Effect of Strategic Intent on Performance of Small and Medium Scale Printing Press Firms in Abuja, Nigeria

2020 ◽  
Vol 1 (2) ◽  
pp. 26-37
Author(s):  
Felicia Eze ◽  
Murat Akyüz ◽  
Opusunju Michael Isaac
Keyword(s):  
Central Bank ◽  
Regression Model ◽  
Mission Statement ◽  
Control Variable ◽  
Multiple Regression Model ◽  
Printing Press ◽  
Coefficient Of Determination ◽  
Access To Finance ◽  
Medium Scale ◽  
Strategic Intent

Purpose: This study investigates the effect of strategic intent on the performance of small and medium scale printing firms in Federal Capital Territory (FCT), Abuja, Nigeria. Methods: The population of the study included all the small and medium scale printing press in Abuja which is 226 and the sample size of 68. A multiple regression model was formulated to estimate the effect of strategic intent (vision, mission, and objectives) on performance (growth) of small and medium scale printing press firms in Abuja. The study also adopted a control variable such as finance to have a better coefficient of determination. Findings: The study found that strategic intent had a positive and significant effect on the growth of small and medium scale printing press firms in Nigeria. The study also found that finance (collateral, access to finance, and insufficient finance) had a negative and insignificant effect on the growth of small and medium scale printing press firms in Nigeria.  Implication: Small and medium printing press firms in Abuja, FCT should communicate their vision, mission statement, and objectives to their employees. The microfinance banks in collaboration with the central bank of Nigeria should minimize collateral conditions in obtaining microcredit from microfinance banks.   

scholarly journals A multiple regression model for prediction of optimal dose of Moringa Oleifera in faecal sludge dewatering

2021 ◽  
Author(s):  
Benjamin Doglas ◽  
Richard Kimwaga ◽  
Aloyce Mayo
Keyword(s):  
Regression Model ◽  
Moringa Oleifera ◽  
Multiple Linear Regression Model ◽  
Optimal Dose ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Septic Tank ◽  
Contributing Factors ◽  
Jar Test ◽  
The University

Abstract Moringa Oleifera (MO) is a highly effective conditioner in the dewatering of Fecal sludge (FS). However, the model for the prediction of its optimal dose has not yet been documented. This article presents the results of the developed model for the prediction of MO optimal doses. The developed model was based on assessing the FS parameters and MO stock solution. The FS samples were obtained from a mixture of a pit latrine and septic tank and were analyzed at the water quality laboratory of the University of Dar es Salaam. The multiple linear regression model was used to establish a relationship between MO optimal dose as a function of FS characteristics (pH, Electrical Conductivity, Total Solids and Total Suspended Solids) and concentration of MO stock solution. The results indicated that the main contributing factors which determine the MO optimal dose were the concentration of MO stock solution, followed by pH of FS. The model results showed a good agreement between the predicted and observed MO optimal dose with a coefficient of determination of R2 = 0.72 and 0.9 for calibration and validation respectively. Therefore, the model can be adapted to determine the MO optimal dose without running the Jar-test experiment.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA KARYAWAN PT. BIO NUSANTARA TEKNOLOGI Jl. Lintas Utara Km. 19 Bengkulu Tengah

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Merta Kusuma ◽  
Tm Said
Keyword(s):  
Regression Model ◽  
Work Environment ◽  
Significant Influence ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Environment Variable ◽  
Regression Test ◽  
Training Variable ◽  
Permanent Employees ◽  
And Training

Merta Kusuma, Tm. Said:The purpose of this research is to know the influence of motivation, work environment, selection, and training toward employee’s performance in PT BIO Nusantara Teknologi. Population of this research was 550 permanent employees in PT BIO Nusantara Teknologi. Sampling techniques that used in this research were Nonprobability and Insidental sampling. To determined the sample used Solvin formula, so there was 84 people as samples. Data collection techniques were observation questionnaire, and interview. Data analysis techniques used by researcher were qualitative and quantitative analysis which consists of a multiple regression model and coefficient of determination. The result of this research used regression test Y= 6.003 + 0.230 X1 + 0.134 X2 + 0.188 X3 + 0.232 X4. While the result of determination R =  0.472 and R2 = 0223. T test significant level < 0,05 so, there was significant influence, if significant level > 0,05 so, there was not significant influence, the result of each t sig was 0,021 for motivation variable, 0,034 for work environment variable, 0,045 for selection, and 0,049 for training variable. The four variables were < 0,05 so there was significant influence. Next, F test, if F sig < 0,05 and (Fanova > F table) so, Ho was rejected ad H1 was accepted, the result of F sig=(0,000) < 0,05 and (5,673>2,337)so, the regression model can be used to predict employee’s performance variable or it can be said that the motivation variable (X1), work environment(X2), selection(X3), and training(X4) were influenced employee’s performance(Y) together in PT BIO Nusantara Teknologi.Key Words: Motivation, Work Environment, Selection, Training and Employee’s Performance.

scholarly journals ANALISIS PENGARUH KEBIJAKAN MONETER TERHADAP RETURN INDEKS SAHAM INFOBANK15 DI INDONESIA PERIODE NOVEMBER 2012- FEBRUARI 2016 (STUDY KASUS PADA INDEKS SAHAM PERBANKAN INFOBANK15 YANG TERDAFTAR DI BURSA EFEK INDONESIA)

