Backgrounds Pregnancy related complications are major causes of maternal morbidity and mortality worldwide. Diversified food consumption is essential to produce hormones during pregnancy and it reduced complications. In Ethiopia, many researchers were investigated about the proportion of pregnant women with dietary diversity and its determinant factors. However, those studies are inconsistent and fragmented. Therefore, the aim of this study was to estimate the pooled proportion of pregnant women with dietary diversity practice and its associated factors in Ethiopia. Methods We conducted a systematic electronic web-based search of PubMed/ /MEDLINE, EMBASE, Web of Science, Google Scholar and Google online databases for identifying studies on proportion of pregnant women with dietary diversity practice and its associated factors in Ethiopia using pre-defined quality and inclusion criteria. STATA version 14 statistical software was used to analyze the data. We extracted relevant data and presented in tabular form. The I2 test was used to assess heterogeneity across studies. Funnel plot asymmetry and Begg’s test were used to check for publication bias. The final effect size was determined by applying a random-effects model. Results Our search identified 170 studies. Of which, 23 were included in the final analysis stage. The pooled proportion of dietary diversity among pregnant women in Ethiopia was 41% (95% CI: 33, 49). Mothers can read and write (OR = 1.82 (95% CI: 1.25, 2.64)), maternal primary school and above educated (OR = 2.11 (95% CI: 1.10, 4.05)), nutritional information (OR = 4.1 (95% CI: 2.1, 7.99), dietary diversity knowledge (OR = 3.4 (95% CI: 2.73, 4.73)) and household had rich wealth index (OR = 3.45 (95% CI: 1.19, 10.1)) were significantly associated with dietary diversity practice during pregnancy. Conclusions In this meta-analysis; we found that low proportion of pregnant women with adequate dietary diversity in Ethiopia (41%). Maternal education, nutritional information, dietary diversity knowledge and wealth index level of household were significantly associated factors of pregnant woman with dietary diversity practice. This finding implies that improving the awareness of woman about dietary diversity during pregnancy and empowering women economically would play a significant role to improve dietary diversity practice.

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Nature create variables using its character component, and variables are sharing characters from a vary small to relatively large scale. This results, variables to have from a vary different to a more similar character, and leads to have a relation ship. Literature suggested different relation measures based on the nature of variable and type of relation ship exist. Today, due to having high variety of frequently produced large data size, currently suggested variable filtering and selection methods have gaps to full fill the need. This research desires to fill this gap by comparing literature suggested methods to finding out a better variable selection and dimension reduction methods. The result from regression analysis using all literature suggested factors shows that none of the predictors for development status of enterprise are significant, and only 10 predictors for number of employer in an enterprise are significant out of 81 factors. Since, variable selection and dimension reduction methods are applied to find out predictors of a response by removing variable redundancy, and complexity of incorporating large number variable. Based on statistical power, for the results from variable selection methods, specially association and correlation methods showed that, CANOVA more efficiently detects non-linear or non-monotonic correlation between a continuous-continuous and a continuous-categorical variables. Spearman's correlation coefficient more efficiently detects a monotonic correlation between a continuous with a continuous, and a continuous with a categorical variable. Pearson correlation coefficient more efficiently detects the linear correlation between continuous variables. MIC efficiently detects non-linear or non-monotonic relation between continuous variables. Chi-square test of independence efficiently detects relation between a continuous with a continuous, and categorical with categorical variables, but the non linear or non monotonic relation between a continuous with a categorical are not well detected. On the other hand, the result from lasso and stepwise methods reveals that, the relation between the predictor and response due to interaction effect not detected by correlation and association methods are detected by stepwise variable selection method, and the multicollinearity is detected and removed by lasso method. Regressing the response variable "number of employer in an enterprise" based on variables selected by lasso and stepwise method does bring greater model fitness (based on adjusted R-squared value) than variables selected by association and correlation methods. Similarly, regressing the response variable "development status of an enterprise" based on variables selected by association and correlation methods does bring

IntroductionNumerous natural and man-made factors have afflicted Ethiopia, and millions of people have experienced food insecurity. The current cut-points of the WFP food consumption score (FCS) have limitations in measuring the food insecurity level of different feeding patterns due to the diversified culture of the society. The aim of this study is to adapt the WFP food security score cut-points corrected for the different feeding cultures of the society using effect-driven quantile clustering.MethodThe 2012, 2014, and 2016 Ethiopian socio-economic household-based panel data set with a sample size of 3,835 households and 42 variables were used. Longitudinal quantile regression with fixed individual-specific location-shift intercept of the free distribution covariance structure was adopted to identify major indicators that can cluster and level quantiles of the FCS.ResultHousehold food insecurity is reduced through time across the quintiles of food security score distribution, mainly in the upper quantiles. The leveling based on effect-driven quantile clustering brings 35.5 and 49 as the FCS cut-points corrected for cultural diversity. This corrected FCS brings wider interval for food insecure households with the same interval range for vulnerable households, where the WFP FCS cut-points under estimate it by 7 score. Education level, employment, fertilizer usage, farming type, agricultural package, infrastructure-related factors, and environmental factors are found to be the significant contributing factors to food security. On the other hand, the age of the head of the household, dependency ratio, shock, and no irrigation in households make significant contributions to food insecurity. Moreover, households living in rural areas and farming crops on small lands are comparatively vulnerable and food insecure.ConclusionMeasuring food insecurity in Ethiopia using the WFP FCS cut-off points underestimates households’ food insecurity levels. Since the WFP FCS cut-points have universality and comparability limitations, there is a need for a universally accepted local threshold, corrected for local factors those resulted in different consumption patterns in the standardization of food security score. Accordingly, the quantile regression approach adjusts the WFP-FCS cut points by adjusting for local situations. Applying WFP cut-points will wrongly assign households on each level, so the proportion of households will be inflated for the security level and underestimated for the insecure level, and the influence of factors can also be wrongly recommended the food security score for the levels. The quantile clustering approach showed that cropping on a small land size would not bring about food security in Ethiopia. This favors the Ethiopian government initiative called integrated farming “ኩታ ገጠም እርሻ” which Ethiopia needs to develop and implement a system that fits and responds to this technology and infrastructure.