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Statistical data analysis is an approach which uses quantitative data to investigate trends, patterns, and relationship in order to quantify and validate data, and establish some sort of meaningful analysis. Such quantitative data usually includes descriptive data like observational data and survey data. Basically this analysis helps in drawing relevant conclusion from any raw and unstructured data. There are primarily two types of data in statistical data analysis- continuous data and discrete data. The data in this analysis consists of variable which are sometimes univariate or multivariate. In this analysis there is a major task which comprises of statistical inference. And this statistical inference mostly has two parts namely estimation and tests of hypothesis. Cross-sectional and time-series are also important under this analysis. Statistical data analysis is used in business applications and is pivotal in business intelligence (BI) as it needs to operate large data volumes. This analysis can also be applied in the field of statistical analysis of data analytics in big data, deep learning, financial and economic analysis, market research and machine learning.
Descriptive Statistics: Raw data on its own does not give a clear meaning to the statisticians or anyone handling it. The sheer amount of data can be overwhelming to interpret and it is through descriptive statistical analysis those data can be summarized and simplified into a practical form. It helps in creating a basic visual description of data in the form of charts and graphs as well as other visuals to make people understand it better. There are different types of tools for descriptive statistics out of which the most popular are: mean, median, mode, and standard deviation.
Mean: Through mean the average of the dataset can be calculated. It can be used for both discrete data and continuous data.
Median: The median is the middle value of the data set that has been arranged according to a numerical quantity or value.
Mode: The value that occurs the most often is the mode in a dataset.
Standard Deviation: Relative to the mean in a dataset, standard deviation estimates or measures the degree of spread of values. Low standard deviation indicates that the values are clustered around the mean; however, high standard deviation indicates that the values are more dispersed from the mean.
The inferential statistics is used when the inspection of an entire population is not possible leading to a sample being extracted from that population which will then be used for testing of hypothesis or estimation. Inferences are extracted from these sample data by applying probabilities and making generalizations which eventually helps in drawing conclusions about the rest of the dataset. However, it is important to use random and unbiased sampling to come to a valid statistical inference of a sample data. The most common tools of inferential statistics are hypothesis tests, confidence intervals, regression analysis, T-test, and ANOVA.
Hypothesis Test: It evaluates two mutually exclusive statements known as null hypothesis and the alternative hypothesis to determine which statement out of the two represent the sample data the best.
Confidence Intervals: It displays a range of value that has been estimated from a sample as probability to accurately reflect the population.
Regression Analysis: It is a statistical method through which relationship between a response variable and one or more predictor variable are estimated in order to determine which of these variables have impact on a topic of interest.
T-test: It is used to compare the means of two groups and if the differences between them are significant or not.
ANOVA: Analysis of Variance (ANOVA) is a statistical method used for testing three or more population means for any variance and if they remain constant or not when implemented in different groups that are independent of each other.
Linear Regression: This analysis is used for predicting the value of a dependent variable based on the value of an independent variable. It seeks to represent the relationship between these two variables by fitting a linear equation to the observed data. There are mainly two types of linear regression- simple linear regression and multiple linear regression.
Classification: This a data mining technique through which specific categories are assigned to a collection of data for making thorough predictions and analysis. The two types of classification techniques are logistic regression and discriminant analysis.
Resampling Methods: This is a procedure where extracting or repeating samplings from a given sample or population are used. There are two main types of resampling method which are bootstrapping and cross-validation.
Tree-based Method: This method is most commonly used in both classification and regression problems. Here the predictor space is stratified or segmented according to a number of manageable sections which are also known as decision-tree methods. Bagging and boosting are the two methods which grow multiple trees for precise forecasting.
Unsupervised Learning: When the groups or categories in a dataset are neither classified nor labeled, unsupervised learning is used to identify their patterns. The common approaches of unsupervised learning are clustering and the association rules. Principal Component Analysis, hierarchical clustering, and k-means clustering are some unsupervised learning algorithms.
SAS: It is a statistical analysis platform offering options to use either the GUI or to develop scripts for more advanced analysis. It is a premium solution widely used in healthcare, business, and human behavior research. It enables us to carry out advanced analysis and produce graphs and charts that are worthy of publication.
SPSS: It is an extensively used statistics software package for statistical research. It enables the researchers to compile descriptive statistics, parametric and non-parametric analysis, and graphical depictions of results through the graphical user interface. It also comes with an option to create scripts, to perform highly advanced statistical processing, and to automate analysis. Follow this link for SPSS Homework Help.
Minitab: It offers a wide range of both fairly advanced and basic statistical tools for data analysis. It can be used to carry out highly complex analysis as both the scripted commands and commands through GUI can be executed.
GraphPad Prism: It is a premium software that is employed to carry out statistics related to biology. It offers a wide range of functions that can be used across different fields.
R Language: R is a free statistical software that is employed widely across human behavior research and other fields. It comes with toolboxes that have a wide range of applications and can simplify different aspects of data processing. It has a steep learning curve, requiring a certain extent of coding.
There are several key performance indicators (KPIs) every company has which helps in judging its comprehensive performance. For this purpose statistical data analysis serves as a principal strategy to find accurate metrics. This type of statistical analysis helps in judging the employees and their performances through unbiased numerical standards instead of opinions which can be biased.
Through statistical data analysis the objective value of a company can be assessed and comparing of common metrics such as net profit margin and sales revenue can be done which helps in analyzing the company’s performance with the other competitors.
Predictive analytics is one of the most important applications of statistical data analysis as it helps in using past numerical data to predict future outcomes in areas where adjustments are needed to improve performance.
The assessment of marketing and sales can be done accurately through statistical data analysis as it can easily measure sales data that are based on products, individual salesperson, and timeframes.
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