Introduction to Square Analysis

What is/are Square Analysis?

Square Analysis - Frequency and chisquare analysis was used to present the results. ^[1] Quasi-experimental type, Chi-square analysis, calculating odds ratio, Chi-square test. ^[2] Results: From a total of 8,199 subjects, those who are female, Asian, black, Hispanic, ages 18–24 years old, have a family income <$35,000, unemployed, not wearers of corrective lenses, and have not seen a general or eye care provider showed increased no PE usage according to chi-square analysis. ^[3] The Chi-square analysis was done through the SPSS for Mac software, version 21. ^[4] A chi-square analysis was used to compare discrete data. ^[5] Descriptive statistics and Chi-square analysis were used to present data through Statistical Package of Social Sciences software. ^[6] To analyze the data, chi-square analysis and logistic regression were used. ^[7] The analysis used is descriptive analysis and Chi-Square analysis. ^[8] Statistical analyses included students' t-tests, chi-square analysis and analysis of variance. ^[9] Descriptive statistics and Chi-square analysis were conducted, and the level of significance was set at P < 0. ^[10] The chi-square analysis highlighted the presence of phenotypic variability within and between accessions from each agro-climatic zone for most of the traits evaluated indicating some adaptive forms related to the four zones. ^[11] The analytical method used is Chi-Square analysis using SPSS 18. ^[12] Chi-Square analysis was made to obtain p-value and confidence interval of statistical associations. ^[13] Chi-square analysis was used to determine distribution of differences among groups. ^[14] Data analysis included summary statistics, Student's t-test, and chi-square analysis, as appropriate, in addition to assessing convergence and agreement among measures. ^[15] This data are analyzed using second-level statistical techniques, such as chi-square analysis and ANOVA. ^[16] The data was analyzed for statistical significance using student t-test, chi-square analysis. ^[17] The Chi-Square analysis results showed that the p value is 0,020, meaning that there was a significant relationship between predialysis respiratory rate and intradialysis muscle cramps. ^[18] Chi-square analysis, t tests, and Fisher’s exact tests were used to determine significant associations. ^[19] The results of chi-square analysis showed a signifcant relationship between family knowledge with rheumatic recurrence in elderly and family attitude with rheumatic recurrence in elderly. ^[20] Descriptive and Chi-square analysis were done using SPSS version 16. ^[21] We compared early birth complications by ethnicity and DIP status using chi-square analysis. ^[22] Chi-square analysis was used to evaluate the categorical variables. ^[23] Chi-square analysis showed that age, education and household size significantly influenced preference for LSDs; marital status and occupation did not (P<0. ^[24] Comparisons of success of ECV between the BMI categories were made using chi-square analysis with normal BMI as the reference group. ^[25] Chi-square analysis has shown that there are significant relationship betweenage (p=0,006 and 0,000; POR=7,25 and 31,88), sex (p=0,027; POR=2,87), family medical record (p=0,000; POR=11,3), educational status (p=0,000 and 0,001; POR=24,37 and 9,37), nutritional status (p=0,000; POR=11,43), physical activity(p=0,019 and 0,002; POR=4,01 dan 6,07) with hypertension. ^[26] Two applied tests were chi-square analysis for bivariate analysis test and logistic regression analysis for multivariate analysis test. ^[27] Chi-square analysis revealed that junk food consumption (OR=3,152; 95% CI=1,253-7,925; P=0,023), and soft drink consumption (OR=4,747; 95% CI=1,797-12,539; P=0,002) were the risk factors of prehypertension. ^[28] Based on the Chi-Square analysis, respondent's characteristics that influenced the improvement of respondents’ knowledge was only the age and job (p <0. ^[29] Statistical methods included chi-square analysis, student T-test and propensity score matching. ^[30] Data were analyzed by descriptive statistical methods such as number, percentage, mean, and chi-square analysis. ^[31] Measurements and Main Results Chi-square analysis and Student's t-test were used to describe the population and compare groups. ^[32] Association between PET/CT use and change in management was tested using chi-square analysis. ^[33] Chi-square analysis was used to determine whether statistically significant differences in success rates were found between biometers. ^[34] In the analysis of the data, frequency, percentage, and chi-square analysis are used. ^[35] Chi-square analysis was performed. ^[36] In order to assess the efficiency of this method, histogram analysis, data loss attack, salt-pepper noise attack, differential attack, chi-square analysis and correlation analysis tests were applied. ^[37] Chi-square analysis assessed whether any specific clinical features were significantly associated with a positive diagnostic yield. ^[38] Descriptive statistics, Student's t tests, and Mann–Whitney nonparametric 2-group tests with chi-square analysis were used. ^[39] Features of accurate and inaccurate websites were compared using chi-square analysis for categorical data, and Mann-Whitney U for continuous and ordinal data. ^[40] Data collected within 20 days were processed with dental waste laboratory tests and chi-square analysis. ^[41] One-way ANOVA and Pearson's chi-square analysis were used to compare perioperative outcomes stratified by HCC score. ^[42] Chi-square analysis determined associations between demographics and adherence, and having unused medication. ^[43] Students’ t-test for continuous and chi-square analysis for categorical variables were completed. ^[44] Age-based analysis was performed using chi-square analysis. ^[45] We examined the relationship between patient characteristics, postoperative hyperglycemia (any value > 180 mg/dL), and complications with chi-square analysis. ^[46] The researchers employed statistical tools such as Cluster Analysis, Chi-Square analysis and Linear Multiple regression analysis for the study about the intrinsic relationship among the factors of quality of work-life and with other four effects of quality of work-life. ^[47] The results were examined by chi-square analysis. ^[48] Data on practices and perspectives on the disposal of medications were collected from members of the public using a questionnaire, and subjected to chi-square analysis of demographic variables. ^[49] The relationship between the level of adherence to clinical outcomes using Chi-square analysis while the relationship between the level of adherence to quality of life using Spearman Rho analysis. ^[50]

