scholarly journals Correlation between academic performance and NBCE part I scores at a chiropractic college

2013 ◽  
Vol 27 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Amilliah W.M. Kenya ◽  
Hope M. Kenya ◽  
John Hart
Keyword(s):  
Missing Data ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Correlation Matrix ◽  
Regression Coefficients ◽  
Convenience Sample ◽  
Response Variable ◽  
National Board ◽  
Chiropractic College ◽  
Subject Categories

Objective This study investigates the association between pre-National Board assessments and National Board Part 1 scores (NBCE) at a chiropractic college. Methods A convenience sample of 24 students enrolled in the doctor of chiropractic degree program was recruited for the study. These were 6th and 7th quarter students who had registered to take NBCE in March 2011. Each student's class scores were computed and average numeric means score derived. Subject clusters that make up testable subject categories in NBCE also were computed to obtain a single numeric mean score. Pretests were administered in all areas tested in NBCE. Results were compared to the student's scores in NBCE using correlation and multiple linear regression for 14 predictors and one response variable (NBCE). Results Among the 14 correlations for 19 students (due to missing data when running the correlation matrix), six were moderate-to-strong and statistically significant. Two predictors qualified for multiple linear regression (where n = 22): mean anatomy and mean chemistry, both of which revealed similar regression coefficients. Conclusion Mean anatomy and mean chemistry scores were shown to be the best predictors of NBCE Part 1 results in this sample.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT BELI KONSUMEN PADA SITUS JUAL-BELI ONLINE

2020 ◽  
Vol 6 (2) ◽  
pp. 179
Author(s):  
I Gede Andri Setiawan ◽  
Sukardi Sukardi
Keyword(s):  
Data Analysis ◽  
Risk Perception ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Significant Influence ◽  
Regression Coefficient ◽  
Regression Coefficients ◽  
Test Results ◽  
Price Perception ◽  
Usefulness Perception

The purpose of this study were to analyze (1) the influence of trust of the purchase interest of online shop consumers, (2) the influence of risk perception of the  purchase  interest  of  online  shop  consumers,  (3)  the  influence  of  use fulness perception of the purchase interest of online shop consumers, (4) the influence of price perception of the purchase interest of online shop consumers. The populations in this research are  student of Ahmad Dahlah University Yogyakarta  Campus  One.  The  method  sample  is  purposif  sampling  with  the number  of  sample  as  much  as  100  respondents  from  three  Faculty  that  is, Economy  Faculty,  Psycology  Faculty,  and  FTDI.  Collecting  data  using questionnaires  that  was  done  validity  test  and  realibity  test.  This  research  was done by using multiple linear regression data analysis. The  results  of  this  study  indicate  that  (1)  the  trust  is  not  significant influence on consumer purchase interest of the sites online shop, this is evidenced by the significant value  is bigger  than the probability alpha value of 0.05 (0,206 > 0.05), and the regression coefficient has a  positive value of 0.062; (2)  the  trust is not significant influence on consumer purchase interest of the sites online shop, this  is  evidenced  by  the  significant  value  is  bigger  than  the  probability  alpha value of 0.05 (0, 0,234 > 0.05), and the regression coefficient has a positive value of  0.057;  (3)  the  usefulness  perception  is  significant  influence  on  consumer purchase interest of the sites online shop, this is evidenced by the significant value is  smaller  than  the  probability  alpha  value  of  0.05  (0.000  <  0.05),  and  the regression coefficients has a positive value of 0.456; (4) the price perception is not significant  influence on consumer purchase interest of the sites online shop, this  is  evidenced  by  the  significant  value  is  bigger  than  the  probability  alpha value of 0.05 (0.066 > 0.05), and the regression coefficient has a  positive value of  0.111;  and  (5)  trust,  risk  perception,  usefulness  perception  and  price perception  together  have  an  effect  on  consumer  purchase  interest  of  the  sites online  shop,  this  is  evidenced  by  the  significant  value  is  smaller  than  the probability alpha value of 0.05 (0.000 < 0.05). The R2 test results in this study was obtained  R2 value  of  0.413.  This  shows  that  purchase  interest  is  influenced  by trust, risk perception, use perception, and price  perception by 41.3%, while the remaining 58.7% is influenced by other factors not observed by the researcher or considered fixed.

