scholarly journals On the partial correlation ratio

1915 ◽  
Vol 91 (632) ◽  
pp. 492-498 ◽  
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Partial Correlation ◽  
Regression Line ◽  
Multiple Correlation Coefficient ◽  
Correlation Ratio ◽  
Multiple Correlation ◽  
Non Linear ◽  
Single Number ◽  
Gaussian Surface

(1) In a paper communicated to the Royal Society in 1903 I gave very briefly in a footnote the properties of the correlation ratio . These properties were discussed more at length in my memoir, “On the General Theory of Skew Correlation and Non-linear Regression,” published in 1905. The two papers dealt only with the total correlation ratio , or the relation between two variates without consideration of any other correlated variates. The introduction of the correlation ratio enabled the measure of the relationship between two variates to be expressed by a single number, measuring its total intensity, in cases where the regression line was of any form. The ratio passed into the usual correlation coefficient when the regression line became straight. This correlation ratio has been generally accepted by statisticians as a useful measure of relationship in cases of skew correlation and non-linear regression. Shortly after the appearance of the above memoirs I generalised this coefficient in a manner comparable with the generalisation of the coefficient of correlation, namely, by the definitions of the multiple correlation ratio and of the partial correlation ratio . These ratios correspond to the multiple correlation coefficient and the partial correlation coefficient in multiple linear regression. Their importance is very considerable, as they enable us to measure the intensity of association between two variates when other correlated variates are considered as constant without any assumption that the regression is linear, still less that the frequencies follow the normal (or Laplace-Gaussian) surface. I had not intended to discuss the results of the present paper before the probable errors had been provided, but the recent revival of interest in skew regression, and its fundamental importance in all higher statistical inquiry, justifies, at least, the publication of those formulæ which are fundamental to the subject. (2) I deal first with the problem of three variates, although the extension to any number is not hard to make.

IMBALAN DAN GAYA KEPEMIMPINAN PENGARUHNYA TERHADAP KEPUASAN KERJA KARYAWAN DI BALAI BESAR INDUSTRI HASIL PERTANIAN BOGOR

2018 ◽  
Vol 1 (01) ◽  
pp. 17
Author(s):  
Ramlan Ruvendi
Keyword(s):  
Job Satisfaction ◽  
Multiple Regression ◽  
Correlation Coefficient ◽  
Analysis Of Variance ◽  
Partial Correlation ◽  
Multiple Correlation Coefficient ◽  
Multiple Correlation ◽  
Average Value ◽  
Partial Correlation Coefficient

The study was carried out to find out whether there were influence and correlation bet-ween : a) Reward received by the IRDABI’s employees on their job satisfaction. b) style of the leader-ship on the job satisfaction. c) Reward together with style of leadership on the job satisfaction of IR-DABI’s employees.The result of the study showed that there was significant correlation and influence between the reward on the job satisfaction with was shown by the value of partial correlation coefficient of 0.6185 and coefficient of multiple regression for reward variable (β1) of 0.412. The influence of variable for style of leadership on the job satisfaction was also significant with the partial correlation coefficient of 0.5495 and coefficient of multiple regression (β2) of 0.355.In the test of Analysis of Variance (ANOVA) on the equation of multiple regression show that F-value was bigger that F-table (F = 58.97 > F-table = 3.098) or the Probability Value smaller than 0.05. At showed that there was significant correlation and influence between reward variables all together with style of leadership on the job satisfaction of employees. The value of multiple correlation coefficient (R) was 0.751 and R Square (R2) was 0.564. Value of R Square (0.564) meant that 56.5% of variation pro-portion total of job satisfaction can be eliminated of equation of multiple regression was used as the es-timator rather than using average value of job satisfaction as the estimator.

XX.—On the Theory of Statistical Regression

1934 ◽  
Vol 53 ◽  
pp. 260-283 ◽  
Author(s):  
M. S. Bartlett
Keyword(s):  
Correlation Coefficient ◽  
Partial Correlation ◽  
Regression Coefficient ◽  
Multiple Correlation Coefficient ◽  
Statistical Regression ◽  
Multiple Correlation ◽  
Partial Regression ◽  
Partial Regression Coefficient ◽  
Moment Distribution ◽  
A Chain

1. The product moment distribution in the general case of p normal variates, obtained in 1928 (1), and again in 1933 (2), has been awaiting further analysis. Some indication has already been given (Wishart, 1928) that new results might be expected from it; in the particular case of two variates obtained previously by Fisher (3), it has been used to deduce the distributions of the correlation coefficient (3), co-variance (4), and regression coefficient (5). In the general case, it has been used by Wilks (6) to furnish a proof of Fisher's distribution of the multiple correlation coefficient (7), and also in connection with his idea of a generalized variance (8). Further analysis appears to be most fruitful in studying statistical regression in general. It is shown in Part I of this paper that the product moment distribution can be split up into a chain of independent factors. Most of the known distributions related to regression or partial correlation are simply obtained, in a manner which clearly indicates the relations they bear to one another; the distribution of a partial regression coefficient of any order is also readily derived.

