Note on Cognitive Factors Related to Factor B of the 16 Pf Test

1971 ◽  
Vol 29 (3_suppl) ◽  
pp. 1075-1077 ◽  
Author(s):  
Joseph J. Fleishman ◽  
Bernard J. Fine
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Equation ◽  
Cognitive Factors ◽  
Cognitive Tests ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Factor B ◽  
Enlisted Men ◽  
Selection Of

A selection of 21 tests from the French, Ekstrom, and Price battery of cognitive tests and the Cattell 16 PF Test were administered to 54 Army enlisted men. Product-moment correlations and multiple linear regression equations were computed between 16 PF Factor B scores (considered a measure of intelligence) and the 21 cognitive tests. The multiple linear regression equation indicated that 70% of the variance of Factor B scores could be accounted for by the selected cognitive tests.

scholarly journals PENGARUH TINGKAT PENDIDIKAN, MOTIVASI, DAN KOMPENSASI TERHADAP KINERJA PEGAWAI DI BRI CABANG SEMARANG AHMAD YANI

2019 ◽  
Vol 11 (02) ◽  
pp. 31-47
Author(s):  
Sopi Sopi ◽  
Zumrotun Nafi'ah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Industrial Revolution ◽  
Employee Performance ◽  
Science And Technology ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Improve Performance ◽  
Independent Variables ◽  
Life Education

Education, motivation and compensation are important things that can improve performance. This study aims to explain whether there is an influence of education, motivation and compensation on employee performance. So that through the results of this study it is expected to be a reference for leaders in managing the organization. In this study there are three independent variables namely education, motivation and compensation and one dependent variable is employee performance. At present it is in the era of industrial revolution 4.0, which is marked by; big data / giant data, internet of think, labor knowledge, and long life education. Since the beginning of the life of mankind to an infinite period, it is largely determined by the mastery of science and technology. Science and technology can not be separated from the progress of education level. Education is the base of all changes both individually, as well as countries. Employee performance is determined by the education that is owned, as high as education, the higher the performance and vice versa. The population in this study are BRI CAB employees, SEMARANG A-YANI, 60 people and all of them are sampled. The results of the analysis using SPSS 23 program statistical tools obtained multiple linear regression equation Y = 0.505 X1 + 0.175 X2 + 0.408 X3 The results of multiple linear regression equations show that there is a positive and significant influence between education on employee performance at BRI CAB. A YANI SEMARANG (t count test 6.314> t table 0.05), motivation towards employee performance at BRI CAB. A YANI SEMARANG (tcount 2,160> t table 0,05), and compensation for employee performance at BRI CAB. A YANI SEMARANG (t test 5.108> ttable 0.05). While together (simultaneously) the influence of education, motivation and compensation has an effect on and significant on the performance of employees at BRI CAB. A YANI SEMARANG (count = 44,692> ftabel = 0.05). The influence of the two research variables is very strong with a correlation value of 69.0% for employee performance at BRI CAB. A YANI SEMARANG is influenced by the motivation and compensation education of the remaining 31.0% of the employees' performance at BRI CAB. A YANI SEMARANG is influenced by other variables that affect employee performance.

scholarly journals Pre Clearance Analysis on Dwelling Time for Imported Container (Case Study: Boom Baru Port)

2020 ◽  
Vol 5 (7) ◽  
pp. 595-600
Author(s):  
Aulia Safira ◽  
Erika Buchari ◽  
Edi Kadarsah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Equation Model ◽  
Study Data ◽  
Regression Equation ◽  
Linear Regression Equation ◽  
Clearance Time ◽  
Preparation Time ◽  
Dwelling Time

Dwelling time is one of many problems that happens in Boom Baru Port. Dwelling time itself consists of pre-clearance, customs clearance, and postclearance stages. One of the causes of high dwelling time is in the pre-clearance stage due to the length of time for obtaining a product permit, especially for imported commodities. The reasons for the length of time for obtaining this permit include the process of obtaining permits for prohibited and restricted goods, the process of quarantine, preparation of documents. In this study data were acquired through distributing questionnaires to forwarding companies. The analysis is done by entering the variables that has an effect toward the preclearance time at the Boom Baru Port so an equation model that can predict the length of pre-clearance time will be found. After data were analyzed using multiple linear regression, equation Y= 1,384 + 0,380 X1 + 1,078 X2 + 0,290 X3 is achieved, which X1 is the Prohibited and Restricted Goods Permit Process, X2 is the Quarantine Process, dan X3 is Documents Preparation Time.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN KONSUMEN DALAM MEMBELI MOBIL MITSUBISHI PADA PT. LAUTAN BERLIAN UTAMA MOTOR BENGKULU

