ASSIGNMENT 2

Collecting and using Business Data

Collecting and using Business Data

Introduction

The chi-square test is a nonparametric test of the statistical significance of a relation between two nominal or ordinal variables. Because a chi-square analyzes grosser data than do parametric tests such as t tests and analyses of variance (ANOVAs), the chi-square test can report only whether groups in a sample are significantly different in some measured attribute or behavior; it does not allow one to generalize from the sample to the population from which it was drawn. Nonetheless, the less demanding nature of chi-square about the data it will accept could be used in various research patterns. This entry focuses on the application, requirements, computation, and interpretation of the chi-square test, along with its role in determining associations among variables.

In this paper we will test two hypotheses on the basis of chi square test, to see whether there is a systematic relationship between the specified variables. Though one can apply the chi-square test to a single variable and judge whether the frequencies for each category are equal (or as expected), a chi-square is applied most commonly to frequency outcome accounted in bivariate tables, and recognizing bivariate tables is crucial to interpreting the outcomes of a chi-square test. Bivariate tabular analysis (sometimes called crossbreak analysis) is used to understand the relationship (if any) between two variables.

The independent variable is the worth or feature that the researcher hypothesizes assists to forecast or enlighten some other characteristic or behavior. Researchers manage the independent variable (in this example, by sampling males and females) and draw and calculate the dependent variable to test their hypothesis that there is some association among the two variables.

Discussion

The statistical importance of chi-square test is a series of mathematical formulas through which the actual observed frequencies of the two variables measured with frequencies in a sample can be compared; one may anticipate if there was no connection between those variables at all. That is, chi-square assesses whether the actual results are different enough from the null hypothesis to achieve a definite probability that this is the result of sampling error, randomness, or a combination. We have considered the following two hypotheses:

Contingency Tables

The investigation of relationships between groups (gender, grades, distance, staff social club or travelling of a person) is a very common question. The easiest way to illustrate these contexts is a cross table (contingency table). For the description of the systematic relationships exist several measures of association, the most famous is the chi-square test. The chi-square test checks whether a trait in two or more samples is identically distributed. The corresponding null hypothesis is: H0: The proportion of each characteristic value is the same in both samples.

Hypothesis 1

Employees with higher grades visits staff social clubs more than lower grade employees.

Hypothesis 2

Male employees live closer to their work than female employees.

Results and Analysis

The table below shows the contingency table for the first hypothesis, it involves two variables i.e. grade and staff social club ...