scholarly journals Correlation and Regression using VASSARSTATS

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Suma AP ◽  
KP Suresh
Keyword(s):  
Correlation Coefficient ◽  
Partial Correlation ◽  
Rank Order ◽  
Correlation Coefficients ◽  
Regression Coefficients ◽  
Multiple Correlation ◽  
Population Correlation ◽  
Coefficient Corrélation ◽  
Sample Correlation ◽  
Simple Logistic

In a bivariate or a multivariate data, to understand the association between the variables Correlation is the best tool. It gives the degree of relationship between the variables. Regression gives the exact linear relationship between the variables. This article gives details of capabilities of Vassarstats Correlation and Regression and procedure to calculate Correlation coefficient and Regression coefficients with examples. Vassarstats Correlation and Regression can perform Linear Correlation and Regression, Intercorrelations, Multiple Correlation and Regression, Partial Correlation, 0.95 and 0.99 Confidence intervals for population correlation coefficient, Estimating the Population Value of rho, Significance of value of r, Significance of difference between two correlation coefficients, Significance of difference between sample correlation coefficient and hypothetical value of population Correlation coefficient, Rank Order Correlation, Correlation coefficient for a 2*2 contingency table, Point biserial correlation coefficient, Correlation for unordered pairs, and then Simple Logistic Regression.

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.

Measurement of dyspnea in patients treated with mechanical ventilation

1999 ◽  
Vol 8 (4) ◽  
pp. 254-261 ◽  
Author(s):  
J Powers ◽  
SJ Bennett
Keyword(s):  
Mechanical Ventilation ◽  
Correlation Coefficient ◽  
Rating Scales ◽  
Rank Order ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Criterion Validity ◽  
Spearman Rank Order Correlation ◽  
Spearman Rank ◽  
Test Retest Reliability

BACKGROUND: Dyspnea, or difficult breathing, is common in patients receiving mechanical ventilation; however, dyspnea is not routinely or systematically measured. OBJECTIVE: The primary purpose of this methodological study was to evaluate the test-retest reliability of 5 dyspnea rating scales and the criterion validity of 4 dyspnea rating scales in patients receiving mechanical ventilation. The secondary purpose was to examine the correlations between each of these 5 rating scales and physiological measures of respiratory function. METHODS: The convenience sample consisted of 28 patients on mechanical ventilation during their hospitalization in the intensive care units of a large, inner-city hospital. Patients rated their dyspnea twice at 30-minute intervals on the visual analogue scale, the vertical analogue dyspnea scale, the modified Borg scale, the numerical scale, and the faces scale. Test-retest reliability was computed by using the intraclass correlation coefficient. Criterion validity was evaluated by using the Spearman rank-order correlation coefficient. RESULTS: The 5 rating scales had acceptable test-retest reliabilities, with intraclass correlation coefficients ranging from 0.81 to 0.97. Criterion validity of the 4 scales also was acceptable, with Spearman rank-order correlation coefficients from 0.76 to 0.96. The rating scales were not correlated with most of the physiological variables. At least half of the patients reported moderate to severe dyspnea. CONCLUSION: The scales showed acceptable reliability and validity, and they will be useful in quantifying dyspnea experienced by patients receiving mechanical ventilation. Further work is needed to evaluate the extent and the severity of dyspnea in such patients in order to evaluate the effectiveness of interventions.

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.

scholarly journals Hubungan Antara Kepemimpinan Manajerial Kepala Sekolah Dan Kompensasi Dengan Kepuasan Kerja

2020 ◽  
Vol 1 (2) ◽  
pp. 131-141
Author(s):  
Irfan Mujahid
Keyword(s):  
Job Satisfaction ◽  
High School Teachers ◽  
Partial Correlation ◽  
Regression Equation ◽  
Analysis Method ◽  
Vocational High School ◽  
Multiple Correlation ◽  
Managerial Leadership ◽  
Determination Coefficient ◽  
Coefficient Corrélation

