The R function t.test() can be used to perform both one and two sample t-tests on vectors of data. The function contains a variety of options and can be called as follows: > t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) Here x is a numeric vector of data values and y is an optional numeric vector of data values...

In the mid-nineteenth century, almost 70 percent of persons age 65 or older resided with their adult children; by the end of the twentieth century, fewer than 15 percent did so. Many scholars have argued that the simplification of the living arrangements of the aged resulted primarily from an increase in their resources, which enabled increasing numbers of elders to afford independent living. T...

the purpose of this study was to investigate female customers' satisfaction in private and state bodybuilding clubs of tehran with a descriptive method. the sample of this study consisted of the customers of private and state bodybuilding clubs in tehran who had at least 6 months of sport participation. based on systematic sampling, 5 regions of tehran were selected. then, 25 private and 2...

the aim of this study was to determine the situation of organizational citizenship behavior (ocb) in the sport organization of tehran municipality. the method of the study was descriptive-analytic and it was conducted as a field study. the population consisted of sport experts of this organization (n=90). the sample equaled the population. netmeyer's (1997) questionnaire of organizational ...

OBJECTIVES To determine whether animal-assisted therapy is effective in the rehabilitation of middle-aged schizophrenic patients living in a social institution. DESIGN A before and after study with nine-month treatment period. SETTING Social institute for psychiatric patients. SUBJECTS Seven schizophrenic patients living in the social institute. INTERVENTIONS Weekly sessions of animal-a...

We study the sample complexity of learning neural networks, by providing new bounds on their Rademacher complexity assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth, and under some additional assumptions, are fully independent of the network size (both depth and width). These results ...