Statistics is the branch of science that provides various analytical tools and techniques to process large amounts of data. Put simply, it is the science of collecting, classifying, analyzing, and interpreting and expressing the numerical form of data to make inferences about populations. , from selected sample data that can be used by business professionals to solve their problems.
Therefore, to organize data and predict future trends depending on the information, many organizations rely heavily on statistical analysis.
But exactlystatistical data analysisrefers to the collection, interpretation and presentation of data. It can be addressed when dealing with data to solve complex problems. More specifically, statistical analysis gives meaning to insignificant/irrelevant data or numbers.
The main types of statistical analysis are
Specifically, statistical analysis is the process of consolidating and analyzing different samples of data to uncover patterns or trends and anticipate future events/situations in order to make appropriate decisions.
Statistical analysis has the following types, which depend heavily on the data types.
7 types of statistical analysis
1.Descriptive statistical analysis
Basically, it's all about organizing and summarizing data using numbers and graphs. It provides huge amounts of data for understandable interpretation, even without drawing conclusions or answering hypotheses beyond analysis.
Rather than processing data in its raw form, descriptive statistical analysis allows us to present and interpret data more efficiently through numerical calculations, graphs or tables.
From all the necessary preparatory steps to the completion of the analysis and interpretation,descriptive statistical analysisincludes several processes such as tabulation, a measure of central tendency (mean, median, mode), a measure of spread or variance (range, range, standard deviation), measures of skewness, andtime series analysis.
In the descriptive analysis, the data are summarized in tabular form and managed and presented in the form of tables and graphs for the sum of the data assuming the total population.
Also read |Descriptive Statistics in R
In addition, it helps to extract different characteristics from the data and to summarize and explain the essential characteristics of the data. In addition, no information is obtained about the unobserved/sampled groups.
2.Inferential Statistical Analysis
Inferential statistical analysis is generally used when examining each unit of the population is not feasible, so the information obtained is extrapolated to the entire population.
In simple terms, inferential statistical analysis allows us to test a hypothesis based on a sample of data from which we can draw inferences, applying probabilities and making generalizations about any data, and we can also draw conclusions about future outcomes move beyond the available data. .
Therefore, it is very preferable to draw conclusions and make decisions about the entire population based on sample data. Therefore, this method involves sampling theory, various significance tests, statistical control, etc.
Descriptive statistical analysis vs. inferential
Predictive analytics is implemented to make a prediction about future events or what is likely to happen next based on current and past facts and figures.
in simple words,Predictive analytics uses statistical techniques andMachine Learning Algorithmsto describe the possibility of future outcomes, behaviors and trends based on current and historical data. Techniques widely used in predictive analytics include data mining, data modeling, artificial intelligence, machine learning, etc. to make compelling predictions.
In today's business system, this analysis is used by marketing companies, insurance companies, online service providers,Datengetriebenes Marketingand financial companies, however, any company can benefit by planning for the unpredictable future, how to gain a competitive advantage and reduce the risk associated with an unpredictable future event.
Predictive analytics converge in predicting future events using data and checking the likelihood of various trends in the behavior of the data. Therefore, companies use this approach to get the answer "What can happen?". where the basis for predictions is a probability measure.
Prescriptive analytics analyzes data to determine what to do. It is commonly used in business analysis to determine the best possible course of action for a situation.
While other statistical analyzes can be implemented to generate exclusions, they provide the actual answer. Basically, it focuses on finding the optimal signal for a decision-making process.
Various techniques implemented within the framework of prescriptive analytics are simulation, graph analysis, algorithms, complex event processing, machine learning,recommendation engine, business rules, etc.
However, it is almost related to descriptive and predictive analytics, where descriptive analytics explains data in terms of what happened, predictive analytics anticipates what might happen, and here prescriptive analytics is about making appropriate suggestions between available preferences.