Penelitian ini bertujuan untuk mencari hubungan antara variabel bebas kepemimpinan The objective of this researdh is to find out the correlation between Principal’s Managerial Leadership and Compensation as independent variables with Job Satisfaction as dependent variable, both individually and collectivelly. Among 119 vocational high school  teachers were surveyed and 77 respondent were selected as samples for the research. Normality, simple regression, multiple regression, simple correlation, multiple correlation, and partial correlation are several data analysis method that were used in this research.  The result of this research imply that: Firstly, a positive and significant corelation was found between Principal’s Managerial Leadership with Job Satisfaction and expressed in coefficient correlation correlation  ry1 = 0.0,319 (a = 0,05) and regression equation as Y=94,350 + 0,245X1 and  determination coefficient is  r2y1 = 0.101  Secondly, a positive and significant corelation was found between Compensation and Job Satisfaction can be found and  expressed in coefficient correlation ry2 = 0.314 and regression equation as Y = 98,752 +0,205X2 and determination coefficient is r2y2 = 0.099.  Thirdly, this research also found that there is a positive corelation between Principal’s Managerial Leadership and Compensation along with Job Satisfaction which is expressed in multiple  correlation of  ry12 = 0.319 and regression form of Y= 94,709 +0,219X1 + 0,023X2,  and determination coefficient is r2 = 0,319 . According to result of this research that improvement of teachers job satisfaction can be in creased by Principal’s Managerial Leadership interest and Compensation interest.

scholarly journals An experimental determination of the distribution of the partial correlation coefficient in samples of thirty

1920 ◽  
Vol 97 (684) ◽  
pp. 218-224 ◽  
Keyword(s):  
Correlation Coefficient ◽  
Partial Correlation ◽  
Correlation Coefficients ◽  
Small Samples ◽  
Complete Evaluation ◽  
Frequency Distributions ◽  
Zero Correlation ◽  
Suitable Form ◽  
The Mean

In 1908. “Student” dealt experimentally with the distribution of the total correlation coefficient of small samples. In particular, he dealt with values of n as low as 4 for the case of zero correlation in the sampled population. In 1913 H. E. Soper theoretically determined the mean correla­tion and the standard deviation of the distribution of correlations to second approximations. In 1915 R. A. Fisher gave an equation for the frequency distribution of r , and in 1917, as a result of a co-operative study by H. E. Soper, A. W. Young, B. M. Cave, A. Lee and K. Pearson, this was reduced to suitable form for numerical manipulation, and the frequency distributions and frequency constants for samples of size ranging from n = 3 to n = 400 were given for values of the correlation in the sampled population ranging from ρ = 0 to ρ = 0·9. The present experimental investigation was commenced in 1914, but had to be put aside during the war. It was intended to determine whether the distribution of partial correlation coefficients for samples as small as 30 showed greater dispersion than is observed for total correlation coefficients. Yule has shown that for normal distributions and large samples the standard deviations of the distributions should be of the same magnitude. The experiment can now be related to the complete evaluation of the distributions of total correlations referred to above.

Correlation between an Embedded Figures Test and Tennis Rank Order at Three Levels of Skill

1978 ◽  
Vol 47 (3_suppl) ◽  
pp. 1143-1146 ◽  
Author(s):  
E. Lorraine Lindquist
Keyword(s):  
Correlation Coefficient ◽  
Significant Relationship ◽  
Partial Correlation ◽  
Rank Order ◽  
Short Form ◽  
Male And Female ◽  
Tennis Players ◽  
Embedded Figures Test ◽  
Field Independent ◽  
Embedded Figures

To see if there were a significant relationship between the short form of Dees, O'Reilly, and Griffith embedded figures test and playing ability for tennis as measured by tournament rank order, setting the minimum correlation coefficient at .70, from 116 beginning, 37 advanced, and 72 intercollegiate male and female tennis students from 6 universities data were collected by 9 instructors. Spearman and Kendall rank-order correlations and partial correlation indicated that, even though several values were significant, most were not high enough to conclude that tennis players are field independent.

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.

Sign in / Sign up

Export Citation Format

Share Document