MULTIFACTORIAL REGRESSION ANALYSIS OF COW BODY TYPE INDICATORS OF FIRST CALF OF THE BLACK-ANDWHITE HOLSTINIZED BREED OF THE MOSCOW REGION

2020 ◽  
pp. 226-231
Author(s):  
А.F. Konte ◽  
◽  
L.P. Ignatieva ◽  
Keyword(s):  
Correlation Coefficient ◽  
Regression Line ◽  
Multiple Correlation Coefficient ◽  
Regression Equation ◽  
Moscow Region ◽  
Positive Sign ◽  
Regression Equations ◽  
Multiple Correlation ◽  
Body Type ◽  
Genetic Trend

The object of research was 59765 heads (2005...2018) of cows of the first calving of black-and-white Holstein breed, this number is predominant in the complexes of the Moscow region. The database contains 4 parameters of the “A” system assessment and 17 features of the “B” system body type assessment. Based on shown calculations, we have obtained the coefficients of the regression equation b0…n (78.34…-0.02). The studied indicators “milk type”, “body” and “udder” have an average level of linear dependence (multiple correlation coefficient R - ≤0,70 and ≥0,30), from the regression statistics, we can see that the “multiple correlation coefficient R” in our case is in the range of 0.67...0.57. The regression equations show that the indicator “milk type” is strongly influenced and interacted with by “height” and “ milk type “(according to “system B”); “corpus” depends on “height” and “depth of the corpus”; “limbs” also depends on “height” and “setting of the hind legs from behind”; “udder” cooperates mostly with the indicators - “central ligament” and “ length of the anterior lobes». The dependent variables “milk type”, “body” greater than” udder “and” limb “are statistically dependent indicators of the” system b “score the regression line is positive, i.e. when any xn increases by one in the regression equation, if all other values of x1...n are 0, the value of bn increases (assuming a positive” + “ sign in front of them). The genetic trend of traits for evaluating the physique of the studied animals tended to improve the indicators of the exterior in the direction of greater severity of the dairy type of cattle and in the direction of smaller udders and limbs.

scholarly journals PENGARUH KOMPENSASI DAN DISIPLIN KERJA TERHADAP KINERJA PERANGKAT DESA UNTUK MENSUKSESKAN PENYALURAN DANA DESA DI DESA SUNGAI GUNTUNG TENGAH KECAMATAN RENGAT KABUPATEN INDRAGIRI HULU

2019 ◽  
Vol 8 (2) ◽  
pp. 302-311
Author(s):  
Yenny Iskandar ◽  
Sry Windartini ◽  
Suharmiyati Suharmiyati
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Direct Relationship ◽  
Multiple Correlation Coefficient ◽  
Regression Equation ◽  
Coefficient Of Determination ◽  
Multiple Regression Equation ◽  
Linear Regression Equation ◽  
Multiple Correlation ◽  
The Village

This research was conducted at office Desa Sei Guntung Tengah,  Kecamatan Rengat  Kabupaten Indragiri Hulu. The aim is to determine the effect of Compensation on the performance of village officials in the success of Village Fund Distribution in desa Sungai Guntung Tengah, Kec. Rengat, Kabupaten Indragiri Hulu. The results of the study obtained by linear regression equation is that SPSS knows that constant (a) is 1.361. and the coefficient of X (b) is 0.355 with a multiple regression equation is Y = 1.361 + 0.355 X. The correlation coefficient is known that (X) compensation has a relationship with (Y) the performance of village officials. This can be seen from the value of the multiple correlation coefficient R is 0.813 meaning that it has a very strong and direct relationship. and then tested with a multiple coefficient of determination (R2) is 0.661. this shows (X) compensation can contribute to the variable (Y) the performance of the village apparatus by 66.1%. And the remaining 33.9% is influenced by other variables not examined in this study. t count for compensation variable is 2.239, in table t with db 35 and significant level 0.025 is obtained 2.034. because t count (2,239)> from t table (2,034) then Ho is rejected and Ha is accepted, which means compensation has a significant influence on the performance of the village apparatus.

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