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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.

1. Descriptive research is considered to identify the fundamental characteristics of the variables involved in the dataset.

2. Correlation research is commonly used to study relationships between more than two variables provided in the data.

3. 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.

4. 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.

Since quantitative assignments are based on numeracy, most students are nervous while listening to the word "mathematics". Alternatively, the student who makes them well in mathematics also makes the subject problem. There are many techniques, formulas and more tools to work well in solving quantitative assignments. Therefore, there are big problems which require students to help complete the quantitative assignment. We are here to provide Assignment support for online quantitative methods to the needy students. Our experienced experts are well molded and there are high degree qualifications in this field which can easily solve the problems of one of the hardest quantitative methods. Our specialists will solve your quantitative assignment on your half and you can understand the solution by adding each line for each step that you can clearly clarify each step.

We provide quality solutions for the following comprehensive list of topics:

Data analysis, sampling and charts

1. Sampling methods: random, stratified, cluster, etc.

2. Histogram- This is a graphical display of data using different heights of time.

3. 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'.

4. 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.

5. 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.

Central tendency and dispersion measures

1. 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.

2. Standard Deviation - Standard deviation is considered as the measure of stretching from numbers. The symbol of standard deviation is σ.

3. 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.

4. 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.

5. 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.

6. 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.

Regression analysis

1. Coefficient of Correlation - Correlation coefficient is an equation used to determine the strength of relations between two variables.

2. Coefficient of Assessment

3. Multiple Regressions

4. Logistic regression

Return

1. Sample regression

2. Phased Regression

3. Multiple Regressions

4. Polynomial regression

Probability distributions

1. Binomial distribution

2. Poison distribution

3. Exponential distribution

4. Standard Normal

Hypothesis testing

1. Z-test, two independent samples

2. T-test, two independent samples, added samples

3. F-Test

4. Non-parametric square test

5. Pool variations

6. Unequal variations

Linear programming

1. Linear optimization

2. Sensitivity analysis

3. Non-linear programming

4. Simplex analysis

5. Integer programming

Chance

1. Independent Events

2. Mutual exclusive events

3. Not mutually exclusive events

4. Confidential possibility

5. Inverted probability

6. Randomness

Chi-Square

1. Chie-square Test for the Fit's Fine

2. Chi-square test for the well-being of fit for mediocrity

Confidence interval

1. The confidence interval for the population is known, it is known

2. Meaning confidence interval for population, unknown

3. Sample Sizing

ANOVA

1. One way analysis of variation

2. Analysis of two types of difference

3. Block Analysis of Variation Random

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