scholarly journals . Reflection of individual typological properties in the tremorogram (Predicting the indicators of Rusalov’s test according to the indicators of the tremorogram)

2021 ◽  
Vol 11 (5) ◽  
pp. 213-222
Author(s):  
B. A. Lobasyuk ◽  
L. N. Akimova ◽  
A. N. Stoyanov ◽  
A. V. Zamkovaya
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Target Feature ◽  
Left Hand ◽  
Right Hand ◽  
Postural Load ◽  
Significant Regression ◽  
The Right

Rationale for choosing. An increase in physiological tremor (Tr) in emotionally saturated situations is reflected not only in fiction, but also in scientific literature. In other words, tremors and emotional responses are interconnected. Purpose. To investigate the reflection of individual-typological properties in a tremorogram using V. M. Rusalov’s mathematical modeling. Material and methods. Tr was recorded using a linear transducer. Tr was recorded under postural load (arms extended forward). The sensor was alternately placed on the outstretched right and left arms in front of oneself, under conditions of “eyes open” (operative rest). The analysis of the tremorogram (TrG) files was carried out after the end of the study using the "Analist - 2" software according to the half - period analysis algorithm. To study the individual psychological characteristics of the personality, we used the method of determining the properties of the temperament by V.M. Rusalov. Each of the many indicators of Rusalov’s test selected in the analysis was considered as a target feature (Y-s), and the amplitudes and frequencies of TrG were considered as influencing variables (sets of X - s) and multiple linear regression equations of the form were built: The parameters of the amplitude and frequency of EEG rhythms were used as Xs. Own research. In multiple regression analysis of the influence of TrG indices of the right hand on the indices of Rusalov’s test, 12 statistically significant regression coefficients were determined, and 11 statistically significant regression coefficients for the left hand. After obtaining the diagnostic equations of multiple linear regression, describing the influence of TrG indicators on the indicators of Rusalov’s test, an attempt was made, using these equations, to obtain the indicators of Rusalov’s test, using the tremor indicators. On average, the% discrepancy between the determined and predicted indicators was 97.42% for the right hand, and 101.98 for the left. Conclusions. 1. With the use of diagnostic equation, it was possible to predict the indicators of psychological testing according to Rusalov’s test by the indicators of tremor of the right and left hands.2. Influence of Rusalov’s test indicators on TrG indicators were less in modulus than the influence of TrG indicators on the indicators of Rusalov’s  test, i.e. did not participate in the control of the mechanisms of TrG generation.3. The results obtained indicate that tremor indicators contain information about the subject-activity and communicative aspects of temperament according to V. M. Rusalov.

Multiple linear regression is a useful alternative to traditional analyses of variance

1988 ◽  
Vol 255 (3) ◽  
pp. R353-R367 ◽  
Author(s):  
B. K. Slinker ◽  
S. A. Glantz
Keyword(s):  
Missing Data ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Analysis Of Variance ◽  
Continuous Variable ◽  
Parameter Estimates ◽  
Regression Problem ◽  
Regression Approach ◽  
Linear Regression Problem ◽  
Analyze Data

Physiologists often wish to compare the effects of several different treatments on a continuous variable of interest, which requires an analysis of variance. Analysis of variance, as presented in most statistics texts, generally requires that there be no missing data and often that each sample group be the same size. Unfortunately, this requirement is rarely satisfied, and investigators are confronted with the problem of how to analyze data that do not strictly fit the traditional analysis of variance paradigm. One can avoid these pitfalls by recasting the analysis of variance as a multiple linear regression problem. When there are no missing data, the results of a traditional analysis of variance and the corresponding multiple regression problem are identical; when the sample sizes are unequal or there are missing data, one can use a regression formulation to analyze data that cannot be easily handled in a traditional analysis of variance paradigm and thus overcome a practical computational limitation of traditional analysis of variance. In addition to overcoming practical limitations of traditional analysis of variance, the multiple linear regression approach is more efficient because in one run of a statistics routine, not only is the analysis of variance done but also one obtains estimates of the size of the treatment effects (as opposed to just an indication of whether such effects are present or not), and many of the pairwise multiple comparisons are done (they are equivalent to t tests for significance of the regression parameter estimates). Finally, interaction between the different treatment factors is easier to interpret than it is in traditional analysis of variance.

