Regression equations of probability plot correlation coefficient test statistics from several probability distributions

2008 ◽  
Vol 355 (1-4) ◽  
pp. 1-15 ◽  
Author(s):  
Jun-Haeng Heo ◽  
Youn Woo Kho ◽  
Hongjoon Shin ◽  
Sooyoung Kim ◽  
Taesoon Kim
Keyword(s):  
Correlation Coefficient ◽  
Probability Distributions ◽  
Regression Equations ◽  
Test Statistics ◽  
Probability Plot

Regression equations of probability plot correlation coefficient test statistics using machine learning

2020 ◽  
Author(s):  
Hyunjun Ahn ◽  
Sunghun Kim ◽  
Joohyung Lee ◽  
Jun-Haeng Heo
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Distribution Model ◽  
Regression Equations ◽  
Probability Plot ◽  
Sample Data

<p>In the extremes hydrology field, it is essential to find the probability distribution model that is most appropriate for the sample data to estimate the reasonable probability quantile. Depending on the assumed probability distribution model, the probability quantile could be estimated with quite different values. The probability plot correlation coefficient (PPCC) test is one of the goodness-of-fit tests for finding suitable probability distributions for a given sample. The PPCC test determines whether assumed probability distributions are acceptable for the sample data using correlation coefficients between sample data and theoretical quantiles of assumed probability distributions. The critical values for identification are presented as a two-dimensional table, depending on the sample size and the shape parameters of models, for a three-parameter probability distribution. In this study, the applicability and utility of machine learning in the hydrology field were examined. For the usability of the PPCC test, a regression equation was derived using a machine learning algorithm with two variables: sample size and shape parameter.</p>

scholarly journals Pengaruh Pelatihan Kerja Terhadap Kinerja Karyawan Pada PT Kusumatama Mitra Selaras Jakarta

2018 ◽  
Vol 2 (2) ◽  
pp. 239-246
Author(s):  
Rani Kurniasari ◽  
Nurvi Oktiani ◽  
Gema Ramadhanti
Keyword(s):  
Human Resources ◽  
Correlation Coefficient ◽  
Research Method ◽  
Quantitative Methods ◽  
Regression Coefficient ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Performance Variables ◽  
Training Variable

ABSTRACT: One of the important factors in improving the quality of Human Resources (HR) is by training and developing employees to achieve company goals. This research method uses quantitative methods, with the technique of determining saturated sampling. The research was conducted through questionnaires to all employees of PT. Kasumatama Mitra Selaras, which is an outsourcing company with a total of 37 respondents. This questionnaire calculation with the technique of calculating the correlation coefficient, the coefficient of determination and multiple linear regression equations. With the SPSS 21 application, the correlation coefficient results are 0.650 and the results are categorized as strong. the determination coefficient can be seen that 0.422 or 42.2% of PT Kusumatama Mitra Selaras performance variables can be influenced by the training variable and the remaining 57.8% is influenced by other factors. Based on the calculation table of the regression equation can be obtained the equation is Y = 13.155 + 0.662 X. From the equation of the function it can be said that if training is constant or is 0 then Y (Performance) is equal to 13.155 with a regression coefficient of 0.662.

scholarly journals Comparative Assessment of Effects of Types of Coarse Aggregate on Strength of Different Grades of Concrete in Imo State, Nigeria

2021 ◽  
pp. 139-145
Author(s):  
Uche Felix Ikechukwu ◽  
Franklyn Chukwujike Kenkwo
Keyword(s):  
Correlation Coefficient ◽  
Strong Association ◽  
Test Statistics ◽  
Construction Sites ◽  
Concrete Production ◽  
Concrete Mix ◽  
Overall Performance ◽  
The Difference ◽  
Curing Age

Production characteristics and compositions of constituent ingredients of concrete influence to a large extent the quality of concrete works in general. The levels of implication of these factors on performance of concrete are therefore appraised for improved production of concrete work in the study area. Experimental research method was adopted to obtain data on the compressive strength of concrete produced at some construction sites in the study area. A preliminary survey conducted confirmed that 1:2:4 and 1:3:6 mix ratios are commonly used as their mix designs. Forty eight concrete cubes of 150mm x 150mm x 150mm were used to collect sample at the selected sites for the laboratory tests. Product moment correlation coefficient was used to determine the strength of relationship for changes in the increasing strength of concrete with increase in curing ages between concrete produced with sedimentary and granite aggregates. On the other hand, differences in proportion of variation on strength of 1:2:4 and 1:3:6 concrete mix ratios between concrete produced with the various types of aggregates at 7-day and 28-day curing ages respectively were analyzed using Z–test statistics.  Findings reveal that it is only concrete produced with granite material aggregate at the 28-day curing age reached the minimum stipulated standard strength values of 21N/mm2 and 18N/mm2 for 1:2:4 and 1:3:6 concrete mix ratios respectively. The correlation coefficient (r) of 0.999 and 0.993 for 1:2:4 and 1:3:6 concrete mix ratios respectively were calculated to confirm very strong association in changes in strength as the curing age increases between concrete produced with the two different aggregates. The difference in proportion of the variations in the two different mix ratios between the two different aggregates at the curing ages however are not significant in the study. Thus, the study concludes that mix ratio and curing age which remain positively strong on their effects on quality of concrete are as well significant as aggregate type in the overall performance of concrete. Granite material aggregate therefore was recommended to be used for concrete production of higher quality; and as well be always cured till the 28th day of production for desired strength of the concrete.

