Data Analysis is a process that takes individuals from being data Novice to being data experts. It is a process of carrying out inspection, transformation modelling and cleaning data with the aim of discovering useful information and suggesting solutions which support decision making (Cramer, 2003). It also comprises of application of basic data science tools which include visualisation modelling and data management. Different bodies use data analysis techniques so as to solve social, economic, environmental and political dilemmas. It uses logistics and linear regression, smoothing in regression, analysis, tests for collinearity of the data set, evaluation of the model, carrying out simulation process and description of stochastic models in the determination of uncertainties and sampling distributions (Cramer, 2003). It also deals with determination of variance, standard errors, confidence intervals and hypothesis testing.Therefore, use of statistics mainly helps psychologists to study and understand the world.
Data Analysis Process
It is the process of attaining raw statistics and convert it to the facts and figures expedient for the users during decision-making.The data collected and analysed answers the question, test hypothesis and disapprove theories.The process can be divided into different phases.They include
In this case, the data which are necessary for the analysis process are determined based on the requirement and the customers or clients who shall use the information for analysis.
It is the process in which the required data is gathered from various sources.They can be collected or obtained from satellites, traffic cameras and recording devices.The data can also be collected through online downloads, interviews and other reading documentations.
It is the processes in which data is organised.It involves organizing data in columns and rows to allow easy analysis and giving inferences based on the results.
It is the process in which errors in the data set is prevented and collected.The processes include deduplication, record matching and column segmentation.The forms of cleaning data include a quantitative method for outlier recognition which is used to correct the errors.
Exploratory data analysis
It is the analysis technique which allows the researcher and the users to start comprehending the communications embedded in the data set.The process may call for an superfluous cleaning of data or an additional request for the data set.Besides, the mode, mean, and median which form part of descriptive statistics may be used.
Modelling and Algorithms
Mathematical formulas such as correlation determination as well as causation is used.Various prototypes are established to help in the evaluation of a precise variable.It includes inferential statistics which are used to measure the relationship between variables.For instance, regression analysis.
It is the statistical analysis of data which involves the determination of the key relationships between the independent and dependent variables. In a linear regression model, the dependent variable of the model is assumed to be a function of an error term and independent variables which is introduced to account for all other factors which cannot be determined within the model. Assumptions for the regression models
The error term u has an expected value of 0 which means that the errors balance itself.
The independent variables are linearly independent variable.
The disturbances u and I are assumed to be homoscedastic
We assume that the variances are not autocorrelatedIf at least three of the above assumptions are gratified, then the OLS method estimator is unbiased and shows that we shall get the true parameter value after getting various sampling distributions (Langdridge, 2004).
It is a data solicitation that uses data inputs and spawns outputs which are quite important to the society. It is based on the model developed above
Visualization and management of Data
Data management system is the process in which data are collected organised, analysed and put ready for usage. It involves incorporation of steps in data analysis which include determination of business objectives, identifying business levers, data collection process, data cleaning which aims at improving data quality, correlation, data modelling, optimisation of results and carrying out a repeat so as to ensure continuous improvements (Roskam, 1968). Data management also involves the determination of research questions and thus ensure that the whole process of developing research projects without any drawbacks.
Pearson correlation analysis
It is a measure of linear correlation between two quantitative and continuous variables which give values between +1 and -1.It helps in the determination of fortes between two variables. It therefore indicate that when the values are +1, there is a positive correlation between variables, when the value is-1, there is a negative correlation, when the value is 0, there is no correlation between the variables.Besides, t-tests (which involves the determination of both type 1 and type 2 error) is established if coefficient of correlation differs from zero
Data analysis process is very crucial in decision making both in research work in academics and in the management of business.It guides the managers over the decisions they ought to have made so as to maximize profits,revenue and cost minimization.Data analysis process is effected by the use of data analysis tools which includes,softwares (stata,evews,SPSS and Lips).These aids in provision of various course of action hence efficiency of firms and organizations.
Cramer, D. (2003). Advanced quantitative data analysis. Maidenhead, Berkshire: Open University Press.
Langdridge, D. (2004). Introduction to research methods and data analysis in psychology. Harlow: Pearson/Prentice Hall.
Roskam, E. E. (1968). Metric analysis of ordinal data in psychology: Models and numerical methods for metric analysis of conjoint ordinal data in psychology. Voorschoten: V.A.M.
Top 10 Data Analysis Tools for Business. (n.d.). Retrieved from http://www.kdnuggets.com/2014/06/top-10-data-analysis-tools-business.html
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