binary logistic regression

Chi-square analysis and binary logistic regression statistical methods were used to analyze the correlation between influencing factors and prognosis. ^[1] Descriptive statistic, bivariate Chi-square analysis and binary logistic regression were conducted to determine factors influencing low QOL and poor health status. ^[2] Descriptive statistics, linear regression, binary logistic regression and chi square analysis were performed to evaluate the data. ^[3] Their responses were then compared to their compliance rate by chi-square analysis and binary logistic regression in SPSS version 23 (IBM Corp, Armonk, NY, US). ^[4] The data was explored and Chi-square analysis alongside binary logistic regression was applied in order to evaluate the relationship between low back pain and some associated factors. ^[5] Chi-square analysis and binary logistic regression analysis were applied to identify the key factors associated with the presence of Cx. ^[6] Data was analysed using chi-square analysis, t-test, two-way ANOVA and binary logistic regression. ^[7]

multivariate logistic regression

Results were compared using chi square analysis and examined using multivariate logistic regression. ^[1] Chi-Square analysis and weighted multivariate logistic regression was performed. ^[2] Correlation between textural parameters, histologic response, biochemical response, and genetic mutations was assessed using Mann-Whitney test, chi-square analysis, and multivariate logistic regression. ^[3] Data was analyzed with univariate analysis via chi square analysis and multivariate logistic regression analysis. ^[4] Results were compared using chi square analysis and examined using multivariate logistic regression. ^[5]

p value 0

Chi-square analysis (p-value: 0. ^[1] This study used chi-square analysis with interpretation p-value 0. ^[2] The chi-square analysis of characteristics of the patient and medication adherence showed that there was a significant relationship between side effect (p-value 0, 025) with medication adherence. ^[3] The results of the chi-square analysis showed that the variable completeness and accuracy of information provided by nurses (p value 0,000), skilled and professional nurse variables (p value 0. ^[4]

multiple logistic regression

Socioeconomic positions in relation to clustering were identified through Chi-square analysis followed by multiple logistic regression where clustering at family and area was adjusted through multilevel modeling techniques. ^[1] Data analysis was done in three stages: univariate analysis, bivariate analysis with chi square analysis method, multivariate analysis with multiple logistic regression analysis method. ^[2] Data analysis method with chi square analysis and multiple logistic regression. ^[3]

significant differences among

Hence, chi square analysis was run to ascertain significant differences among clusters based on business-related variables. ^[1] One-way ANOVA and chi-square analysis was employed to assess and test the existence of significant differences among project implementation management approaches. ^[2] The chi-square analysis showed that on parameter wise comparisons, there were no significant differences among the levels as treated based on the various oils except gross energy, Mg, K, P, Ca/P, Fe/Pb and K/Co. ^[3]

determine statistical significance

Fisher's exact test and chi-square analysis were used to determine statistical significance. ^[1] Percent incorrect for each item was used to determine common errors, and Pearson chi-square analysis was used to determine statistical significance between percent incorrect versus PGY level as well as method of Sig entry. ^[2] Data were analyzed in SAS, variables were compared by chi square analysis and Fishers Exact test to determine statistical significance. ^[3]

mean square error

For theoretical analysis, the mean square analysis of RLMLS is provided in terms of the mean square deviation (MSD) and excess mean-square error (EMSE) with a white Gaussian reference. ^[1] The models were compared based on performance indices like coefficient of determination, mean square error, root mean square error (RMSE), model predictive error, mean average deviation, goodness of fit, and chi‐square analysis. ^[2]

