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Chi-Square Analysis
Chi square analysis is a way of comparing categorical responses from two or more different groups (Ryan & Eck, Unk.). This comparison can help reveal whether there is a relationship between the two different groups, and also whether real-world results are in line with anticipated results. Chi square analysis is what is known as a nonparametic test. "Parametric and nonparametric statistical procedures test hypotheses involving different assumptions. Parametric statistics test hypotheses based on the assumption that the samples come from populations that are normally distributed. Also, parametric statistical tests assume that there is homogeneity of variance (variances within groups are the same). The level of measurement for parametric tests is assumed to be interval or at least ordinal. Nonparametric statistical procedures test hypotheses that do not require normal distribution or variance assumptions about the populations from which the samples were drawn and are designed for ordinal or nominal data" (Key, 1997). In other words, chi square analysis permits one to examine data even outside of an expected normal curve distribution. Furthermore,...
It makes sense that heavy down jackets would sell more in cold weather areas than in warm weather areas. However, lightweight layering pieces might do equally well in both locations. A national chain clothing store could compare sales of lightweight longsleeve shirts between geographic areas to determine if geographic area impacts the sales of those shirts. This would tell the chain which locations should stock those shirts, and which locations should not. Moreover, it would provide more information for the company than just overall sales numbers, because it would allow for the comparison of more than one factor at a time.
Inappropriate Use of Statistics
There are a number of ways that statistics can be used inappropriately. Though not an exhaustive list, some of the ways that statistics can be misused is by: failing to provide a context for the statistics, selective use or omission of statistics, using misleading charts or graphics, using selective surveys, confusing or…
References
Bolton, P. (2010). Statistical literacy guide: How to spot spin and inappropriate use of statistics.
Retrieved April 11, 2013 from GetStats website: http://www.getstats.org.uk/wp-content/uploads/2012/02/How-to-spot-error.pdf
Key, J. (1997). Module S7- chi square. Retrieved April 11, 2013 from the Oklahoma State
University website: http://www.okstate.edu/ag/agedcm4h/academic/aged5980a/5980/newpage28.htm
website: http://www.truthpizza.org/logic/stats.htm
Ryan, J. & Eck, D. (Unk). The chi square statistic. Retrieved April 11, 2013 from Hobart and William Smith Colleges Math website: http://math.hws.edu/javamath/ryan/ChiSquare.html
Chi-Square Test Study The focus of the research is to use the Chi-Square analysis to test whether the use of marijuana is less dangerous than alcohol. Over the years, there is high number of related deaths linked to alcohol in the United States, and comparably, there are no related deaths linked to the Marijuana use. A report from Center for Disease Control and Prevention (2013) reveals there is no category of
Essentially, Pearson's formula translates qualitative data from a set of observations into a single number. Probability tables with corresponding numbers, with variances built in for different levels of significance and different degrees of freedom (the number of available data points used for the estimation/prediction of other data, the calculation of which in Chi Square analysis is provided for by another straightforward equation), provide the probability of dependence for any given
75 The standard value of 27.75 represents the distance of each score or frequency of representation of each employment category to the average or mean score or frequency for the distribution (i.e., employment categories. Looking at the gender-commuting relationship, a possible hypotheses that can be developed from these variables are the following: Ho: There is no significant relationship between commuting and gender. H1: There is a significant relationship between commuting and gender. SPSS results showed
23343849 73 0.35009171 35-54 88.40378549 82 0.46387684 55+ 81.36277603 93 1.66445872 2 = 11.39 This value does exceed the critical ?2 value for df = 2 at ? = 0.05. Therefore, we can assume that one of the observed values is significantly different from the expected value for that group. Without post-hoc pairwise tests it is impossible to say exactly which group is different. We can make an educated guess, however, that the proportion of 55+ shoppers in store a is statistically
GWA (Genome-Wide Association ) Study Analysis for OCD The OCD (Obsessive Compulsive Disorder) is a type of mental disorder that makes people doing the same thing repeatedly. In other words, the OCD is characterized by unreasonable obsessive fear and thoughts that lead to repetitive behaviors. People suffering from OCD are unable to control their activities and thoughts. While the cause of OCD is unknown, however, the risk factors include stress and
Nonparametric Tests Many interesting questions related to students are categorical. For instance, there is considerable interest in the different enrollment patterns of male and female students in the following majors: Science, technology, engineering, and mathematics (STEM). While the literature does provide robust data for the numbers of students enrolled in these majors, intriguing questions remain that may be better suited to more qualitative data collection. For instance, a research question is: