Purchase Solution

Non-parametric Testing - Data Analysis (interpreting the data)

Not what you're looking for?

Ask Custom Question

Examine the relationship between education and perception of life. Determine whether to reject the null hypothesis that education and perception of life are independent. Summarize my findings. Below is the problem; and attached is the output and bar chart.

Examine the relationship between education and perception of life. Can you reject the null hypothesis that education and perception of life are independent? Make a bar chart that graphically summarizes your findings.

Attachments
Purchase this Solution
Solution Summary

This solution outlines how to solve the given statistics problem.

Solution Preview

** Please see the attached file for the complete solution response **

Answer
The null hypothesis to be tested is that education and perception of life are independent. For this, first we classify the given data in the form of a contingency table on the basis of these two ...

Purchase this Solution
Free BrainMass Quizzes
Measures of Central Tendency

Tests knowledge of the three main measures of central tendency, including some simple calculation questions.

Terms and Definitions for Statistics

This quiz covers basic terms and definitions of statistics.

Measures of Central Tendency

This quiz evaluates the students understanding of the measures of central tendency seen in statistics. This quiz is specifically designed to incorporate the measures of central tendency as they relate to psychological research.

Know Your Statistical Concepts

Each question is a choice-summary multiple choice question that presents you with a statistical concept and then 4 numbered statements. You must decide which (if any) of the numbered statements is/are true as they relate to the statistical concept.

View More Free Quizzes
Related BrainMass Solutions

  • formal hypothesis testing

    The similarity between parametric tests and non parametric tests is that the nonparametric tests also use parameters. Only these parameters are not fixed in advance but are determined from the data.

  • Compare and contrast the characteristics of parametric and non-parametric test methodologies

    Non-parametric statistics are used with nominal data, where the response set can be converted to counts of events, and the measurement scale is ignored. Non-parametric statistics can be used when data are converted to ranks.

  • Epidemiology and Biostatistics

    Examples of statistical data used in quantitative research are multiple regression and analysis of covariance. Non-parametric data is associated more with qualitative testing and has no parameters or has no known specific limit or boundary.

  • Explains the analysis of variance, multivariate statistics, and non-parametric methods

    224738 Explains the analysis of variance, multivariate statistics, and non-parametric methods Company W is testing a sales software. Their salesforce of 500 people is divided into four regions: Northeast, Southeast, Central and West.

  • One-way ANOVA F-test, & Chi-Square test examples

    Q2: Why is a test, which is testing significant differences among means called Analysis of Variance? Q3: Why might a non-parametric test be used, in place of the One-Way ANOVA F-test, for testing the difference among several population means?

  • Hypothesis Testing - Parametric and Nonparametric Statistics

    In other words, non-parametric statistics impose less stringent demands on the data.

  • Data Falling on a Normal Distribution

    Use the non parametric procedure because ordinals do not support use of the means or variance testing. They also are not subject to outliers because they are in specified order.

  • The answer to Nonparametric tests

    With some assumption made about the distribution of the variable (say normality, for example), the variable is fir to be tested parametrically. The t-test, the z-test etc as a few well-known parametric testing methods.

  • Statistics - Nonparametric, Tests

    With some assumption made about the distribution of the variable (say normality, for example), the variable is fit to be tested parametrically. The t-test, the z-test etc are a few well-known parametric testing methods.

View More Related Solutions