Please Help! We are interested in knowing if the following frequency distributio
ID: 1187753 • Letter: P
Question
Please Help!
We are interested in knowing if the following frequency distribution is normally distributed. The mean and standard deviation of the data is 600 and 160. To test the hypothesis that the frequency distribution is normally distributed, what is the degrees of freedom for the chi-square statistic? To test the hypothesis that the frequency distribution is normally distributed, what is the value of the chi-square critical value at the .05 level of significance? To test the hypothesis that the frequency distribution is normally distributed, the value of the chi-square test statistic is 12.19. What is the decision regarding the null hypothesis tested at the .05 level of significance? To test the hypothesis that the frequency distribution is normally distributed, we estimate two population parameters. What are they?Explanation / Answer
Definition of 'Nonparametric Method' A method commonly used in statistics to model and analyze ordinal or nominal data with small sample sizes. Unlike parametric models, nonparametric models do not require the modeler to make any assumptions about the distribution of the population, and so are sometimes referred to as a distribution-free method. Typically, this method will be used when the data has an unknown distribution, is non-normal, or has a sample size so small that the central limit theorem can't be applied to assume the distribution...........................................................................................................A statistical method is called non-parametric if it makes no assumption on the population distribution or sample size. This is in contrast with most parametric methods in elementary statistics that assume the data is quantitative, the population has a normal distribution and the sample size is sufficiently large. In general, conclusions drawn from non-parametric methods are not as powerful as the parametric ones. However, as non-parametric methods make fewer assumptions, they are more flexible, more robust, and applicable to non-quantitative data.