity of rainfall data. The missing data values ultimately influence the results of hydrologic studies that use rainfall data as one of the input variables. Therefore, it is crucial to estimate the missing rainfall data for qualitative hydrologic assessment.

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  • Chapter 13 - Correlation and linear regression. When you have completed this chapter, you will be able to: Understand and interpret the terms dependent and independent variable; calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate; conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero.

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  • Chapter 14 - Multiple regression analysis. This chapter include objectives: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination.

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  • The great advantage of using panel data over a simple cross-sectional sample is that one can control for the country-specific fixed effects ai. Failure to do so leads to biased estimates if these fixed or latent effects are correlated with the explanatory variables, as is likely to be the case. However, unfortunately the use of panel data also leads to more compli- cations if some or all of the variables in the estimating equation follow a trend over time. Such trending typically implies what econometricians call non-stationarity.

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  • These and all subsequent regressions include our full array of demographic and time controls, but we only report the distance coefficients for the sake of brevity.12 All regressions report robust standard errors, which are adjusted for possible correlation between the error terms of observations drawn from the same household. The first column of Table 2 summarizes the relationship between distance and diary week spending. Diary week spending declines significantly over the pay period at a rate of 0.8 percent per day. Over the entire pay period this implies a substantial decline. E.g.

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