Currently working as Assistant Professor of Statistics in Ghazi University, Dera Ghazi Khan.
Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
l like Applied Statistics, Mathematics, and Statistical Computing.
Statistical and Mathematical software used is SAS, STATA, Python, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel.
Like to use type-setting LaTeX for composing Articles, thesis, etc.
The post contains the MCQs on quality control. Statistical quality control is used for (i) process control and (ii) product control. For process control, variable and attribute sampling is used and for product control, the acceptance sampling technique is used. Let us start with the Online MCQs Quality Control quiz.
Statistical quality control (SQC) is a broad field that utilizes statistical methods to monitor and maintain the quality of products and services. It encompasses various tools and techniques to ensure consistent production of good quality output, minimizing waste, and improving efficiency.
Most of the MCQs on this page covered Estimate and Estimation, Testing of Hypothesis when the assumption of population parameters are unknown, that is Non-Parametric Methods, etc.
The relationship/ Dependency between the attributes is called association and the measure of degrees of relationship between the attributes is called the coefficient of association. The Chi-Square Statistic is used to test the association between the attributes. The Chi-Square Association is defined as
Time series analysis deals with the data observed with some time-related units such as a month, days, years, quarters, minutes, etc. Time series data means that data is in a series of particular periods or intervals. Therefore, a set of observations on the values that a variable takes at different times.
Real-World Applications of Time Series Analysis
Finance: Predicting stock prices, and analyzing market trends.
Sales and Marketing: Forecasting demand, and planning promotions.
Supply Chain Management: Optimizing inventory levels, and predicting product needs.
Healthcare: Monitoring patient health trends, and predicting disease outbreaks.
Environmental Science: Forecasting weather patterns, and analyzing climate change.
Correlation analysis is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of the correlation lies between $-1$ and $+1$. The regression analysis describes how an explanatory variable is numerically related to the dependent variables.
The formula to compute the correlation coefficient is:
Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed using a graphical representation between the variables. We can also compute the strength of the relationship between variables by performing numerical calculations using appropriate computational formulas.
Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both correlation and regression are used to indicate how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.
