STATISTICAL ANALYSIS

Statistical Analysis with Independent Sampling

Writer's Name

Writer's University

Statistical Analysis with Independent Sampling

Introduction

Research proposal simulation is imperative prior the research process. This paper includes the computation of the descriptive statistics and percentile ranks, with and without the assumption of normal distribution. Moreover, a comprehensive interpretation and analysis of the statistics is presented. All the calculation is done with the help of SSPS. The purpose of the whole exercise is to determine the most cost effective in-service training programs for Green Valley District Schools. The approach of independent sampling is applied in this research. Independent samples are the one taken from groups of the population that have no correlation between one another. The purpose for extrapolating data from such sources is to conclude an unbiased interpretation without any factors affecting the sample size or population.

Descriptive Statistics on the Anxiety Scores

It is imperative to understand the concept of descriptive statistics before proceeding towards the calculation and interpretation. Descriptive statistics refers to a set of brief descriptive coefficients. They abridge the given data of the sample size. They help in measuring and indicating the central tendency, variability and dispersion in the data. These coefficients include the mean, mode, skewness, median, standard deviation, kurtosis and the extremes of variance. The statistics facilitate the analytical analysis with the help of historical trend of returns

Skewness, Mean, Standard deviation and Kurtosis

Skewness refers to the extent to which a distribution of scores deviates from perfect symmetry. Positively skewed distributions, as in this case, occur when majority of the scores falls below the rank, at the low-end of the distribution (Lomax, 2007). Kurtosis may be defined as the peakedness of the distribution. In other words, it is a statistical tool that measures the distribution of data around its mean. A high kurtosis means even distribution, whereas, a low kurtosis refers to distribution concentrated in the centre (Rubin, 2010). Standard deviation is a statistical tool of measuring the historical volatility. It computes and indicates the measure of dispersion of data from its mean. Higher the deviation, larger is the level if dispersion.

Anxiety Scores

N

Valid

15

Missing

0

Mean

32.27

Std. Deviation

23.478

Skewness

.416

Std. Error of Skewness

.580

Kurtosis

-1.124

Std. Error of Kurtosis

1.121

Range

73

Minimum

5

Maximum

78

Percentiles

12

5.92

27

10.64

38

16.16

73

49.04

88

61.44

The calculation of the descriptive statistics is indicated in the above table. It can be verified from this table that the skewness of the distribution is 0.416; the standard deviation is 23.478; the mean is 32.27; and the kurtosis of the distribution of anxiety scores is -1.124.

Percentile Ranks on the Anxiety Scores ...