scores for the two groups. From the coffee drinking habits research, we could obtain this statistic of how males and females differ significantly in terms of their coffee consumption during
on the measurement scale of the tested variables and the normality distribution of data. The parametric test (e.g. t-test and one–way ANOVA) is suitable for the ratio or the interval scale and for the data which is normally distributed. In contract, the non-parametric tests (e.g. Mann-Whitny and Kruskal-Wallis tests) are suitable for the nominal and ordinal scales and for the data which is normally or non-normally distributed (Field, 2009; Kleinbaum, et al. 2008). As mentioned earlier, the normality
Football Statistics Project Introduction ------------ I have chosen to base my project on football statistics because they are both readily available and interesting enough for deep analysis. As a starting point I decided to look at the generally accepted theory of 'Home Advantage'. Home advantage, or the tendency for the home team to do better than they would away, could have several causes. It could be partly psychological - the home team would almost always have the majority of
http://coglab.wadsworth.com/experiments/Stroop/ Neuropsychological Model of the Stroop Effect, http://www.uwm.edu/~neuropsy/Strpmast.html Neuroscience for Kids - The Stroop Effect, http://faculty.washington.edu/chudler/words.html Parametric Assumptions, http://www.sgcorp.com/normality_tests.htm The Stroop Effect - Attention and Memory, http://www.cgl.uwaterloo.ca/~bgbauer/chapters/stroop.html The t-test, http://trochim.human.edu/kb/stat_t.htm
Synergy comes from the Greek word sunergos which means “working together” (Morris, 1981). Synergy results from two or more people working together, sharing ideas with open minds and mutual respect, and managing conflict in ways that empower all members. This is the advantage of working in a group: the whole group is greater than the sum of its parts. (Harris and Sherblom, 2005, p.11) Synergy consists of two aspects: problem solving and interpersonal relations. (Adult Learners Guide, p.13) An in-class
Proseminar In Partial Fulfillment of the Degree of Master of Science in Mathematics and Statistics University of West Florida April 2014 Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution
males and females are influence by gender, a nonparametric was used for the following reasons: The assumptions of a parametric test, primarily the assumptions of having a scale dependent variable, were not met, and there are two nominal dependent variables (gender and conformity). For these reasons, the Chi-Square Test for independence was chosen for this research. The Chi-Square statistics are shown: X2(3, N = 60) = 10.543, p < 0.05. Reject the null hypotheses; it appears that conformity rates between
Three hundred and forty 5 year old children from 18 state primary schools in Lambeth were examined as part of the BASCD oral epidemiological programme in 2008. The collection of the data was undertaken between February and June 2008. The children were recruited from local state schools using a two stage stratified sampling strategy. The dental examination was undertaken by trained and calibrated examiners and recorded the children’s caries experience, oral hygiene levels and presence of acute infection
Wolf packs are very diverse in species and intricate in behavior. Those things are what makes them interesting enough to create a research paper on them. This research paper will also be mentioning these wonderful creatures’ lifestyle, behavior, and how they mature. Wolves are some of the most territorial canines in the animal kingdom, so imagine how they’d act within a pack! Wolves can live in moderately small packs to relatively large packs, but some wolves live alone as loners. In a wolf pack
Data Envelopment Analysis (DEA) Data Envelopment Analysis calculates the best-practice frontier for a given sample using piecewise linear programming. It then indicates the relative inefficiency of other units by measuring the distance between these units and the best practice frontier. These models can be input oriented (seeking to minimize inputs while retaining a constant output) or output oriented (seeking to maximize outputs while holding inputs constant). In this instance outputs would be factors
Data Envelopment Analysis in University Rankings 1.0 Introduction Data Envelopment Analysis (DEA) is a means of producing a performance measure where sets of organisational decision making units (DMUs) have multiple inputs and outputs. (Dyson, 2000, OR Insight Vol13 Issue 4). DEA considers each unit in turn through linear programming and selects the most favourable weights for it. In this investigation the DMUs are universities, and the outputs are the different categories, ie
obstacles in achieving it. Group consensus was swiftly attained in determining our main purpose, to “stay alive”, and the presumption that dehydration was our leading hindrance in reaching this goal. Discussion consisted of an encouraging, supportive, and non-judgmental atmosphere with active listening and members providing and requesting clarification. These group dynamics quickly nurtured a set of norms which were continuous throughout the Desert Survival Group Exercise. Although these norms were not explicit
Methodology Purposes and Approaches of the Bootstrap The bootstrap procedure can be used for inferential or descriptive purposes (Thompson, 1999). When used inferentially, the bootstrap estimates a sampling distribution from which a p-calculated or test statistic can be derived (Thompson, 1999). In inferential bootstrapping, the focus is on the ... ... middle of paper ... ... measures that can be analyzed mathematically, such as the mean or standard deviation, and move to more complex statistical questions
do inferential statistics allow you to infer? Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer
Burns and Grove (2011) add that this test provides an examination of frequencies for two nominal scaled variables in a cross-tabulated form to determine whether the variables have a non-monotonic relationship. The Chi-square test (2) examines the relationships between two variables at nominal and discrete level. The test compares the actual frequencies with the expected results or how strongly they match or differ from the expected
Study of Literature for related work is the most important step in software development process. Before designing and developing the tool it is necessary to determine the time factor, the economy and company strength. Considering the importance of software reliability in software engineering, its prediction becomes a very fundamental issue. Machine learning and soft computing techniques have been leading the statistical techniques in last two decades as far as their applications to software engineering
in med- ical images. Images with low-contrast do not have well de- fined homogeneous regions, performance may be degraded in these scenarios since the method assumes homogeneous in- tensities in the foreground and background. Level-sets being non-parametric, include a regularization term such as penalty on curve/length/surface area or curvature. These regulariza- tion terms do not contain any information about the shape of the region of interest. The Chan-Vese algorithm may be slow for some applications
Two of the tests used parametric statistics while the other two produced analyses from non-parametric methods. The study by Ibraim, Eid, & Moawd (2012) reported improvement in knee extensors muscle strength combine with the decrease spasticity in the same muscle group, increase in walking speed, and improvement
The researchers took a sample from a population to apply results of the study to that population for better outcomes post total hip replacement. The parametric aspect is that the researchers utilized interval/ratio levels of measurement to rate the independent variables with the participants. These levels of measurement assisted researchers to gage advancement towards activities of daily living after a
This paper is an illustration of quantitative data analysis using the IBM SPSS Statistics software. It does not provide the details of technical skill to operate SPSS but focuses on developing a set of decisions and actions in order to set up, describe, manipulate and analyse data in the specific context of the study of Jackson and Mullarkey (2000). In order to fulfil the task, this paper illustrates a step-by-step of actions that were made on the data. It also gives the insight into the determination