scholarly journals An upper-branch Brewer–Dobson circulation index for attribution of stratospheric variability and improved ozone and temperature trend analysis

2016 ◽  
Vol 16 (24) ◽  
pp. 15485-15500 ◽  
Author(s):  
William T. Ball ◽  
Aleš Kuchař ◽  
Eugene V. Rozanov ◽  
Johannes Staehelin ◽  
Fiona Tummon ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Trend Analysis ◽  
Regression Coefficients ◽  
Circulation Index ◽  
Wave Forcing ◽  
Altitude Dependence ◽  
Total Variability ◽  
Stratospheric Variability ◽  
Middle Stratosphere

Abstract. We find that wintertime temperature anomalies near 4 hPa and 50° N/S are related, through dynamics, to anomalies in ozone and temperature, particularly in the tropical stratosphere but also throughout the upper stratosphere and mesosphere. These mid-latitude anomalies occur on timescales of up to a month, and are related to changes in wave forcing. A change in the meridional Brewer–Dobson circulation extends from the middle stratosphere into the mesosphere and forms a temperature-change quadrupole from Equator to pole. We develop a dynamical index based on detrended, deseasonalised mid-latitude temperature. When employed in multiple linear regression, this index can account for up to 60 % of the total variability of temperature, peaking at  ∼  5 hPa and dropping to 0 at  ∼  50 and  ∼  0.5 hPa, respectively, and increasing again into the mesosphere. Ozone similarly sees up to an additional 50 % of variability accounted for, with a slightly higher maximum and strong altitude dependence, with zero improvement found at 10 hPa. Further, the uncertainty on all equatorial multiple-linear regression coefficients can be reduced by up to 35 and 20 % in temperature and ozone, respectively, and so this index is an important tool for quantifying current and future ozone recovery.

scholarly journals Selection of Appropriate Model for Quality of Life Data of Strabismus Patients Despite Censorship

2021 ◽  
Author(s):  
Zahra Ghassemi ◽  
Mehdi Yaseri ◽  
Mostafa Hosseini
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Tobit Model ◽  
P Value ◽  
Response Variable

Introduction: Previous studies on the quality of life of strabismus patients have not examined the existence of censoring to express the relation between the response variable and its predictors. Methods & Materials: The information used in this study is a conducted cross-sectional study in 2012. The sample size is 90 children in the age range (4-18) years and with congenital strabismus. We used the RAND Health Insurance Study questionnaire with ten subscales to evaluate the quality of life, which was increased to 11 dimensions by adding some items related to eye alignment concerns introduced by Archer et al. The demographic profile is also recorded by 13 other questions. We have expressed the relationship between the independent and response variables in each of the 11 dimensions of the questionnaire and the overall quality of life score by fitting the multiple linear regression model. Then we fitted the two models of classic Tobit and CLAD, which are for censoring, to all dimensions of the questionnaire. Results: We showed that in fitting the models to the overall quality of life scale variable, the best model is the multiple linear regression. Because the response variable was normal, and there was no censoring (ceiling and floor effect). However, in the depression subscale, due to the high censoring (28.89% of the ceiling effect) and the almost normal distribution of the response variable (p-value of skewness< 0.05), the appropriate model according to the criteria is the classic Tobit (AIC = 546.33). That is, the classic Tobit model is the best alternative to the multiple linear regression model in the presence of censoring. But these conditions did not exist in all variables. In the subscale, there was a severe censoring performance constraint (67.78% of the ceiling effect). When censoring is high, the distribution of the response variable becomes very skewed, and the distribution of response variables deviates drastically from normal. The distribution of the performance constraint variable was very skewed (p-value <0.001). Here the RMSE standard scale for the classic Tobit model was 28.74, which is much higher than the standard scale for the multiple linear regression model (14.23). The best model for the high censoring was CLAD. Conclusion: To use the appropriate statistical method in the analysis, one must look at how the response variable is distributed. The multiple linear regression model is very widely used, but in the presence of censoring, the use of this model gives skewed results. In this case, the classic Tobit model and its derived model, CLAD, are replaced. The nonparametric CLAD model calculates accurate estimates with minimum defaults and censoring.

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