Chi Square Analysis

Data were analyzed using Chi square analysis, principal components analysis, and calculation of Cronbach's alpha. ^[1] Demographics between groups were compared using Chi Square analysis. ^[2] Data analyzed by univariate and bivariate using Chi Square analysis. ^[3] Chi square analysis was used to compare SRI strategies used (lower sodium product replacement; removal of a high sodium ingredient; reduced ingredient serving size; and combined strategies) and their resulting sodium reduction categories per item (<200, 200-499, 500-999, or ≥1000 mg of sodium). ^[4] Data collected were analysed using descriptive and chi square analysis. ^[5] Cases were combined into a single dataset and univariate and chi square analysis (p < 0. ^[6] The chi square analysis showed a correlation between income (p value = 0,001) with choices of JKN’s membership class, and no correlation between accessibility (p value = 0,131) and services quality (p value = 0,091) with choices of JKN’s membership class. ^[7] Hence, chi square analysis was run to ascertain significant differences among clusters based on business-related variables. ^[8] Chi square analysis was performed to compare device and non device related complication rates. ^[9] Results were compared using chi square analysis and examined using multivariate logistic regression. ^[10] Pearson's Chi square analysis and Student's T test analysis were performed. ^[11] Internal consistency of the scale was determined with Chronbach's alpha and Pearsons chi square analysis was used to analyze relationships between demographic variables and attitudes scores. ^[12] Chi square analysis was employed for comparison of adequacy of both the techniques, the P value was found to be 0. ^[13] Using chi square analysis, propensity score analysis, and multiple regression models (of the total sample, public school sample, and private school sample), as well as Z-score coefficients, findings suggest that a positive school climate predicted less reporting of bullying incidents and that private school students in particular reported a more positive school climate and less bullying. ^[14] A chi square analysis was completed to understand the prevalence rate among the three groups and a logistical regression was conducted to understanding the association between timing from injury to surgery and presence of concomitant pathology. ^[15] The results were subsequently presented in absolute numbers and percentages and subjected to chi square analysis were applicable. ^[16] Statistically, Chi square analysis showed that there was no significant difference in the number of isolates from SHS, MAWCHS and NCHS and in the occurrence of organism in relation to gender and age (p˃0. ^[17] Chi square analysis was performed to identify any association between biopsy site location and the degree of expression of the different cell markers. ^[18] Secondary analyses were performed using chi square analysis for demographic risk factors, and t-test for the continuous outcome variables. ^[19] Chi square analysis was used to compare adherence to recommendations before and after program implementation. ^[20] Results: From the results of the Chi Square analysis, it is known that there is a significant relationship that is the attitude towards pulmonary Tuberculosis disease and physical activity towards pulmonary Tuberculosis (p 0. ^[21] As a research method, 63,929 elderly people aged 65 or older were surveyed using the Community Health Survey (Indicator Bank) _v09, and the frequency of health use by economic level, subjective health level, euphoria and quality of life Analysis and Chi square analysis and independent t-test. ^[22] At Chi Square analysis no statistically significant differences (all p > 0. ^[23] The results of the study after Chi Square analysis obtained the value of Sig. ^[24] Descriptive statistics, linear regression, binary logistic regression and chi square analysis were performed to evaluate the data. ^[25] Chi square analysis was used to compare percentages. ^[26] Results from the first year was compared to those from the third year using descriptive statistics and chi square analysis. ^[27] Chi square analysis results are significant if the p value is < 0. ^[28] This difference was significant by chi square analysis (p<0. ^[29] Chi square analysis was used to detect baseline differences between the three groups. ^[30] Chi square analysis showed smoking to be associated with prolonged time to full mucosalization of the sella. ^[31] The results using chi square analysis obtained non-obstetrical factors: age >60 years old ( OR 6. ^[32] Data analysis was done in three stages: univariate analysis, bivariate analysis with chi square analysis method, multivariate analysis with multiple logistic regression analysis method. ^[33] Chi square analysis and non-parametric tests for significance were applied. ^[34] The results of the study: the results of Chi Square analysis p = 0. ^[35] Chi square analysis was used to ascertain significant differences (P≥ 0. ^[36] The results of the chi square analysis of the data suggested that although rate of use of passive voice was higher than pseudo-cleft, the differences between males and females, and graduates and postgraduates were minor and hence gender and university degree did not significantly influence the rate of use of the structures. ^[37] Data were analyzed with chi square analysis on STATA software. ^[38] Statistical analysis performed utilizing t tests and Chi square analysis; p  < 0. ^[39] The analysis technique used with chi square analysis. ^[40] The observation frequency was subsequently tested by Chi Square analysis. ^[41] The result of chi square analysis revealed that social support, personal self-efficacy, age, marital status, religion, educational level and occupation had significant association with non-adherence at 95% CI,  6. ^[42] Material and Methods: This study is a secondary data analysis with the design of Cross Sectional and uses Chi Square analysis. ^[43] Chi square analysis was done and p value Results: 52 ovarian tumor patients were included in the study period. ^[44] Chi square analysis also demonstrated that women with night vs day-only shifts reported BDI scores in the critical range (p<0. ^[45] Chi square analysis was used to measure significance of associations and p <  0. ^[46] The chi square analysis was significant (P<0. ^[47] Data collected from questionnaire filled by subjects followed by chi square analysis. ^[48] Chi square analysis (α=0. ^[49] Main outcome measure Adherence to guideline indicators before and after intervention, calculated by proportions, Mann-Whitney U and Chi square analysis. ^[50]

