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Statistics is the study of real world data and inferring them to get a meaningful result. In broader sense, it is a mathematical way of collecting, verifying, analyzing, interpreting and hence summarizing a complete set of data. Hence, it can be said that this field provides knowledge to work quantitatively on collected data. The understanding of statistics is applied in number of technical and practical fields. Economics, Business, Science, and Research are all the important fields that require knowledge of statistics for completion of the professional courses.
Studying Statistics helps individual, company, government and nation in number of ways. The statistical calculations serve an important pillar to summarize the large information in quantitative way. Hence, below are the points that reflects the reasons for studying Statistics in college and universities:
- The knowledge of Statistics helps us to conduct our research task or projects carefully and logically. It means that we can make a skilful decision by interpreting the collected data logically with the use of Statistical tool.
- Study of Statistics helps in development of critical thinking and analytical skills.
- Basic ideas on Statistics helps in proper evaluation of any information.
- To be able to read and understand articles and journals that contain statistical information, Statistical knowledge is very important.
Hence, these are few points that reflect the necessity of studying statistics in this advanced world. For sure, pursuing statistics can always be an advantage for students because it enhance our capability to skillfully present and infer any information.
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Specialization in Statistics can be done in two major areas:
1. Descriptive Statistics
Descriptive Statistics is the way to summarize and interpret the collected data so that a meaningful pattern is formed. But this area is narrowed down for making conclusion of only those informationвЂ™s that we have collected or that is presented to us. Hence, applying descriptive statistics requires two types of statistical ideas: measure of spread also called graphical summary. Graphical summary uses graphical representation to analyze data. Second is the, measure of central tendency also called numerical summary. In case of numerical summary, mean, median, mode, quartile are used to analyze the given data.
2. Inferential Statistics
It is the study conducted for inferring or equating our data with the general conditions. So, this shows the difference between inferential statistics and descriptive statistics. Inferential statistics is used in the broader area of statistical study. Hence, the method used in inferential statistics is sample, testing of hypothesis, principle component analysis and regression. Explaining it further with an example: If you need to calculate the marks of 150 students of a class, knowledge of Descriptive statistics can be applied but on the other hand if you want to calculate the marks of entire students of Australia, you need to apply the knowledge of Inferential statistics in which a correct sample from a particular population is drawn to get the result.
These are two major areas in statistics. Hence, studying statistics broadens up knowledge in practical fields and thus serve as a helpful tool in taking decisions wisely.
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Statistics is the science of formulating decisions through the use of empirical facts and quantitative data. Statistics provides tools for prediction and forecasting via statistical models. We are unique Academic Writing Agency located in USA and Netherlands. Hire us for your Statistics assignments, homework and problems.
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Statistics is a branch of mathematics where data is collected, analyzed, interpreted, presented, and organized. Two major analytical strategies are made use of in data evaluation: descriptive statistics, which outlines records coming from a sample using elements like Mean or Standard Deviation, as well as inferential statistics, which draws conclusion from records that undergo arbitrary variance (e.g., empirical mistakes, testing errors).
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Statistical Analysis Help
Statistics is an academic discipline which is one of the most popular academic disciplines. The term is defined as the systematic study through which a person can gather, organize, analyze, illustrate, and present the data graphically. Statistics is mostly used to generalize the data in order to draw the conclusion about something.
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A large number of mathematical tools as well as techniques are used to gather the data that include survey questionnaires, focus groups, interviews and many others. The data can be derived from the large population and then it is significantly analyzed by the statisticians. In addition, the data represents in the graphical format so that a person is able to arrange the data in a systematic manner for the purpose to illustrate the conclusion. Statistics is also used to design the data collection tools which are used to gather the data by the target population or sample for various purposes. help with assignments In the present world, the field of statistics is changing quite fast. Due to the technological changes, a large number of changes are also occurred in statistics. The tools and techniques of statistics are used in number of other disciplines that include management sciences, finance, marketing, social sciences, medicines and many others. The essential purpose of statistics is to analyze the situation and make the decision about particular scenario. A number of decisions are made on the basis of statistical findings. In universities and colleges, assignment or project are assigned to the students that require extensive research in order to analyze situation and draw conclusion as well as recommendation. Therefore, a lot of students take help from the statistical tools and techniques for the purpose to gain success in their assignment or project.
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Furthermore, many businesses or professions also take the help from statistics in order to do business researches such as marketing research, finance research, etc. In marketing research, a company or business conducts a research from the customers in order to take their insights about new product of company. However, this can only happen by the data collection tools and techniques of statistics. In addition, it generally helps the company in order to estimate the future of the new product.
