In probability and statistics, to study the correlation between two or several random variables or numerical statistics is to study the intensity of the bond that can exist between these variables. The association is looking for a relationship affine. In the case of two numeric variables, this is the linear regression. A measure of this correlation is obtained by calculating the coefficient of linear correlation. This coefficient is the ratio of their covariance and nonzero product of their standard deviations. The correlation coefficient is between -1 and 1. All of the selected articles have chosen the Correlation and Regression technique for the examination of the relationship between the defined variables (Cohen et al, 2003).
Simple linear regression examines the relationship between two variables, one of which is referred to as the predictor variable (i.e., the variable that usually precedes the other), and the other of which is referred to as the criterion variable (i.e., the variable that the researcher is interested in explaining, predicting, or better understanding). Because simple regression results provide an understanding of the patterns of relationships between the two variables of interest in a given context, we often use the term prediction in describing the relationship. The procedure is called “simple” because it includes only one predictor variable (Moore & McCabe, 2009).
Name of the article: “When do Babies Start to Crawl”
This study was conducted in University of Denver Infant Study Center. The aim of this study was to investigate whether infants take more time to learn to crawl while they are bundled in outfits that limit their movement in winter, than in summer. The study has taken a collective sample of 412 babies (208 boys and 206 girls) whereas; 40 babies were twins to observe. Parents bring their babies to University ...