Technologies For Data Statistics and Testing

Data and its analysis a crucial aspects for modern businesses to consider. This importance is due to the extreme competitiveness in business as each company vyes with their rivals for sales. Because of this, businesses will resort to investing in various strategies to get an edge over the competition through the use of technology. Technology has been progressing rapidly, with simple things like a security cam even becoming technically advanced. One advanced form of technology that companies invest in deals with data statistics and testing. This technology can help them make sense of data to analyze trends and test products. They can then make informed decisions with the data backing them up and reducing risks. There are various such technologies available, and in this article, we will look at them. 

Technologies For Data Statistics And Testing

Types of Data Statistics and Tests

Many technologies, mostly software programs, can help present data in useful graphical forms where trends are visible. You can then use these graphics to compare, analyze, and further process to achieve the desired result or make a decision. There are various tests and statistics the data can be presented in. Here are some of the more common ways that this data is processed. These are: 

  • Exploratory Data Analysis (EDA): This is an integral part of data science that forms the basis for machine learning and statistics. Data scientists can use it to test for various statistical qualities like outliers and correlation. 
  • Regression and Classification: This is an essential part of statistical models as it helps to establish a relationship in raw data. This data can then be classified into different data models
  • Forecasting: Forecasting can identify trends in data and then estimate how it will act in the future. This is important for many businesses. 
  • Data Grouping: This can be used to group data into different classifications to organize raw data. 

Which technology can be used to achieve this?

These technologies can be advanced and straightforward, depending on the complexity of the required statistics. They can do some or most of the statistics mentioned above. You can use this software across various applications that vary from simple school experimentation to advanced business decision-making. These software tools also specialize in specific niches according to the type of statistics and data testing required. Here are some of these software programs: 

MATLAB

MATLAB is an advanced analytical tool that is used extensively by scientists and engineers. You can use it across a variety of industries including finance, medical research, and climatology. You can use it to organize and analyze complex data sets that you can use in various flexible ways. These include customizable data visualization, function applications, and report generation. MATLAB is a highly complicated software that may not be suitable for novices. 

Microsoft Excel

Microsoft Excel is a data statistics software program that is a lot more suitable for beginners. While not technically advanced, it can still perform basic functions and data visualization useful for introductory statistics. It is a great way to start in statistics with a decent enough set of tools available. You can even use customizable graphics to generate reports. 

Statistical Analysis Software (SAS)

SAS is a statistical analysis software that is a premium solution used by many companies in business and research. You can use any data set and subject them to the latest statistical techniques and methods. You can also create a large variety of charts and graphs to present this data visually. You can use either the GUI provided by the software or make up your script for complex statistical analysis. 

To sum up

Data analysis is an essential aspect for most industries to consider. This is because it can help them get a competitive edge in the market. Data statistics and testing are one such aspect that can help companies look at trends and make decisions based on them. There are a variety of statistical tests and software that you can use, which we have discussed in this article. We hope it has provided you with great insight into technologies for data statistics and testing.

Chi-Square Test of Independence

Chi-square test is a non-parametric test. The assumption of normal distribution in the population is not required for this test. The statistical technique chi-square can be used to find the association (dependencies) between sets of two or more categorical variables by comparing how close the observed frequencies are to the expected frequencies. In other words, a chi-square ($\chi^2$) statistic is used to investigate whether the distributions of categorical variables are different from one another. Note that the responses of categorical variables should be independent of each other. We use the chi-square test for a relationship between two nominal scaled variables.

The chi-square test of independence is used as a test of goodness of fit and as a test of independence. In a test of goodness of fit, we check whether or not the observed frequency distribution is different from the theoretical distribution, while in a test of independence, we assess, whether paired observations on two variables are independent from each other (from the contingency table).

Example: A social scientist sampled 140 people and classified them according to income level and whether or not they played a state lottery in the last month. The sample information is reported below. Is it reasonable to conclude that playing the lottery is related to income level? Use the 0.05 significance level.

 Income
LowMiddleHighTotal
Played46282195
Did not play14121945
Total604040140

A step-by-step procedure for testing of hypothesis about the association between these two variables is described, below.

Step1:
$H_0$: There is no relationship between income and whether the person played the lottery.
$H_1$: There is a relationship between income and whether the person played the lottery.

Step2: Level of Significance 0.05

Step 3: Test statistics (calculations)

Observed Frequencies ($f_o$)Expected Frequencies ($f_e$)$\frac{(f_o – f_e)^2}{f_e}$
4695*60/140= 40.71$\frac{(46-40.71)^2}{40.71}$
2895*40/140= 27.14$\frac{(28-27.14)^2}{27.14}$
2195*40/140= 27.14$\frac{(21-27.14)^2}{27.14}$
1445*60/140= 19.29$\frac{(14-19.29)^2}{19.29}$
1245*40/140= 12.86$\frac{(12-12.6)^2}{12.86}$
1945*40/140= 12.86$\frac{(19-12.86)^2}{12.86}$
$ \chi^2=\sum[\frac{(f_0-f_e)^2}{f_e}]=$6.544

Step 4: Critical Region:
Tabular Chi-Square value at 0.05 level of significance and $(r-1) \times (c-1)=(2-1)\times(3-1)=2$ is 5.991.

Step 5: Decision
As the calculated Chi-Square value is greater than the tabular Chi-Square value, we reject $H_0$, which means that there is a relationship between income level and playing the lottery.

Note that there are several types of chi-square tests (such as Yates, Likelihood ratio, test in time series) available which depend on the way data was collected and also the hypothesis being tested.

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