Least Square Analysis

Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78. ^[1] Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. ^[2] By implemeting the partial least square analysis method to measure the correlation between variables, the result of the research found that e-impulse buying and user interface design have a significant influence on consumer buying interest in website. ^[3] In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. ^[4] The Generalized Inversion Technique (GIT) assuming that a reference site was applied to isolate the source one, site, and path effects from the observed displacement P-wave spectra by means of linear least square analysis. ^[5] Another finding based on Partial Least Square analysis shows that family values that have a significant positive effect on adolescent character habituation are self-control and emotion ( p =0. ^[6] The data analysis technique uses Partial Least Square analysis. ^[7] Ordinary Least Square analysis (OLS) was used to study the significance of independent variables towards Tobin’s Q. ^[8] ULNs were studied using least square analysis. ^[9] Thisstudy focused on the measurement model and structural model of Partial Least Square analysis. ^[10] The utilized analytical tool is partial least square analysis assisted by SmartPLS 3. ^[11] An intrinsically one-dimensional free energy calculation method developed in our recent works is successfully employed in these studies: First, one-dimensional umbrella samplings are performed using the two reaction coordinates; Then, bin-segmentations are performed along the transition paths in multi-dimensional phase spaces; Finally, the weighted least square analysis method (Welsam) is used for free energy analysis. ^[12] We processed the data using partial least square analysis technique. ^[13] This study uses the positivistic paradigm with quantitative research methods through the analysis of Structural Equation Modeling - Partial Least Square analysis. ^[14] Applying partial least square analysis on 246 responses from domestic tourists who visited Taitung in Taiwan, this study found that the contribution of smartphones to trip satisfaction is limited, and is often confined to applications that help tourists c have visited Taitung only once; these visitors use smartphones for functional and communication purposes. ^[15] ,To test the hypotheses, the partial least square analysis is applied to questionnaire survey data from 201 employees from State Province Government of Indonesia. ^[16] To see the effect of the independent variables on the dependent variable, the Ordinary Least Square analysis model is used. ^[17] Data were simultaneously tested using partial least square analysis with the help of Smart PLS 3 software. ^[18] The method used is the Partial Least Square analysis method which is doing descriptive statistical analysis and causal analysis that aims to find out whether the factors that influence and become obstacles for SMEs to participate in public procurement. ^[19] Hypothesis testing are conducted using Partial Least Square analysis (PLS). ^[20] The Purpose of this study is to investigate wheter rotation influences job satisfaction and permormance job sastifaction influences performance of 93 staffs using partial least square analysis. ^[21] Both datasets were analyzed using an innovative chemometric approach involving principal component analysis (PCA) and multivariate curve resolution – alternating least square analysis (MCR‐ALS) yielding new insights on the sodiation reaction. ^[22] Data on 3244 Sirohi kidding during 2004 to 2016 in farmer’s flocks under All India Co-ordinated Research Project on Goat Improvement (AICRP) project, Vallabhnagar, Udaipur were utilized to estimate the average daily milk (ADM) at different lactation months and subjected to least square analysis to study the effect of various non-genetic factors like cluster, periods of kidding, season of kidding, parity, type of birth and regression of dam’s weight. ^[23] This study uses partial least square analysis (PLS) with the help of PLS software program. ^[24] Structural equation model with partial least square analysis tool is used to prove empirically the effect of each variables and the hypothesis testing. ^[25] The co-variation between leaf form (shape and size) and climate variables was significant, estimated by two-block partial least square analysis. ^[26] 9742 based on the least square analysis. ^[27] The data were described and analyzed by using partial least square analysis (PLS). ^[28] After collecting users’ data, partial least square analysis is used to examine the main driving and constraining factors for users to download paid apps in Apple mobile devices. ^[29] Least square analysis of variance did not reveal any significant influence of nucleotide deletion on any sperm motility parameters in both crossbred and purebred cattle. ^[30] The sampling technique used in this study is purposive sampling using a Partial Least Square analysis tool with a population of all users of Go-Food services in the city of Surabaya. ^[31] A research model is estimated to the data through a partial least square analysis. ^[32] This study uses ordinary least square analysis to prove our hypotheses. ^[33] Partial least square analysis was used for data analysis by using SmartPLS software. ^[34] Two dimensional QSAR models had been developed by multiple linear regression and partial least square analysis methods, and then validated for internal and external predictions. ^[35] A partial least square analysis allowed identifying odors associated with a specific taste. ^[36]

