Generally, there are two types of data available such as quantitative and qualifying data. Eligibility data is a definite quantity that can be described as natural language description. An example is a very common solution to understanding any type of problem; so for example, gender may have a definite variable in which naturally two groups- male and female. Thus, in qualitative data, there is no mathematical solution to exit. Alternatively, quantitative data is a numerical measurement that is originally displayed as numbers. Such as library books, students' scores, etc.
Quantitative methods are considered as research techniques Quantitative data is highly used for presentation and formulation. The results of these quantitative methods are usually in reference to the graphs or tables using figures of percentages, frequencies, and dispersion measures, average measures and more.
Four types of quantitative research designs such as descriptive, correlation, semi-experimental or skill-comparative, and experimental research are available.
Descriptive research is considered to identify the fundamental characteristics of the variables involved in the dataset.
Correlation research is commonly used to study relationships between more than two variables provided in the data.
Semi-experimental research involves selecting causal relationships between variables. It is used to check the effect of one or more independent variables on the reaction variable.
Experimental research is known as the correct experiment because it involves applying the method to define a cause-effect relationship between a set of variables.
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Data analysis, sampling and charts
Sampling methods: random, stratified, cluster, etc.
Histogram- This is a graphical display of data using different heights of time.
Stem and Leaf- It is displayed as a special table, where every data is divided into 'stem' (first digits) and 'leaf' (final digits). For example 18 where 1 would be considered for 'stem' and 8 'leaves'.
Box-plot- A box plot can be determined as a graphical representation of statistical measurements such as lower and upper quartile, average, minimum and maximum data value.
General testing - A commonality test is a statistical process that is commonly used to define whether an example of data fits standard normal distribution. A generality test can be completed graphically and mathematically.
A measure of central tendency in mean, mid, mode - figures is an important or representative value Probability Distribution It can also be known as the center of distribution. The highest general measures of central tendency are arithmetical mean, average and mode.
Standard Deviation - Standard deviation is considered as the measure of stretching from numbers. The symbol of standard deviation is σ.
Standard error of mean- A standard error is often known as standard deviation of sample distribution of data. Standard error is a statistical term that processes accuracy with that particular sample which represents one Population Describing statistically, a sample is different from the fixed meaning of the population. This deviation is called standard error.
Law of large numbers- Law of a large number is the source of probability, according to which the frequencies of measures with the same probability of the incident. As the amount of experiments increases, the exact ratio of the result will be in the proportion of theoretical, or projected, results.
Central Boundary Theorem - In the probability theory, the central boundary theorem is a theorem that starts normal distribution in the form of distribution. The central boundary theorem explains why general distribution grows normally and why it is generally an excellent estimate for a combination of data.
Normal distribution - A normal distribution, can often be said as a bell curve, a distribution occurs Naturally in many situations. Normal distribution curves are sometimes designed with histograms inside the curve. The graph is often used in mathematics, statistics and company data analysis.
Coefficient of Correlation - Correlation coefficient is an equation used to determine the strength of relations between two variables.
Coefficient of Assessment
Z-test, two independent samples
T-test, two independent samples, added samples
Non-parametric square test
Mutual exclusive events
Not mutually exclusive events
One way analysis of variation
Analysis of two types of difference
Block Analysis of Variation Random
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