We can view a probability distribution in two ways. The basic view is the Probability function, which specifies the probability that the random variable takes on a specific value: P(X=x) is the probability that a random variable X takes on a specific value x. (Note that capital X represents the random variable and lowercase x represents a specific value that the random variable may take). For a discrete random variable, the shorthand notation for the probability function is p(x) = P (X=x). For continuous random variables, the probability function is denoted f(x) and called the probability density function.
A populace can be either vast or little, contingent upon what you 're examining. When you utilize descriptive statistics, you need to have the whole populace available to you, since descriptive investigation gives you the properties of the populace all in all, such the mean or the supreme deviation. These are called parameters, and with just a little piece of the populace you can 't all of a sudden thought of the parameters. Inferential statistics becomes possibly the most important factor when you don 't approach the whole populace. For example, in the event that you needed to locate the normal of the whole school 's test scores you may think that its incomprehensible for you to do as such with a specific end goal to get the information that you
Roman legal history is framed by two codifications, the Twelve Tables and the Corpus Juris Civilis. Roman law, was effective in the Eastern Roman Empire (331-1453), and is also the basis of our legal system, civil system which most countries apply, from Europe to Latin America. Even English and North American Common law also were influenced by Roman law, particularly in the legal glossary - stare decisis, culpa in contrahendo, pacta sunt servanda. The primary document that all Roman laws were included was the Twelve Tables. This attempt was the earliest of Romans to create a Code of Law and is also the earliest (surviving) piece of literature coming from the Romans.
According to Graziano and Raulin (2007) there are two types of survey designs: Cross-sectional design is a survey which is conducted one time to a sample, resulting data on the measured features as they present at the point of the survey and Longitudinal survey designs is a survey which can be repeated to the same subjects at different times. In a cross-sectional survey, research may be equated to a snapshot of the phenomenon of concern and data are collected at one point in time from a sample selected to describe an approximately greater population (Saris & Gallhofer, 2007). Such a survey can be used not only for the purpose of description, but also for determination of the relationship between variables at the time of the study (Babbie, 2010). In a longitudinal survey, data are collected from the same sample at several different times, with the main purpose being to study changes in the elements over time (McGivern, 2006). A Longitudinal study is inclined to being costly and fraught with great difficulties as a result of the relatively long periods of time it takes to conduct (Saris & Gallhofer, 2007).
In this paper the statistical model used is analysis of variance (ANOVA). Analysis of Variance (ANOVA) is a commonly statistical method that are often used in order to analyze the result of the model related to real world system. According to Burke (2001), Analysis of variance ( ANOVA) is used to compare and analyze
Having the CronBach’s Alpha up to the required level, the analysis has been done according to the methodology. 4.3 Descriptive statistics. In this research descriptive statistics has been used to describe, show and summarize raw data in a meaningful way. Descriptive statistics are very crucial since presenting raw data are hard to visualize. In this study the author has used different type of methods to summarize data such as tabulated description (tables) and statistical commentary (discussion of the results).
For each of those comparative methods there are strengths, weaknesses and solutions for those weaknesses. The first method of comparison I will discuss is the method of comparing many countries. This method is also called ‘large-n’ comparison, where n stands for the number of countries. It’s very suitable with qualitative analysis of aggregate data. The presence of statistical control makes this method most like an experimental design, which has control groups and treatment groups (Landman & Carvalho, 2016).
In this report I am going to be comparing and contrasting quantitative and qualitative methods of research. Quantitative data is data that is generally focused on numbers for example methods like questionnaires and official statistics. Qualitative data is data that is a lot more in depth for example interviews and observations. I am going to be comparing questionnaires with interviews and official statistics with observations. Questionnaires are used to collect quantitative data as the results can be expressed with numbers.
This choice can be through use of probability-based methods, where the selection is done by some "mechanical” procedure, involving lists of random numbers, or the equivalent. This class of sampling is known as probability sampling method Alternatively, the choice may be made by other methods, invoking some elements of judgment. Methods involving judgment are referred to as non-probability selection. . Simple random sampling Simple random sampling is the fundamental sampling technique where a group of subjects, are being selected, from a larger group , known a population.
Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond
What is the 68-95-99.7 rule for normal distribution and how is it relevant? 2. What is the purpose of SEM and when is it applied? While researching this concept, my understanding is that SEM is used to measure the accuracy with which a sample represents a population, is this interpretation correct? 3.
What is meant by exploratory data analysis? Exploratory data analysis is a way of examining data by using statistical tools and ideas in order to describe main features. 4. What is the difference between a categorical variable and a quantitative variable? A categorical variable places an individual into one of several groups or categories.