Mean Square Analysis

The mean square analysis for the rice genotypes was highly significant for all studied traits, indicating that the differences among the rice genotypes and their traits. ^[1] For theoretical analysis, the mean square analysis of RLMLS is provided in terms of the mean square deviation (MSD) and excess mean-square error (EMSE) with a white Gaussian reference. ^[2] The linearized description of the second power quantizer (PTQ) is obtained by the simplified-linearization method in the algorithm, and then used to stabilize the steady-state mean square analysis of the LMF-PTQ algorithm. ^[3] A mean square analysis of the CSMC algorithm is presented to verify the validity of our theory. ^[4] A novel transient and steady-state complementary mean square analysis of the general diffusion complex least mean square (G-DCLMS) algorithm, including diffusion complex LMS (DCLMS) and the diffusion augmented complex LMS (DACLMS) algorithm is proposed. ^[5] METHODS Surface-to-Surface root mean square analysis was used to evaluate both a consumer device (Sense 3D) and a professional surface scanner (Artec Eva) against a reference imaging system (Vectra XT). ^[6] The BAT dynamic power spectrum and a fractional root mean square analysis both show strong variations in the amplitude of the superorbital modulation, but no observed changes in the period were found. ^[7]

square analysis showed

The results of chi-square analysis showed a signifcant relationship between family knowledge with rheumatic recurrence in elderly and family attitude with rheumatic recurrence in elderly. ^[1] The chi square analysis showed a correlation between income (p value = 0,001) with choices of JKN’s membership class, and no correlation between accessibility (p value = 0,131) and services quality (p value = 0,091) with choices of JKN’s membership class. ^[2] Chi-square analysis showed that age, education and household size significantly influenced preference for LSDs; marital status and occupation did not (P<0. ^[3] Chi-square analysis showed a significantly lower proportion of insertions contained pressure spikes when the control system was used (p << 0. ^[4] Chi-square analysis showed tumor diameter, clinical stage and ECOG points were significant independent factors impacting on complete remission rate(P =0. ^[5] The chi-square analysis showed a significant difference between the duration of breastfeeding and the incidence of stunting (p= 0,000), birth weight with the incidence of stunting (p= 0,000). ^[6] Results: The chi-square analysis showed that high MPV could not be used as a predictor of the atherosclerotic severity based on Syntax score in NSTEMI patients (p value =0. ^[7] Statistically, Chi square analysis showed that there was no significant difference in the number of isolates from SHS, MAWCHS and NCHS and in the occurrence of organism in relation to gender and age (p˃0. ^[8] Chi-square analysis showed that the increased kernel number in Z1364 was inherited recessively by a single gene. ^[9] A chi-square analysis showed a significant (P <. ^[10] RESULTS Results from Chi-square analysis showed that there were no significant differences in demographic characteristics between current and former opioid poly-drug users except with respect to marital status. ^[11] Chi-square analysis showed a significant association between the effectiveness of GESS and respondents' sex (χ2=46. ^[12] Based on the selected area, the nested square analysis showed that the fruit vertical diameter, fruit diameter, and the number of seeds per fruit in the region are greater than the regional mean square, and the differences reached an extremely