Generally, the data collection tools and techniques of statistics are used in academics, professions, and also in our daily lives. All of us can make our daily life decisions on the basis of statistical thinking. In addition, we also used statistics in our daily life conversation. For instance, if one can say that he burns 1000 calories per day with the help of various exercises. Therefore, this statement shows the statistical thinking of a person. This clearly shows that person can generate the result on his daily exercising activities.
Particularly, statistics has five main steps which need to be followed in order to draw the conclusion about something. These steps include:
- Formulate a data collection tool through which one can collect the data from the respondents.
- Gather all the data and organizes in a systematic way.
- Draw the findings in a meaningful ways.
- Use graphs, tables, or pictures in order to make the data more understandable.
- Finally, draw the conclusion, give recommendation, as well as estimate the future.
The study of statistics is quite helpful for any person whether he is business professional, student, employee or a lay man. It increases the quality in the life of humans. Nowadays, people are willing to use statistical tools and techniques in their day to day actions in an excessive manner. The people who are interested to take help about the statistical tools and techniques, they should avail our statistics help service. Our service is really beneficial for those who want to make their profession in the field of statistics. Students of any educational service can also take our professional guidance for statistics assignment or homework. Our statistics help service has the quality, however we offer discount packages especially for the students.
Statistics is a wide ranging academic discipline. It has numerous concepts and theories. The concepts and theories are quite complex and it creates lot of problems for the students. As it has been mentioned before that thousands of people are use statistical techniques for the purpose to make decisions in the regular life. Currently, the people from all the professions use statistical techniques in order to analyze different situations which significantly impact the performance of the professionals. In addition, statistical methods are also used in the war like situations. Decision making plays an important role in war like situation; however without using the statistical thinking one cannot take effective or efficient decisions. A lot of other professional examples also explain the importance of statistics.
For instance, the economic issues that include wages, GDP, demand and supply, inflation and many others have significantly affect the economy of a particular country. However, these problems will be solved by doing the analysis of historical and current data with the help of statistical methods or techniques. These methods will help the economics to identify the problems and give recommendations in order to solve the economic issues.
Moreover, management sciences also take the help of statistics in order to solve the managerial issues. Particularly, marketing management, financial management as well as research courses heavily depends on the statistics methods and approaches. In these courses, one can consider hypothetical conditions which might be tested through statistical tests or analysis. The purpose of statistical testing is to reject or approve hypothetical assumptions.
Furthermore, other professions such as medicine, armed forces, engineering, etc. are also heavily rely on the statistical concepts and theories. For instance, doctors can analyze the medical history of patients before suggesting him a treatment in order to prevent the disease. In addition, armed forces also use statistical methods for the purpose to make strategies. Strategies require extensive scientific knowledge and assumptions which cannot be achieved without taking the help from the area of statistics.
In the field of statistics, a lot of ways through which one collects the data. The purpose of data collection is to make future predictions or derive solutions for the present problems. One of the major tasks in the field of statistics is to collect the data; however the analysis of findings and conclusion comes later. Data collection tools include questionnaire, focus group, in-depth interviews and many others. It is necessary that the data collection tool should be designed or selected as per the type of research or the attributes of the population. All the researches are mainly based upon the data collection methods; however one is unable to conduct a research without collecting the data.
In statistics, there are numerous data collections methods are used for conducting a research in which some of them are experimental studies and observational studies, sampling, etc.
Experimental and Observational Studies
In the causal studies, the data collection method such as experimental as well as observational are used in order to gather the data from population. Causal study is the type of quantitative research. In this kind of study, researcher finds the cause and effect among two variables. Researcher constructs a hypothetical condition which called hypothesis between the variables so that cause and effect will be determined. Work performance and stress is one of the best examples of the causal studies.
Moreover, the researcher mostly collects the data through observation or experimental methods in the causal studies. In the observational method, researchers can closely observe the activities of respondents for the purpose to collect the data. For instance, use of mystery shoppers is one of the best examples of data collection through observations. On the other hand, researcher can design an experiment for the respondents in order to do data collection. Ian Pavlov’s theory can be considered as the example of the experimental data collection tool.
The other kind of data collection method is sampling. It is also used as the method of data collection through which researcher gathers the responses from the participants about a particular issue or problem. Sampling data collection method is used to collect responses from the large population. Sampling is the most preferable data collection methods for the qualitative studies. resume review service In sampling, it is convenience to collect large amount of data in a few time.
Furthermore, sampling data collection method has two kinds that include probability sampling and non-probability sampling. Probability sampling data collection method is also known as the random sampling. In this kind of data sampling technique, researcher is free to choose the elements from the sample on a random basis. It means that all the elements of the sample have an equal chance to select by the researcher.