scholarly journals Teachers’ Perception of the usefulness of ICT in Colleges of Education in Osun State, Nigeria

1970 ◽  
Vol 11 (2) ◽  
pp. 149-159
Author(s):  
Faremi Margaret Funke ◽  
Abanikannda Mutahir Oluwafemi
Keyword(s):  
Correlation Coefficient ◽  
Random Sample ◽  
Significant Relationship ◽  
Positive Relationship ◽  
Colleges Of Education ◽  
Test Statistics ◽  
Pearson Product Moment Correlation ◽  
Ict Tools ◽  
Teachers Attitudes ◽  
Frequency Counts

This study sought information on teachers’ perception of the usefulness of ICT on their effectiveness. The population comprised of the teachers in Colleges of Education teachers in Osun State. A random sample of one hundred (100) teachers was selected from these teachers. The data collected were analysed using frequency counts, percentages, Pearson Product Moment Correlation Coefficient and t-test statistics. The findings were that there was no significant relationship between ICT resources and teachers’ effectiveness; there was a positive relationship between teachers’ attitudes towards the usage of ICT and their usage of ICT; and that there is a difference in usage of ICT in teaching by gender. It is argued that for teachers to appreciate usage of ICTs in teaching, they need to see that ICT tools are imperative for teaching.

scholarly journals Estimation of Stature from Upper Arm Length

2021 ◽  
Vol 11 (5) ◽  
pp. 23-29
Author(s):  
Tripti Shakya ◽  
Deepshikha Mishra ◽  
Prabesh Pandey
Keyword(s):  
Correlation Coefficient ◽  
Strong Relationship ◽  
Cross Sectional Study ◽  
Regression Equation ◽  
Regression Equations ◽  
Strong Positive Correlation ◽  
Upper Arm ◽  
Cross Sectional ◽  
Male And Female ◽  
Upper Arm Length

Introduction: Reconstruction of stature from long bones of the upper extremity is of great medico-legal relevance. Upper arm length (UAL) estimate stature with reasonable accuracy and is reliable factor for predicting stature. There is a strong relationship between stature and UAL and many sets of linear equation have been developed which are easiest and reliable methods for predicting relation between stature and body segments. These derived regression equations are population specific and cannot be applied in other populations. Studies on UAL for estimation of stature is lacking among Nepalese population. Hence, present study was conducted to investigate relationship between stature and UAL and to formulate regression formula for estimation of stature from UAL among Nepalese population. Method: A cross-sectional study was conducted among 150 attendees of patient visiting male and female clinic in Patan hospital, Nepal. Among 150 participants 75 were male and 75 were female. In present study stature, right and left upper arm length was measured. Pearson’s correlation coefficient (r) was calculated. Regression equation was formulated for reconstruction of stature from upper arm length of both sides as well as for both male and female separately. Result: All the measured parameters i.e. stature, right upper arm length and left upper arm length were higher in male than female (p<0.001). Strong positive correlation was found between stature and UAL in both males and females thereby indicating that stature can be estimated from UAL. Conclusion: UAL is a reliable body parameter for predicting stature. It can be of great help to anatomists, clinicians and anthropologists. Key words: Stature, upper arm length, correlation coefficient and regression equation.

Standard distributions in R.

2021 ◽  
pp. 335-347
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Programming Language ◽  
Negative Binomial ◽  
Probability Distributions ◽  
Test Statistics ◽  
P Values ◽  
Chi Squared ◽  
Student’S T ◽  
Log Normal

Abstract There are a number of in-built probability distributions, including uniform, binomial, negative binomial, normal, log-normal, logistic, exponential, Chisquared, Poisson, gamma, Fisher's F, Student's t, Weibull and others. These are used to generate p-values from test statistics, to generate random values from a distribution or to generate expected distributions. This chapter deals with standard distributions in R (a programming language that has a huge range of inbuilt statistical and graphical functions), focusing on the normal, Student's t, lognormal, logistic, Poisson, gamma, and the Chi-squared.

Sign in / Sign up

Export Citation Format

Share Document