significant level as well. ^[13] The result of the chi-square analysis showed that the p-value of 0. ^[14] Chi square analysis showed smoking to be associated with prolonged time to full mucosalization of the sella. ^[15] The results of Chi-square analysis showed that there were significant differences in occupational stress among workers categorized by job position, working years, mealtime, sleeping time, and weekly work time (P<0. ^[16] Chi–square analysis showed that there was no significant difference (p>0. ^[17] Results from Chi-square analysis showed clients in private MMT program have higher odds of using opiates (OR: 4. ^[18] Results from chi-square analysis showed that withdrawal from higher (three or more glasses daily) kratom intake was associated with severe fatigue during kratom cessation. ^[19] Chi‐square analysis showed no appreciable difference (P < 0. ^[20] Chi-square analysis showed that there was significant relation between Severe preeclampsia, HELLP syndrome and eclampsia toward fetal outcome. ^[21] Chi-square analysis showed that FJV was related to ASDeg in both groups (χ2=3. ^[22] Chi-square analysis showed that empirical antibiotic rationality related to good clinical outcome of CAP children (p = 0. ^[23] The results of the chi-square analysis showed that the diabetes mellitus, ASA score and type of surgery had a significant relationship with the incidence of SSI with significance value diabetes mellitus that is 0. ^[24] Results from Chi-square analysis showed kratom initiation was associated with decreased prevalence of respiratory depression, constipation, physical pain, insomnia, depression, loss of appetite, craving, decreased sexual performance, weight loss and fatigue. ^[25] A chi-square analysis showed that an abnormal inspiratory FVL was significantly more common in patients with a diagnosis of Ilo (χ2= 4. ^[26] The chi-square analysis showed that on parameter wise comparisons, there were no significant differences among the levels as treated based on the various oils except gross energy, Mg, K, P, Ca/P, Fe/Pb and K/Co. ^[27] Univariate chi-square analysis showed significantly lower recurrence rate after bone cement filling (2. ^[28] Chi-square analysis showed a significant association of three risk factors which are species, age and sex of the animals (P < 0. ^[29] RESULTS Results of the Chi-square analysis showed no significant association between the type of implant surface and rate of success or failure of the implant. ^[30] Chi-square analysis showed statically significant association between knowledge and utilization (X2 cal = 89. ^[31] The empirical results from the chi-square analysis showed that promotion as at when due, regular promotion, transparent promotion and when employees are satisfied with promotion has significant influence on employee turnover intention at 5 percent level of significance. ^[32] The single factor chi square analysis showed that there were significant differences in age, depth of focus, puncture position and puncture times (P<0. ^[33] Results: chi-square analysis showed that individually body image, fertility perception, and side effects variables were significantly related to postpartum contraceptive use with p values of 0. ^[34] Chi-Square analysis showed that there were correlation between hospital facility variable (p=0, 004), physician service (p=0. ^[35] The results of the chi-square analysis showed that the variable completeness and accuracy of information provided by nurses (p value 0,000), skilled and professional nurse variables (p value 0. ^[36] Chi-square analysis showed no significant difference in blastulation rates (p=0. ^[37]

