In the subject of data science, there are many statistical analysis methods. A good analytic report comprises the exploring, collecting, cleaning, converting, and modelling of data for the purposes of an empirical analysis. Not only will this article offers the empirical analysis explained, but it will also focus on both the exploratory and modelling methods that provide understanding on familiarising yourself with different types of data. This is important to enable you to adequately choose which ones are best suited to perform an empirical analysis on your data sets.

After data has been collected, the first step when using any sort of statistical analysis methods should be to understand what each of your variables means as well as establishing whether they are qualitative or quantitative. Measurable or quantitative data and qualitative information should always undergo a testing hypothesis which entails clearly defining your variables upfront. Today, most statistical analysis methods use the software, like SPSS to perform an empirical analysis automatically. Even so, it is still important to examine each variable yourself to establish what each variable is and why it is of use to an empirical analysis.

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It is important to use the correct terminology to construct your analytic report. - Source: Pexels

One of the statistical analysis methods to achieve this is to conduct an exploratory analysis, which is the most preferred way of beginning data exploration in order to extract descriptive statistics for the purposes of an analytic report.

A good analytic report can contain both descriptive statistics that are numerical or graphic in nature. Examples of a numerical analytic report can be found in calculating the average, median, and standard variation. On the other hand, once you perform an empirical analysis, you could also depict this visually through graphic representations of raw data within pie charts, bar graphs, and correlation tables.

Once the exploratory side of an empirical analysis is complete, you should investigate whether your data meets assumptions or your testing hypothesis. Understanding the descriptives of your data is a necessary step that will help you to determine the best approach to take to perform an empirical analysis from the many statistical analysis methods available.

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Testing Hypothesis: The Difference Between Hypothesis and Null Hypothesis

For researchers, there are numerous statistical analysis methods to choose from to perform an empirical analysis. While data collection is critical to your project, however of equal importance is to design quality research questions bases on your observation of a particular demographic within a population group. The main question is known as the testing hypothesis which was made popular by early statisticians, Egon Pearson and Jerzy Neyman in the 1930s. A testing hypothesis in an analytic report is usually a statement about a data set where the research method tests the probability of that statement being true.

Part of any hypothesis is what is identified as a null hypothesis.

A null hypothesis includes a statement about the sample group that usually states that the diverse groups being tested have no connection to each other.

Depending on whether you perform an empirical analysis using quantitative data or qualitative data, the hypothesis, as well as the null hypothesis will change. To decide what question to ask, it is vital to decide which variables are important to your research.

For example, if you are performing an empirical analysis of ANOVA (variance), your testing hypotheses would be:

  • H1: The mean of this dependent variable is not the same across all population groups.
  • H0: The mean of this dependent variable is the same across all population groups.

Empirical Analysis Explained Further: Multivariate Methods  

There are many diverse approaches that can be used when carrying out a multivariate testing hypothesis. The methodology preferred will hinge on what type of question you need to be answered to solve your variables. Depending on what approach you consider, the objective of your research will change. By comparing these diverse approaches, you will be able to select the right statistical analysis method. Note that dependence multivariate methods involve testing hypotheses, interdependence multivariate methods do not.

Dependence Multivariate Methods

Dependence multivariate approaches are potent analyses that strive to describe the connection between one or more dependent variables or several independent variables. Some of the most preferred dependence multivariate methods are:

 

MethodObjectiveHypothesisVariables
Multiple RegressionTo find the connection between two and more variables and to use this information to estimate the worth of the dependent variable.Hypothesis: when the dependent variables have a consequence on the independent variable
Null Hypothesis: when the dependent variables have zero effect.
One dependent scale variable as well as multiple scale-independent variables.
MANOVA (Multivariate Analysis of Variance)To determine if two categorical variables have a consequence on two-scale variables.Hypothesis: There is a consequence on one or both categorical variables on the scale variables.
Null Hypothesis: There is zero effect.
Two dependent scale variables as well as  two categorical variables
Discriminate AnalysisTo ascertain whether groups differ and to what extent the variables in groups are different.Hypothesis: Groups differ in terms of the dependent variable.
Null Hypothesis: the groups do not differ in terms of the dependent variable.
One dependent categorical variable, as well as two and more independent scale variables

Interdependence Multivariate Methods

Interdependent multivariate approaches aim to understand a set of variables as a collection. No distinction is made between whether one variable is dependent or independent. The most preferred interdependence multivariate methods are:

 

MethodUseVariables
Factor AnalysisTo abbreviate data if there are many variables in order to decrease many individual variables into a few proportions.Scale or ordinal variables
Cluster AnalysisTo allocate characteristics to groups of variables in order that each group is comparable with regarding those characteristics, and in which the groups are themselves distinct.Categorical or scale, but the analysis will be more difficult with a mix of variables.

Interpreting the R Squared and P-Value

When considering the analysis of hypotheses, it is vital to comprehend the type of test you have performed. Characteristically, interpreting the outcomes will follow the manner in which the output of the statistical software displays the results. Usually, these are summarised in table format.

Take multiple linear regression, with weight, for instance as the dependent variable and income, height and diet as the independent variables, the most important values reported can be found in the R squared value and p-value. The table below shows how to interpret each.

 

AspectResultInterpretation
HypothesisMultiple Regression where:
j symbolises the number of the dependent variable
B indicates the coefficient
H1 is Bj does not equal 0 for at least one j
H0 is Bj=0.
H1: Income, height, and diet do not have an effect on weight
H0: Income, height, and diet do not have an effect on weight.
R Squared ValueR2 = 0.6868% of the variability concerning weight can be explained by the independent variables - income, height, and diet - in the mode.
P-Valuep = 0.0001With a p-value less than 0.05, at 0.0001, we preserve the hypothesis and scrap the null hypothesis.

When dealing with the diverse multivariate methods, the global correlation rarely exists outside of correlation tables. Ensure that the word correlation in your report or paper is used appropriately.

How to Structure an Analytic Report

There is no doubt, attempting to write the conclusion of your analytic report can be frustrating. However, one way to avoid this is to structure your report correctly. Firstly, lay out your abstract, which is a short summary of your research process. This is written after the data collection and analysis has taken place.

Next, follow with an introduction to your topic that includes a contextual framework. Mention your goal, cite other reputable papers that will help you to provide appropriate content analysis for your own project.

Bearing in mind that the methodological collection of data is varied, provide a detailed description of your method. This could include anything from laboratory tests, surveys, or information from online databases.

The analysis component of your data comprises everything already discussed in this article. Note that this section should include your exploratory analysis depicted through visual graphs and tables, as well as the varying statistical methods used to collect your data. State clearly whether variables pass the hypotheses of the tests you used.

This section where you discuss the results of your analysis is the core of your paper and should be written in a clear and organised style.

This portion, where you analyse the results attained from your data, is the core of any paper you write and should be written in an organised and clear manner.

The final section of your analytic report should focus on a strong conclusion. This involves the inclusion of a summary of your results as well as an evaluation of the report. This means that you could offer alternative processes for handling the research and comment on the highlights and challenges of your methodology.

When it comes to data science and research projects, both are areas where a private tutor in Statistics or data science can be extremely beneficial.

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Niki

Niki is a content writer from Cape Town, South Africa, who is passionate about words, strategic communication and using words to help create and maintain brand personas. Niki has a PR and marketing background, but her happiest place is when she is bringing a story to life on a page.