2018 ◽  
Vol 1 (1) ◽  
pp. 53
Author(s):  
Hermawati Sulaiman
Keyword(s):  
Quantitative Analysis ◽  
Linear Regression ◽  
Qualitative Analysis ◽  
Multiple Linear Regression ◽  
Likert Scale ◽  
Positive Influence ◽  
Regression Equation ◽  
Linear Regression Equation ◽  
Design Quality ◽  
Consumer Decisions

The purpose of this study was to determine whether design, quality and service had an effect on consumer decisions in buying Mitsubishi cars at PT. Lautan Berlian Utama Motor. In this study the methods used are quantitative analysis and qualitative analysis. The technique of sampling this study was used by accidental sampling. To facilitate the measurement of scores from the questionnaire so that the calculation is correct, then each item is given a score based on the Likert scale. From the multiple linear regression equation it was found that design (X1), quality (X2), and service (X3) had a positive influence on consumer decisions so that buying a Mitsubishi car at PT. Ocean of Berlian Utama Motor Bengkulu.

scholarly journals FAKTOR YANG MEMPENGARUHI KESELAMATAN BERKENDARA MOBIL DIRUAS JALAN TOL SEMARANG-BAWEN PROPINSI JAWA TENGAH

2020 ◽  
Author(s):  
Andar Sri Sumantri ◽  
Irfan Misbahudin
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Road Safety ◽  
Driving Behavior ◽  
Regression Equation ◽  
Driving Safety ◽  
Linear Regression Equation ◽  
The Road ◽  
Multiple Linear Regression Equation ◽  
Road Condition

<p>Driving safety is influenced by various such as road conditions vehicle condition, driving behavior, knowledge of driving. All four have an important role in influencing the safety of driving. The purpose of this study was to determine whether there is influence of road conditions, vehicle conditions,driving behavior, knowledge of driving on roads Toll-Bawen Semarang Central Java Province either partially or simultaneously.<br />Results of statistical analysis tools SPSS (Statistical Product And Service Solutions) Ver. 22.0 multiple linear regression equation Y= 2,568 +0,268 X1 + 0,266 X2 + 0,216 X3 +0,148 X4 ( The results of multiple linear regression equation showed that there were significant positive influence on Keselamatan drive the road condition (t = 3,229&gt; t tabel = 1,9853) the condition of vehicle on road safety (t =2,624&gt; t tabel = 1,9853 the driving behavior of the safety driving (t = 3,037 &gt; t tabel = 1,9853, drive to the road safety knowledge ( t = 2,139 &gt; table = 1,9853) individually and simultaneously influence, vehicle conditions perilaku driving, and driving knowledge together –have the same effect as 26,529 &gt; F Tabel 2,46. The fourth influence is very strong research variable with the coefficient of determination (R2) were obtained for 50,8 of the safety of driving and 49,2 % are influenced by variable that are not detectedin this study</p><p><strong>Keywords : Road condition, Vehicle condition, Driving behavior, Knowledge of driving, and Driving safety.</strong></p><p> </p><p>Keselamatan berkendara dipengaruhi oleh berbagai factor diantaranya adalah kondisi jalan, kondisi kendaraan, perilaku pengendara, pengetahuan berkendara. Tujuan dari penelitian ini adalah untuk mengetahui ada tidaknya pengaruh antara kondisi jalan, kondisi kendaraan, perilaku pengendara, pengetahuan berkendara diruas jalan Tol Semarang Bawen Propinsi Jawa Tengah. Dalam penelitian ini yang menjadi populasi adalah pengguna mobil golongan I dijalan tol semarang bawen provinsi jawa tengah berjumlah 100 orang. Variabel dalam penelitian ini terdiri dari : Variabel bebas yaitu kondisi jalan x1, kondisi kendaraan perilaku berkendara x3 dan pengetahuan berkendar serta variable terikat yaitu keselamatan berkendara. Semua hipotesis yang diajukan dalam penelitian ini diterima, sehingga model tersebut mengambarkan hubungan yang kausalitas yang terjalin antar variable. Hasil analisi dengan alat bantu statistic program spss ver.22 . Dari hasil persamaan regresi linier berganda menunjukkan bahwa ada pengaruh yang positif dan signifikan antar masing-masing variable. Pengaruh keempat variable penelitian sangat kuat dengan nilai koefisien determinasi yang diperoleh sebesar 50,8% terhadap keselamatan berkendara dan 49,2 % dipengaruhi oleh variable yang tidak terdeteksi pada penelitian ini.</p><p><strong>Kata kunci : Kondisi Jalan, Kondisi Kendaraan, Perilaku Berkendara, Pengetahuan berkendara dan Keselamatan berkendara.</strong></p>

scholarly journals production of milk yield from udder circumference and distance between teats in West African Dwarf and red Sokoto goats