square analysis revealed

Chi-square analysis revealed that junk food consumption (OR=3,152; 95% CI=1,253-7,925; P=0,023), and soft drink consumption (OR=4,747; 95% CI=1,797-12,539; P=0,002) were the risk factors of prehypertension. ^[1] Chi-square analysis revealed that BRAFV600E mutations were significantly associated with the male sex, lateral neck node metastasis, and several risk factors. ^[2] Chi-square analysis revealed that there was a significant relationship between occupation and type of mutual fund scheme opted by the investors. ^[3] A chi-square analysis revealed a significant relationship between attribution and mind-set about wisdom development. ^[4] Chi-square analysis revealed that visitors undertaking unplanned day visit to the sanctuary were more likely to be non-satisfied, raising questions on the type of visitors arriving at the sanctuary. ^[5] Chi-square analysis revealed a significant difference between the incidence of CMI in the early-presenting (group A) and late-presenting (group B) groups (P = 0. ^[6] The results of the Chi-square analysis revealed there was not a significant difference between the progression patterns in English and Persian except for the simple linear pattern. ^[7] Chi-square analysis revealed a significant relationship between parity and anemia (χ2 = 13. ^[8] Chi-square analysis revealed no significant relationship (p>0. ^[9] A comparison of parent responses on quantitative aspects of a structured interview using chi-square analysis revealed significant differences between the groups on relationship quality (x2 (2, N = 53) = 10. ^[10] Chi-square analysis revealed a positive association between the complete remission rate after induction therapy and weak expression of Notch2 and Notch3. ^[11] Chi-square analysis revealed that the 12 bp insertion was related to litter size (p < 0. ^[12] Chi-square analysis revealed that the highly expressed eIF3D group had larger ratios of patients with advanced pathological stage (68/40 vs. ^[13] Chi-square analysis revealed a significant difference between giving prophylaxis for URS or PCNL and the respective case volumes (for URS, X2 = 8. ^[14] Chi-square analysis revealed that individuals with BMI ≥ 25 kg/m2 showed higher frequency of the allelic variation Ala67Ala in AgRP rs5030980 with respect to those with BMI <25 kg/m2. ^[15] The result of chi square analysis revealed that social support, personal self-efficacy, age, marital status, religion, educational level and occupation had significant association with non-adherence at 95% CI,  6. ^[16] Wald chi-square analysis revealed that the period of birth, breed, and genotype were significantly associated with mastitis incidence. ^[17] Results of chi-square analysis revealed that Four Corners series dealt with LOTS and HOTS significantly more and above Prospect and Vision series. ^[18] Chi-square analysis revealed that being non-Caucasian, using cigarettes, and having a less than college-level education were associated with a higher prevalence of “limited” MHL (P < 0. ^[19] Chi-square analysis revealed statistically significant proportions of positive comments pertaining to surgeon-dependent factors (eg, physician personality, knowledge, skills) and of negative comments concerning surgeon-independent factors (eg, waiting time, logistics). ^[20] A chi-square analysis revealed that 1st-generation college students were 3 times less likely to have a job in place at graduation. ^[21] Chi-square analysis revealed that respondents’ areas of competency needs in value added fish production were influenced by their age, gender, level of education, and years of experience. ^[22] A Chi-square analysis revealed that Facebook commenters provided positive support to those sharing their experiences of depression and suicide ideation. ^[23] Chi-square analysis revealed that, among socio-economic characteristics of the respondents, religion(x2= 6. ^[24] Results : Pearson Chi-Square analysis revealed a total of five significant relationships between well-being activities and late assignments, being unprepared for class, and skipping class at α=. ^[25]

square analysis method

By implemeting the partial least square analysis method to measure the correlation between variables, the result of the research found that e-impulse buying and user interface design have a significant influence on consumer buying interest in website. ^[1] An intrinsically one-dimensional free energy calculation method developed in our recent works is successfully employed in these studies: First, one-dimensional umbrella samplings are performed using the two reaction coordinates; Then, bin-segmentations are performed along the transition paths in multi-dimensional phase spaces; Finally, the weighted least square analysis method (Welsam) is used for free energy analysis. ^[2] Data analysis was done in three stages: univariate analysis, bivariate analysis with chi square analysis method, multivariate analysis with multiple logistic regression analysis method. ^[3] The method used is the Partial Least Square analysis method which is doing descriptive statistical analysis and causal analysis that aims to find out whether the factors that influence and become obstacles for SMEs to participate in public procurement. ^[4] Along the MFEP, a one-dimensional bin-segmentation of the sample data is then used to calculate the potential of mean force accurately by the newly developed free energy analysis method: weighted least-square analysis method (Welsam). ^[5] Data were analyzed by Chi-Square analysis method. ^[6] Two dimensional QSAR models had been developed by multiple linear regression and partial least square analysis methods, and then validated for internal and external predictions. ^[7]

square analysis indicated

Results of chi-square analysis indicated characteristics of targets of bullying were significantly different between key and non-key school in 12 main categories. ^[1] However, a chi-square analysis indicated a significant association between gender and age of the birds and infection with helminthes. ^[2] A Chi-square analysis indicated the exposed group reported significantly higher incidences (P <0. ^[3] A chi-square analysis indicated significant patterns between the e-book comprehension scores and the usage of the literacy support tools, X2(6, n = 211) = 25. ^[4] The Chi-Square analysis indicated that all attributes studied were significantly different. ^[5] The results of Chi-square analysis indicated a good fit of this ratio in the hypothesis. ^[6]