On the other side, non-probability sampling is also the data collection method. In this type of sampling, researchers have the authority to choose or select only those elements that are convenience to select.
In the field of statistics, it is necessary for the researches to first identify the type of data before collecting the data. The reason is that if the data collection tool is not equivalent with the data type, then it will be interpreted in a wrongful manner. Statistics has different kinds of data categories which should be identified in order to do correct data analysis.
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Statistics is the field of study or an academic discipline which mainly comprises on data. The fundamental function of statistics is to gather data for the purpose to draw conclusions or solve complex issues with the help of logical reasoning. It is better to categorize the data first before starting the data collection. In addition, categorization of data also helps the researcher in order to best data collection method.
Particularly, there are seven main fundamental kinds of data which is used in the field of statistics. The seven types of data are ordinal, categorical, binary, real valued multiplicative, real valued additive, count and binomial. Each data type has its specific characteristics that should be incorporate with the data collection methods. The description of all data types are given below:
Ordinal is the data type in the field of statistics. In this type of data, the data represents in the form of real numbers as well as integers till the level of arbitrary. Ordinal scale is the level of measurement which is used to measure ordinal data. For instance, the salary of a person is considered as the example of ordinal data.
Categorical is also the type of data in statistics. Categorical data type is quite similar to the binary data type that is because the permissible statistics as well as the measurement scale of both these data types are same. statistics help online The categorical data can be written in the form of number that starts from 1 and ends at infinity value. Its values are based on random choices or the judgment of an individual. The example of categorical data type is the selection of a political party by an individual in order to become a member.
The third type of data called binary. Binary data type represents in only two digits that include 0 and 1. Computers only understand the language of 0 and 1. The numbers 0 and 1 function as yes or no. In addition, this data type is only used where the possible outcomes are only two. The examples of the possible outcomes of binary data type are “Yes or No”, and “True or False”. In statistics, nominal scale is used as the measurement scale binary data. In addition, the appropriate statistics for binary data type are Chi-squared as well as Mode.
Real Valued Multiplicative
Another data type is real valued multiplicative in the discipline of statistics. The values of real valued multiplicative represent in the real numbers. In addition, the real numbers should be in positive. For instance, the prices of the goods as well as the income of an individual come under the real valued multiplicative. The measurement scale for this data type is ratio scale. All the data of interval scale is also used for the real value multiplicative data type. However, the favorable statistics of this data type are coefficient of variation, harmonic mean as well as geometric mean.
Real valued additive is the data type which is used in the field of statistics. It is used to represent the distance of a place as well as the temperature of anything. It is necessary to use real number in order to represent this data type. In addition, interval scale is the preferable scale of measurement for real valued additive. However, the permissible statistics of binomial data type is also used for this data type.
Binomial data type is also used in statistics in order to present the data. It is one of the kinds of data type which is used to highlight the ratio of something that includes satisfaction, votes and many others. The value of binomial data type represents in numbers which starts from 0 and it goes till the infinity. The statistics which are used in the binomial data type are correlation, standard deviation, median, mode and mean.
Another data type is count which is one of the types of data categorization. The values of this data type represents in the positive numbers. Count data type is used to characterize the number of females, males, casualties, births and many others. Ratio scale is used as the measurement scale for count data type. Harmonic mean, geometric mean as well as coefficient of variation are the appropriate statistics for the count data type. In addition, the statistics of the interval scale is also applied on this data type.
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- Descriptive Statistics
- Expectations and Variances
- Inferential Statistics
- Laws of Large Numbers
- Minimax Estimators
- Multivariate Multiple Regression Analysis
- Neyman-Pearson Testing Hypotheses
- Nonparametric Estimation
- Normal Distribution
- Order Statistics
- Packages such as SAS, SPSS, R, Mahout, Nutch, Solr, MatLab, Excel, and many more.
- Theorem of Bayes
- Pie Charts
- Point Estimation
- Poisson Distribution
- Pólya’s Lemma
- Probability Integral Transform,
- Random Variables
- Sampling Theory
- Scatter Plots
- Sequential Sampling
- Stochastic Independence
- The Central Limit Theorem
- The Maximum Likelihood Principle
- Univariate case
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- Analysis of Variance
- Binomial Distribution
- Coefficient of Variance
- Game Theory
- Hypothesis Testing
- Index Number: –
- Linear Programming
- Average Distribution
- Operations Research
- Percentiles and Quartiles: –
- Sampling Theory
- Simplex Method
- Queuing Theory
- Poisson Distributions
- SPSS: –
- Standard Deviation
- Theory of Probability
- Tree Diagrams
- Transportation Problem
- Vital Statistics
- Time Series
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