2021 ◽  
Vol 36 (1) ◽  
pp. 1-11
Author(s):  
M.N. Benji ◽  
O.A Osinowu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Milk Production ◽  
Milk Yield ◽  
West African ◽  
Regression Equation ◽  
Predictive Equation ◽  
Linear Regression Equation ◽  
West African Dwarf ◽  
Independent Variables

Udder circumference (UD) and distance between teats (DBT) measured before and after  milking were used to determine CUC (UC before milking minus UC after milking) and CDT (DBT before milking minus DBT after milking).  All four parameters were utilized as independent variables in two milk yeild from 202 weekly records of 17 lactating does, consisting of 8 West African Dwarf (WAD) and 9 Red sokoto (RS) goats. WAD and RS goats hadsimilar mean values for daily milk yield ( 270.34±12.47 ml vs 245.26±14.51 ml) and UC(28.49±0.33 vs 28.49± 0.13 cm). Both models had significant ( P<0.001) R2 values ranging from 0.244 to 0.757. UC was the best index of milk yeild (R2=0.688) followed by CUC (R2=0.467) in the linear regressionequation while DBT and CDT yeilded lower R2 Values (0.244 vs 0.258).  Inclusion of all four parameters in the multiple linear regression equation yeilded the highest R2 (0.757). The predictive equation was Y= 441.443 + 25.739X1 + 23.349-21.265X2 +61.080X4 in which Y is milk yeilded, Xi , X4  represent UC, CUC, DBT and CDT respectively. Positive and significant (P<0.001) phenotypic correlations were observed between UCand milk yeilded (0.759), CUC and milk yeilded (0.690). DBT and milk yield (0.498), CDT and milk yield(0.508). In the current practice of collecting collecting weekly records, early prediction of future milk production from udder circumference measured prior to milking will be accurate using linear regression predictive equation. Alternatively, if more traits related to udder size such as UC, CUC,DBT and CDT are incorporated as independent variables in multiple linear regression equation, milk production would be predicted with better accuracy.

scholarly journals A new uncertain linear regression model based on equation deformation

2021 ◽  
Author(s):  
Shuai Wang ◽  
Yufu Ning ◽  
Hongmei Shi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Equation ◽  
Elementary Transformation ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Unknown Parameters ◽  
Deformation Method ◽  
Cramer’S Rule

Abstract When the observed data are imprecise, the uncertain regression model is more suitable for the linear regression analysis. Least squares estimate can fully consider the given data and minimize the sum of squares of residual error, and can effectively solve the linear regression equation of imprecisely observed data. On the basis of uncertainty theory, this paper presents an equation deformation method for solving unknown parameters in uncertain linear regression equations. We first establish the equation deformation method of one-dimensional linear regression model, and then extend it to the case of multiple linear regression model. We also combine the equation deformation method with Cramer's rule and matrix, and propose the Cramer's rule and matrix elementary transformation method to solve the unknown parameters of the uncertain linear regression equation. Numerical examples show that the equation deformation method can effectively solve the unknown parameters of the uncertain linear regression equation.

An Aspen Bark Factor Equation for Michigan

1991 ◽  
Vol 8 (1) ◽  
pp. 12-15
Author(s):  
Gary W. Fowler
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tree Height ◽  
Prediction Equation ◽  
Regression Equation ◽  
Linear Regression Equation ◽  
Trembling Aspen ◽  
Breast Height ◽  
Relative Errors ◽  
Factor Equation

Abstract A multiple linear regression equation was developed to predict bark factor for aspen in Michigan as a function of tree height. Bark factors for bigtooth aspen were, in general, somewhat larger than bark factors for trembling aspen. Even though equations were developed for both species, the differences between the two equations were small, and not statistically significant, and a pooled equation based on both species is recommended. The pooled prediction equation yielded average relative errors from - 2.3 to 0.87% and - 1.02 to 3.83% at all tree heights for bigtooth and trembling aspen, respectively. For more accurate predictions of bark factor, the separate prediction equations for bigtooth and trembling aspen should be used. The new equations can be used to more accurately estimate tree and log wood volumes than when using a constant bark factor determined at breast height, which, in general, leads to underestimates of wood volumes. North. J. Appl. For 8(1):12-15.

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