square analysis result

The Chi-Square analysis results showed that the p value is 0,020, meaning that there was a significant relationship between predialysis respiratory rate and intradialysis muscle cramps. ^[1] The chi-square analysis results did not have a significant relationship between the level of education and knowledge of IUD counseling p = 0. ^[2] Chi square analysis results are significant if the p value is < 0. ^[3] Chi-square analysis results obtained there is influence of extension technique to IVA examination involvement with obtained value p value = 0,001 atau p < α=0,05. ^[4] Chi-square analysis results showed that cardiovascular disorders have significant relation with marital status, the number of children, work experience in the shift-working system, and the type of shift-work system (p<0. ^[5]

square analysis found

Chi-square analysis found that there was a correlation between DR and DCI (x2 = 4. ^[1] The results of this study indicated based on chi-square analysis found there was a correlation between knowledge with diet compliance with a significant value (p = 0,000), there was a correlation between motivation with diet compliance with a significant value (p = 0. ^[2] Chi-square analysis found a disparity in male and female participation across all opportunity spots (P=0. ^[3] Chi-square analysis found that there are significant association between farmers’ age, education level and farm assistant towards knowledge about new paddy variety. ^[4] Chi‐square analysis found 9 compounds that differed significantly between cephalexin sensitive and resistant isolates, and 22 compounds that differed significantly between ciprofloxacin sensitive and resistant isolates, at p ≤ 0. ^[5]

square analysis compared

Chi-square analysis compared rates for the year before March, 2012 with the same 12-month period 1 year later. ^[1] A Chi-square analysis compared injury rates by team, and hence by intervention; injury rates by years of experience running; and injury rates by dietary preference. ^[2] Chi-square analysis compared asthma prevalence between the two exposure groups and logistic regression analysis generated adjusted odds ratios (aORs) of asthma diagnosis by ETS exposure by sex, race/ethnicity, and household education and income level. ^[3] Chi square analysis compared identification of inpatients' who smoke and offer of NRT pre and post implementation. ^[4] Chi-square analysis compared means of cells harvested with respect to these variables. ^[5]

square analysis show

Another finding based on Partial Least Square analysis shows that family values that have a significant positive effect on adolescent character habituation are self-control and emotion ( p =0. ^[1] Chi-square analysis shows that history of previous treatment, HIV infection, drug side-effects status, Acid-Fast Bacilli (AFB) gradation, and duration of treatment were related to drug-resistant pulmonary TB. ^[2] RESULTS The chi-square analysis shows that there is no correlation between the knowledge level regarding junk food and the dietary habits of adolescents at the school (p 0. ^[3] Chi square analysis shows no association between Galectin-3 and diastolic dysfunction left ventricle, TIMI flow, MBG score, and acute heart failure. ^[4]

square analysis obtained

Chi-square analysis obtained value P = 0. ^[1] The results of the study after Chi Square analysis obtained the value of Sig. ^[2] The results using chi square analysis obtained non-obstetrical factors: age >60 years old ( OR 6. ^[3] Data from the results of chi-square analysis obtained a value of p = 0. ^[4]

square analysis assessed

Chi-square analysis assessed whether any specific clinical features were significantly associated with a positive diagnostic yield. ^[1] Chi-square analysis assessed bivariate differences among men who received different levels of PSA screening information. ^[2] Perason chi-square analysis assessed differences in clinical, hematologic, and echocardiographic parameters in HbSS patients with and without FMR, and HbSC patients with and without FMR. ^[3]

square analysis technique

This research is a kind of quantitative research which uses Chi-square analysis techniques. ^[1] We processed the data using partial least square analysis technique. ^[2] Then the collected data were analyzed using frequency, percentage, chi-square analysis techniques as well as narrative analysis. ^[3]

square analysis demonstrated

Chi-square analysis demonstrated significantly longer time in practice for practitioners without craniofacial fellowship training. ^[1] Chi-square analysis demonstrated a statistically significant relationship between the selected restorative method at condition A and both gender (P = 0. ^[2] Chi-square analysis demonstrated negative relationships between years as an occupational therapist and clinical skills assessment and between highest degree and use of evidence-based practices. ^[3]

square analysis determined

Chi-square analysis determined associations between demographics and adherence, and having unused medication. ^[1] Chi-square analysis determined associations among responses based on practitioner training. ^[2]

square analysis performed

Comparisons between groups were made using Chi-Square analysis performed in SAS. ^[1] The chi-square analysis performed (P<0. ^[2]