On partial correlation vs. partial regression for obtaining the multiple regression equations.

1931 ◽  
Vol 22 (1) ◽  
pp. 35-44 ◽  
Author(s):  
H. D. Griffin
Keyword(s):  
Multiple Regression ◽  
Partial Correlation ◽  
Regression Equations ◽  
Partial 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.

The Relative Importance of Socioeconomic and Political Variables for Public Policy

1977 ◽  
Vol 71 (2) ◽  
pp. 559-566 ◽  
Author(s):  
Michael S. Lewis-Beck
Keyword(s):  
Public Policy ◽  
Path Analysis ◽  
Multiple Regression ◽  
Partial Correlation ◽  
Policy Outcomes ◽  
Statistical Techniques ◽  
Relative Importance ◽  
Simple Correlation ◽  
Independent Variables ◽  
Political Variables

Since Dawson and Robinson, a dominant issue in the quantitative study of public policy has been the relative importance of socioeconomic and political variables for determining policy outcomes. It is argued here that past efforts to resolve this issue have been unsatisfactory, largely because they relied on inadequate statistical techniques, i.e., simple correlation, partial correlation, or multiple regression. Coefficients from these techniques are irrelevant for all but the most peculiar models of public policy. In general, if the researcher wishes to assess the relative importance of independent variables, it will be necessary to resort to path analysis of a formally constructed causal model. The comparison of “effects coefficients,” derived from path analysis, is offered as the preferred means of evaluating independent variables, superior to comparisons of coefficients from simple correlation, partial correlation, or multiple regression. When the effects coefficients are actually calculated for a popular model of welfare policy, socioeconomic variables appear much more important than political variables, contrary to interpretations coming from the more traditional statistical techniques.

Estimation de la production de jeunes plantations de pin rouge et de pin gris du sud du Québec

1976 ◽  
Vol 6 (4) ◽  
pp. 478-486 ◽  
Author(s):  
H. A. Bolghari
Keyword(s):  
Multiple Regression ◽  
Jack Pine ◽  
Red Pine ◽  
Pine Plantations ◽  
Regression Equations ◽  
Pine Plantation ◽  
South Shore ◽  
Pine Stands ◽  
Natural Stands ◽  
St Lawrence River

Multiple regression equations have been developed to predict yield from young red pine and jack pine plantations. Data from 446 sample plots representing young red pine and jack pine stands located on the south shore of the St. Lawrence River between Quebec and Montreal were analysed. The red pine plantation yielded more than the jack pine. However, in plantation both species yield more than in natural stands. Taking into account the age and spacing of the sampled plantations, the equation obtained can provide information on yield of red pine and jack pine stands the maximum spacing of which is 3 × 3 m, up to the age of 45 and 35 years respectively. The equations will allow the construction of preliminary yield tables for both species.

FAT THICKNESS MEASURED WITH THREE ULTRASONIC INSTRUMENTS ON LIVE RAM LAMBS AS PREDICTORS OF CUTABILITY

1980 ◽  
Vol 60 (4) ◽  
pp. 857-867 ◽  
Author(s):  
ANDRÉ FORTIN
Keyword(s):  
Multiple Regression ◽  
Live Weight ◽  
Optimal Location ◽  
Regression Equations ◽  
Longissimus Muscle ◽  
Fat Thickness ◽  
Ram Lambs

Fat thickness at four locations over the longissimus muscle was measured ultrasonically on 33 live ram lambs ranging in live weight from 16.0 to 37.0 kg. Simple and multiple regression equations were developed to assess the effectiveness of fat thickness as measured by three different ultrasonic instruments (Krautkrämer USM #2, Scanoprobe Model 731A and Scanogram Model 722) to predict cutability. Weight of trimmed or boneless cuts (shoulder + loin + rack + leg) was predicted with more precision than percentage of cuts. Fat thickness alone or combined with weight at scanning was of no significant value (P > 0.05) in the prediction of percentage of trimmed cuts. Percentage of boneless cuts was predicted more efficiently from weight at scanning alone than from fat thickness alone or combined with weight at scanning. Weight of cuts (trimmed or boneless) was also estimated from the fat measurement (P < 0.01), the weight at scanning (P < 0.01) or a combination of both variables. For the latter, fat thickness did not contribute significantly (P > 0.05). The optimal location of the fat measurement depended on the ultrasonic instrument used. Fat thickness measured with the Krautkrämer was more efficient in its prediction of cutability than fat thickness measured with the Scanoprobe or Scanogram. However, over the range of liveweights studied, the usefulness of fat thickness measured on live ram lambs to predict cutability is questionable.

scholarly journals PERAN KECERDASAN SPIRITUALITAS DAN KECERDASAN EMOSIONAL TERHADAP KEBERMAKNAAN HIDUP PADA SUSTER OSF YANG PURNAKARYA

2017 ◽  
Vol 4 (2) ◽  
pp. 33
Author(s):  
Yustina Surani ◽  
Indriyati Eko Purwaningsih
Keyword(s):  
Emotional Intelligence ◽  
Multiple Regression ◽  
Family Environment ◽  
Partial Correlation ◽  
Purpose In Life ◽  
Correlation Study ◽  
Spiritual Intelligence ◽  
Intelligence Scale ◽  
Life Scale ◽  
Sex Knowledge

ABSTRACTThe purpose of this research was to predict the contribution of spiritual and emotional intelligence towards the purpose in life of OSF retired nuns. The participants were 39 nuns. This correlation study used purpose in life scale, spiritual intelligence scale, and emotional intelligence scale to collect data. The data was analyzed with partial correlation and linier multiple regression. Spiritual and emotional intelligence was positively related to the purpose in life ( r = 0,406 ; p <0,05).  The contribution of spiritual and emotional intelligence was 16,4%. It means that other variables such as sex, knowledge, motivation, age, family environment, and other environment contributing 83,6%. The researcher concluded that: First, the purpose in life of retired nuns can be predicted by spiritual and emotional intelligence; Second, spiritual intelligence supports the purpose in life of retired nuns; Third, emotional intelligence supports the purpose in life of retired nuns. Keywords: spiritual intelligence, emotional intelligence, purpose in life

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

EQUATIONS FOR RECIPROCAL CONVERSIONS OF STARCH EQUIVALENT AND TOTAL DIGESTIBLE NUTRIENT VALUES

1962 ◽  
Vol 42 (2) ◽  
pp. 211-214
Author(s):  
J. Kielanowski ◽  
Aleksandra Ziolecka ◽  
Zofia Osińska
Keyword(s):  
Multiple Regression ◽  
Fiber Content ◽  
Crude Fiber ◽  
Regression Equations ◽  
Crude Fiber Content ◽  
Independent Variable ◽  
Total Digestible Nutrient

In order to facilitate reciprocal conversions of Starch Equivalents (SE) and Total Digestible Nutrients (TDN) values of feeds, multiple regression equations were computed for three different groups of feeds (concentrates, green roughages and silages, and dry roughages. These equations are: for concentrates, green roughages and silages, and dry roughages, respectively.[Formula: see text]Where Y = percentage TDN, X1 = percentage SE, and X2 = percentage crude fiber and[Formula: see text]Where Y = percentage SE; X1 = percentage TDN, and X2 = percentage crude fiber.The addition of the crude fiber content as the second independent variable in each equation resulted in a slight but marked gain in accuracy, especially for green and dry roughages, when compared with simple regressions of TDN on SE or vice versa.

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