5.Exploratory Data Analysis (EDA)
Exploratory data analysis, or also called EDA, is a counterpart of inferential statistics and is widely used by data scientists. It is usually the first step in the data analysis process, performed before any other statistical analysis technique.
EDA is not itself implemented to predict or generalize, it provides a visualization of the data and helps to get important information about it.
This method focuses entirely on analyzing patterns in the data to identify possible connections. EDA can be contacted to discover unknown associations in data, examine missing data from collected data and gain maximum insights by examining assumptions and hypotheses.
6.root cause analysis
In general, causal analysis helps to understand the reasons why and determine "why" things happen or why things are the way they seem.
For example, in the current business environment, there are many ideas or companies that have failed due to occurrence of some events; In this state, causal analysis identifies the root cause of the failures, or just the main reason why something might be happening.
noIT Industrie, this is used to check the quality control of a specific software, such as why that software failed, whether there was a bug, a data breach, etc., and prevents companies from suffering major setbacks.
We can consider a causal analysis if;
- Identify significant problem areas within the data,
- Investigate and identify the root causes of the problem or failure,
- Understand what happens to a given variable when another variable changes.
Among the above statistical analyses, the mechanistic is the least common type, but it pays off in the processbig data analysisand life sciences. It is implemented to understand and explain how things happen and not how certain things will happen later.
It uses the clear concept of understanding individual changes in variables causing changes in other variables, excluding external influences and considering the assumption that the whole system is influenced by the interaction of its own internal elements.
The basic goals of mechanistic analysis include:
- Understand definitive changes where you might make changes to other variables
- A clear explanation of the occurrence of a past event related to the data, particularly if the specific issue/concern relates to specific activities.
For example in life sciences to study and study how different parts of the virus are affected by changes in medicine.
In addition to the types of statistical analysis mentioned above, it is worth noting here that these statistical treatments, orstatistical data analysis techniques, are highly dependent on how the data is used. Although it depends on the role and needs of a particular study, data and statistical analysis can be used for many purposes, for example, medical professionals can use a variety of statistical analyzes to test a drug's efficacy or potency.
Also, a large amount of available data can reveal a lot that data practitioners want to investigate, so statistical analysis can provide some informative results and some conclusions. In addition, in some cases, statistical analysis may be performed to gather information about people's preferences and habits.
For example user data, on sites like Facebook andInstagram, can be used by analysts to understand user perception, e.g. B. what users do and what motivates them. This information may benefit commercial ads in which you target a specific group of users in order to sell them things. It is also useful for application developers to understand user reactions and habits and make changes to products accordingly.
A deeper understanding of data can expand the myriad possibilities for an organization through the implementation ofBusiness Analyst, an organization can achieve through the study of data, for example, to make predictions, insights or conclusions from data, and this can be done, for example, by statistical analysis;
- Collect and manifest data in the form of charts or tables to show key results,
- Examine meaningful data items/metrics such as mean, variance, skewness, etc.
- Test a hypothesis from multiple experiments,
- Anticipate future predictions based on past data behavior and more.
Therefore, a company can take advantage of statistical analysis in many ways, e.g. B. to identify underperforming sales, identify trends in customer data, conduct financial audits, etc.
Also read |Bayesian statistics
Rather, we saw two main types.”descriptive and inferential statistical analysis” is to be chosen when applying statistical analysis to a business problem; However, other types of statistical analysis are becoming a priority when it comes to meeting other business needs that organizations are looking for, based on intent or full queries.
What are the 7 steps in the statistical process in order? ›
- Step 1: State the Null Hypothesis. ...
- Step 2: State the Alternative Hypothesis. ...
- Step 3: Set. ...
- Step 4: Collect Data. ...
- Step 5: Calculate a test statistic. ...
- Step 6: Construct Acceptance / Rejection regions. ...
- Step 7: Based on steps 5 and 6, draw a conclusion about.
