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quantitative method assignment help
Generally, two types of data are available such as Quantitative and Qualitative data. The Qualitative data is a definite quantity which can be described as a form of natural language description. An example is a very common solution to understand regarding any kinds of problem, therefore for an example, Gender can be a definite variable which has naturally two groups- male and female. Thus in qualitative data, there has no mathematical solution to exit. Alternatively, Quantitative data is a numerical measurement which is basically displayed as numbers. Such as the number of the books of a library, marks of students etc. The quantitative methods are considered as research techniques which are highly used for quantitative data presentation and formulation. The outcomes of these quantitative methods are commonly in terms of graphs or tables using the statistics such as percentages, frequencies, and measures of dispersion, measures of average and more.
Four types of quantitative research design such as Descriptive, Correlation, Quasi-Experimental or Causal-Comparative, and Experimental Research are available.
1. The descriptive research is considered for identifying fundamental characteristics of the variables which are included in the dataset.
2. The correlation research is generally used to study the relationship between two and more variables provided in the data.
3. The quasi-experimental research comprises in choosing the cause-effect relationships between the variables. It is commonly used to check the effect of one or more independent variables on the response variable.
4. The experimental research is called as true experimentation because it comprises applying the method to define the cause-effect relationship among a set of variables.
As the quantitative assignments are based on numerical therefore most of the student are get panic when they hear the word “Mathematics”. Alternatively, the students who are well in mathematics for them also the subject creates the problem. There have numerous technique, formulas and more tools to accomplish well in solving the quantitative assignments. Hence there have big issues that students require help for completing quantitative assignments. We are here to offer online quantitative methods assignment help to the needy students. Our experienced experts are well molded and have high degree qualifications in this field who can solve every single toughest quantitative method problems effortlessly. Our experts will solve your quantitative assignment on half of you and clarify every single step very precisely that you can understand the solution by connecting every line for each problem.
We provide the quality solutions for the following comprehensive list of topics:
Data Analysis, Sampling and Charts
a) Sampling Methods: Random, Stratified, Cluster, etc.
b) Histograms- It is graphical display of data using different heights of bars.
c) Stem and Leaf- It is denoted as a special table where every data is fragmented into ‘stem’ (the first digits) and ‘leaf’ (the last digits). For an instance such as 18 where 1 will be considered as ‘stem’ and 8 for ‘leaf’ as well.
d) Box-Plot- A boxplot can be determined as graphical representation of statistical measurement such as, lower and upper quartiles, median, minimum and maximum data values.
e) Normality Tests- A normality test is a statistical procedure that is usually used to define if an example of data fits a standard normal distribution. A normality test can be accomplished graphically and mathematically.
Measures of Central Tendency and Dispersion
a) Mean, Median, Mode- In statistics, a measure of central tendency is a vital or representative value for the probability distribution. It may also be known as a center of the distribution. The supreme common measures of central tendency are the arithmetic mean, the median and the mode.
b) Standard Deviation-The Standard Deviation is considered as the measurement of spread out of numbers. The symbol of standard deviation is σ.
c) Standard Error of the Mean- A standard error is often known as sampling distribution’s standard deviation of the statistic. Standard error is a statistical word that processes the exactness with that particular sample which signifies a population. Describing in in statistical way, a sample mean differs from the definite mean of a population. This deviation is called as the standard error.
d) Law of Large Numbers- The law of large numbers is the source of probability according to which the frequencies of measures with the same possibility of event. As the quantity of experimentations upturns, the exact ratio of results will join on the theoretical, or predictable, ratio of consequences.
e) Central Limit Theorem- In probability theory, the central limit theorem is a theorem that starts the normal distribution as the distribution. The central limit theorem clarifies that why the normal distribution rises so commonly and why it is normally an outstanding estimate for the mean of an assemblage of data.
f) Normal Distribution- A normal distribution, often can be called as the bell curve, is a distribution that occurs naturally in many situations. Normal distribution curves are at times designed with a histogram inside the curve. The graphs are frequently used in mathematics, statistics and company data analytics.
Regression Analysis
1) Coefficient of Correlation- The correlation coefficient is an equation that is used to determine the strength of the relationship between two variables.
2) Coefficient of Determination
3) Multiple Regression
4) Logistic Regression
Regression
1) Sample Regression
2) Stepwise Regression
3) Multiple Regression
4) Polynomial Regression
Probability Distributions
1) Binomial Distribution
2) Poisson Distribution
3) Exponential Distribution
4) Standard Normal
Hypothesis Testing
1) Z-test, two independent samples
2) t-test, two independent samples, paired samples
3) F-test
4) Non-parametric Chi Square test
5) Pooled variances
6) Unequal variances
Linear Programming
1) Linear Optimization
2) Sensitivity Analysis
3) Non-Linear Programming
4) Simplex analysis
5) Integer Programming
Probability
1) Independent Events
2) Mutually Exclusive Events
3) Not Mutually Exclusive Events
4) Confidential Probability
5) Inverse Probability
6) Randomness
Chi- Square
1) Chi-square test for goodness of fit
2) Chi- square test for goodness of fit for normality
Confidence Intervals
1) Confidence Intervals for population mean, known
2) Confidence Intervals for population mean, unknown
3) Sample size determination
ANOVA
1) One way analysis of variance
2) Two Way analysis of variance
3) Randomize block analysis of variance
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