Help with Statistics and Data Analysis

Help with Statistics and Data Analysis

Data analysis is collecting, interpreting and assessing data using analytical and statistical tools to come up with useful information that will help in decision making. Statistics is a branch of mathematics that deals with collecting, interpreting and analyzing numerical data. Statistical analysis is a subdivision of data analysis that involves collecting and examining samples.

A number of methods are used in data analysis. These include data mining, business intelligence, data visualization, and text analytics. Business intelligence is making data a reality by transforming it into actions, for example, finding markets. Texts analytics is deriving beneficial information from texts. Data visualization refers to representing information using pivotal tables, graphs and other tools of statistics. Data mining is a technique for discovering patterns using statistical methods.

Statistical tests

Statistical tests involve a process of accepting or rejecting a hypothesis by comparing evidence generated from data against the hypothesis.


Analysis of Variance (ANOVA) is a statistical method that seeks to find out the difference between groups. For example,  a group of patients trying three types of pain reliever paracetamol, ibuprofen and aspirin. You want to find out which one works best

There are different types of ANOVA models such as

  4. T-tests
  5. One way ANOVA


It is a statistical method that seeks to find out whether the distribution of data observed in a sample fits well with the expected data. If the variables are constant. There are several unique characteristics of the Chi-square test. These include;

  1. It can only analyze categorical data.
  2. It examines the null hypothesis when the variables are independent.
  3. Data must be in numbers but not in percentages


It is a statistical test that seeks to find out the significant difference between two data sets that are related. It has an alternative and null hypothesis. The null hypothesis states that there is no difference between the two sets of data. The alternative hypothesis states that there is a difference between the two data sets.

The T-test looks for a critical value which helps identify whether there is a significant difference between the datasets. It compares against 0.05 p-value. If the critical value after the end of the test is less than 0.05 this indicates that there is only a 5% chance that the data is random. It proves that the null hypothesis is correct.

For example

Finding out whether there is a relationship between the grades of public schools and private hypothesis.

Types of T-tests

  1. Un equal variance T-test
  2. Equal variance T-test
  3. Correlated T-test

Correlation test

It is a statistical test that seeks to find out the relationship between two or more variables. For example, whether there is a correlation between the height of a mother and her daughters. There are different types of correlation analysis

  1. Pearson correlation – it measures linear dependence when there is a normal distribution between x and y.
  2. Kendall tau: it is used to measure the relationship between rank based data.
  3. Spearman rho: it is also used to measure the relationship between rank based data that does not have a normal distribution.
  4. Compute correlation in R: It is the use of R functions to check the association between two variables. The functions can be computed using cor () or cor.test (or)

Regression test

It is a software test that seeks to establish whether a code or program change has affected the existing features. It involves re-running functional and non-functional tests to ensure that the existing functionalities are working well. There are different techniques for conducting a regression test

  1. Retest all – It is re-running all the tests in the existing test suite.
  2. Regression test selection – It is running of a test in parts of the test suite.
  3. Prioritization of test cases – involves running of tests depending on the impact and use of parts.


It is a statistical test used in large samples to determine if there is a noteworthy difference between two means of groups when the variance are known and some features are similar. There are several steps involved in Z-test. These include:

  1. Define the null and alternative hypothesis
  2. State alpha
  3. State decision rule
  4. Calculate test static
  5. State results
  6. State conclusion

Parametric tests

It is a statistical test that examines a hypothesis assuming that the sample has a normal distribution. It is the complete opposite of non-parametric tests which assumes that the sample does not have a normal distribution.

Statistical software

These are programs used in collection, organization, analysis, interpretation, and presentation of statistical data.


Statistical Package for the Social Sciences (SPSS) is a software package that analyzes statistical data. It analyzes data from different sources such as survey results, organization customer databases, and scientific research results among others. SPSS analyzes many kinds of data with different formats such as spreadsheets, plain text files, and relational databases.


It is a statistical software package that offers data science needs. Such as analysis, manipulation, interpretation, and presentations.

STATA capabilities

  1. Management of data
  2. Graphical representations
  3. Statistical analysis
  4. Simulations
  5. Customizing programs.

R software

It is a software that has a collection of analysis tools, it is also a programming language for statistical computing.


  1. Data handling and storage
  2. Doing calculations on spreadsheets and arrays
  3. Displaying graphics on the screen.


It is a statistical software for windows that is used for data analysis, time series estimation and forecasting.


  1. Statistical and panel data analysis.
  2. Econometric analysis
  3. Time series estimation


It is a software package that does the following:

  1. Managing data
  2. Data analysis
  3. Business intelligence


It is a software that works with maps and geographical information. It used to collect, organize, interpret, map, analyze and share geographical information to access real-world problems.

In conclusion, statistical tests play a vital part in proving a hypothesis right or wrong, while statistical software is essential in data analysis. They both play an important role in developing beneficial information that is crucial for decision making.

Statistics is one of the branches of mathematics that is quite popular. This is because it equips students with the ability to collect, analyze and interpret data. In most cases when doing so students use samples because collecting data of an entire population is not only time consuming but also very expensive. For you to be good in statistics then you must have great critical thinking ability. You must also enjoy working with numbers. This is because the data that are analyzed using different statistical data are normally quantitative in nature. It is good to note that not all students enjoy interpreting quantitative data and as such, some students opt to look for help with statistics and data analysis. It is not a bad idea to look for such help but there are a number of things that you should keep in mind when doing so.

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