There are two main types of statistical analysis: Descriptive statistics explains and visualizes the data you have, while inferential statistics extrapolates the data you have onto a larger population. Statistical analysis can help companies cut costs and improve workplace efficiency, among other benefits.What are the seven 7 Steps to perform a data analysis? ›
- Define goals. Defining clear goals will help businesses determine the type of data to collect and analyze.
- Integrate tools for data analysis. ...
- Collect the data. ...
- Clean the data. ...
- Analyze the data. ...
- Draw conclusions. ...
- Visualize the data.
The kinds of insights you get from your data depends on the type of analysis you perform. In data analytics and data science, there are four main types of data analysis: Descriptive, diagnostic, predictive, and prescriptive.What are the 8 stages of data analysis? ›
data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...What are six steps of data analysis discuss briefly the main objectives of each step? ›
According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier.What is data analysis explain in detail? ›
Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data, is collected and analyzed to answer questions, test hypotheses, or disprove theories.What are the 10 steps in analyzing data? ›
- Collaborate your needs. ...
- Establish your questions. ...
- Harvest your data. ...
- Set your KPIs. ...
- Omit useless data. ...
- Conduct statistical analysis. ...
- Build a data management roadmap. ...
- Integrate technology.
- STEP 1: DEFINE QUESTIONS & GOALS.
- STEP 2: COLLECT DATA.
- STEP 3: DATA WRANGLING.
- STEP 4: DETERMINE ANALYSIS.
- STEP 5: INTERPRET RESULTS.
What are 7 QC tools explain the content of all 7 tools? ›
These seven basic quality control tools, which introduced by Dr. Ishikawa, are : 1) Check sheets; 2) Graphs (Trend Analysis); 3) Histograms; 4) Pareto charts; 5) Cause-and-effect diagrams; 6) Scatter diagrams; 7) Control charts.What is 7QC tools PDF? ›
Keywords: Seven QC Tools; Check Sheet; Histogram; Pareto Analysis; Fishbone Diagram; Scatter Diagram; Flowcharts, and Control Charts.What are the major types of statistical methods? ›
Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics.What are the steps in SPSS data analysis? ›
- Get your data into IBM SPSS Statistics. ...
- Select a procedure. ...
- Select the variables for the analysis. ...
- Run the procedure and look at the results.
The process of performing certain. calculations and evaluation in order to extract. relevant information from data is called data. analysis.What are the 8 types of data? ›
These include: int, byte, short, long, float, double, boolean, and char.What are the different types of data and describe each type? ›
There are two types of data: Qualitative and Quantitative data, which are further classified into four types data: nominal, ordinal, discrete, and Continuous.What is a database class 7? ›
A database is an organized collection of data which helps us to enter, manage, access, andanalyse a large amount of information quickly and efficiently.What are the 10 data types? ›
- Integer. Integer data types often represent whole numbers in programming. ...
- Character. In coding, alphabet letters denote characters. ...
- Date. This data type stores a calendar date with other programming information. ...
- Floating point (real) ...
- Long. ...
- Short. ...
- String. ...
- Internal data.
- External data.
- Time-stamped data.
- Structured data.
- Unstructured data.
- Open data.
- Big data.
- Genomic data.
What are the four steps of analysis? ›
- Step One: Define. ...
- Step Two: Measure. ...
- Step Three: Analyze. ...
- Step Four: Decide.
Using five levels of analysis (explicit, implicit, theoretical, interpretive, and applicable) addresses this concern by challenging students to comprehend the central ideas of texts, interrogate in terms of social justice, connect concepts to their immediate realities and extrapolate useful ideas to apply to their ...What are the 9 stages of data processing? ›
- Acquisition. First, relevant data sources are identified. ...
- Preparation. Data preparation is itself a sequence of smaller processes. ...
- Integration. ...
- Organization. ...
- Processing. ...
- Visualization. ...
- Storage. ...
- Acquisition and Preparation.