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Eva Yudika Sembiring
Keyword(s):  
Monetary Policy ◽  
Regression Analysis ◽  
Exchange Rate ◽  
Data Analysis ◽  
Regression Model ◽  
Stock Exchange ◽  
Control Variable ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Jones Index

This study was aimed to see the effects of of monetary policy on Infobank15 index return listed on the Indonesia Stock Exchange. Monetary policy variables is proxied through BI rate, followed by inflation and exchange rate, as well as the Dow Jones index as a control variable. The method of data analysis was multiple regression analysis using secondary data from November 2012 to February 2016. The results showed that the BI rate, inflation, exchange rate, and the Dow Jones index significantly take effect to on Infobank15 index return. The coefficient of determination equals to 0.73 which means that 73% of Infobank15 index returns can be explained by BI Rate, Inflation, exchange rate, and the Dow Jones Index while the rest (27%) is explained by other variables outside of the regression model.

Abstract 9: Peak Width of Skeletonized Mean Diffusivity and Cognition in Cerebral Amyloid Angiopathy

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Mitchell J Horn ◽  
Elif Gokcal ◽  
J. A Becker ◽  
Alvin S Das ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
White Matter ◽  
Regression Model ◽  
Cerebral Amyloid Angiopathy ◽  
Mean Diffusivity ◽  
Multiple Regression Model ◽  
Amyloid Angiopathy ◽  
Peak Width ◽  
Symbol Substitution ◽  
Relevant Variables ◽  
Cerebral Amyloid

Background: We hypothesized that Peak Width of Skeletonized Mean Diffusivity (PSMD), an automated marker of cerebral microangiopathy representing microstructural disruption of white matter (WM), would be increased in patients with cerebral amyloid angiopathy (CAA) compared to healthy controls (HCs) and increased PSMD would be associated with lower processing speed scores (PSSs) in patients with CAA. Methods: Seventy-two nondemented probable CAA patients and 23 HCs prospectively underwent high-resolution brain MRIs and cognitive tests. PSMD scores were quantified from a probabilistic skeleton of the WM tracts as previously validated (http://www.psmd-marker.com). In subjects with intracerebral hemorrhage (ICH, n=27), ICH regions were masked and removed from the PSMD pipeline. The analyses were repeated in the non-ICH hemisphere. Raw scores of Trail Making Test-B and Symbol Substitution Test were transformed into standardized z -scores and averaged to obtain PSSs. Results: The mean age (p=0.366) and sex (p=0.811) were similar between CAA patients and HCs. PSMD was higher in the CAA group [(3.95±0.9) х 10 –4 mm 2 /s] compared to HCs [(3.32±0.6) х 10 –4 mm 2 /s] (p=0.003). This association remained significant in a linear regression model corrected for age and sex (β=0.700, 95%CI 0.3-1, p=0.001). Within the CAA cohort, higher PSMD was associated with higher WM hyperintensity volume in a multiple regression model adjusted for all relevant variables (β=0.890, 95%CI 0.7-1, p<0.001). In a regression model corrected for age, sex, years of education and presence of ICH, a lower PSS was independently associated with increased PSMD (β=-0.405, 95%CI {-0.6}-{-0.2}, p<0.001). These results did not change when the non-ICH hemisphere was used for PSMD processing. Conclusion: PSMD is increased in CAA and is associated with worse PSSs supporting the view that disruption of white matter has a significant role in cognitive impairment in CAA.

Factors Affecting NPAs in Indian Banking Sector

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Banking Sector ◽  
Relative Weight ◽  
Multiple Regression Model ◽  
Factors Affecting ◽  
Weight Analysis ◽  
Specific Factors ◽  
Relative Weight Analysis ◽  
Bank Specific Factors

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.

scholarly journals An Illustrated Guide to Estimation of Water Velocity in Unregulated River for Hydrokinetic Performance Analysis Studies in East Malaysia

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1330
Author(s):  
Almalik Mohd Saupi ◽  
Nashiren Mailah ◽  
Mohd Mohd Radzi ◽  
Kamarul Mohamad ◽  
Saiful Ahmad ◽  
...  
Keyword(s):  
Regression Model ◽  
Water Level ◽  
Rural Areas ◽  
Electrical Energy ◽  
Peninsular Malaysia ◽  
Water Velocity ◽  
Velocity Estimation ◽  
Coefficient Of Determination ◽  
Velocity Data ◽  
Model Equations

Electrification coverage in Sarawak is the lowest at 78.74%, compared to Peninsular Malaysia at 99.62% and Sabah at 82.51%. Kapit, Sarawak, with 88.4% of its population located in rural areas and mostly situated along the main riverbanks, has great potential to generate electrical energy with a hydrokinetic system. Yearly water velocity data is the most significant parameter with which to perform a hydrokinetic analysis study. Nevertheless, the data retrieved from local river databases are inadequate for river energy analysis, thus hindering its progression. Instead, flow rates and rainfall data had been utilized to estimate the water velocity data. Till present, there is still no publication has been found on estimating of water velocity data in unregulated river using water level. Therefore, a novel technique of estimating the daily average water velocity data in unregulated rivers is proposed. The modelling of regression equation for water velocity estimation was performed and two regression model equations were generated to estimate both water level and water velocity on-site and proven to be valid as the coefficient of determination values had been R2 = 87.4% and R2 = 87.9%, respectively. The combination of both regression model equations can be used to estimate long-term time series water velocity data for type-C unregulated river in remote areas.

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