- Step 1: Collection. The collection of raw data is the first step of the data processing cycle. ...
- Step 2: Preparation. ...
- Step 3: Input. ...
- Step 4: Data Processing. ...
- Step 5: Output. ...
- Step 6: Storage.
: a detailed examination of anything complex in order to understand its nature or to determine its essential features : a thorough study.What is the difference of qualitative and quantitative data analysis? ›
Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.What is the first step a data analyst? ›
1. Get a foundational education. If you're new to the world of data analysis, you'll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.What are the 7 rules used to identify an out of control process? ›
Rule of Seven Tests
The tests state that an out of control situation is present if one of the following conditions is true: 1) Seven points in a row above the average, 2) Seven points in a row below the average, 3) Seven points in a row trending up, or 4) Seven points in a row trending down.
The acronym PEMDAS, or the mnemonic "please excuse my dear aunt Sally," are sometimes used to help students remember the basic order of operations, where P = parentheses, E = exponents (and square roots), M = multiplication, D = division, A = addition, and S = subtraction.What is the first statistical process? ›
1. Determine Measurement Method. Statistical Process Control is based on the analysis of data, so the first step is to decide what data to collect. There are two categories of control chart distinguished by the type of data used: Variable or Attribute.
What are the steps of solving a statistical problem? ›
This process typically has four components:
- Ask a Question.
- Collect Appropriate Data.
- Analyze the Data.
- Interpret the Results.
Statistical Inference can be thought of as a process that can be used for testing claims and making estimates. Step 1 (Problem): Ask a question that can be answered with sample data. Step 2 (Plan): Determine what information is needed. Step 3 (Data): Collect sample data that is representative of the population.What are the 5 statistical process? ›
The Statistical Process has five steps: Design the study, Collect the data, Describe the data, Make inferences, Take action.What are the five stages of statistics? ›
A cycle that is used to carry out a statistical investigation. The cycle consists of five stages: Problem, Plan, Data, Analysis, Conclusion.What is the OR rule in statistics? ›
The Or Rule states that we can find the probability of either event A or event B occurring by adding the probability of event A and the probability of event B, as long as both events are mutually exclusive: P(A or B) = P(A) + P(B)Who is the father of statistical? ›
Prasanta Chandra Mahalanobis is also known as the father of Indian Statistics. He was a physicist by training, a statistician by instinct and a planner by conviction.What are the 4 statistical methods? ›
Statistical methods were classified into four categories: descriptive methods, parametric inferential methods, nonparametric inferential methods, and predictive methods.What are the steps in the problem definition process? ›
- Step 1: Define the Problem. What is the problem? ...
- Step 2: Clarify the Problem. ...
- Step 3: Define the Goals. ...
- Step 4: Identify Root Cause of the Problem. ...
- Step 5: Develop Action Plan. ...
- Step 6: Execute Action Plan. ...
- Step 7: Evaluate the Results. ...
- Step 8: Continuously Improve.
- Step One: Define the Problem. Step One is about diagnosing the problem – the context, background and symptoms of the issue. ...
- Step Two: Determine the Root Cause(s) of.
- Step Three: Develop Alternative Solutions. ...
- Step Four: Select a Solution. ...
- Step Five: Implement the Solution. ...
- Step Six: Evaluate the Outcome.
Plan (Ask a question): formulate a statistical question that can be answered with data. A good deal of time should be given to this step as it is the most important step in the process.
Why is statistical analysis useful? ›
Statistical analysis can be used in situations like gathering research interpretations, statistical modeling or designing surveys and studies. It can also be useful for business intelligence organizations that have to work with large data volumes.What are statistical tools? ›
The most well known Statistical tools are the mean, the arithmetical average of numbers, median and mode, Range, dispersion , standard deviation, inter quartile range, coefficient of variation, etc. There are also software packages like SAS and SPSS which are useful in interpreting the results for large sample size.