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 RESEARCH-RELATE CONCEPTS Difference Between Homoscedasticity and Heteroscedasticity Prof. Ogulu: In fact, Big Data Analytics has now moved on to Industrial Internet of Things (IIoT) and Ecosystemic Data Analytics. What can a developing nation do today to stay economically afloat in a globally connected world? It must resort to Big Data Analytics or better still, the wider Data Science in every sector, in every industry and in every organisation. Kenedy Nnaji: Thanks Prof. DJO for reading my paper on Autoregressive Conditional Heteroscedasticity (ARCH/GARCH family of models) and making some intuitive remarks. I want say here that Homoscedasticity and Heteroscedasticity are two sides of a coin. Whereas the error term entering the classical regression model is assumed to be homoscedastic (i.e. its variance is assumed to be constant over time), heteroscedasticity becomes an issue of concern if this assumption is violated. So, the difference between homoscedasticity and heteroscedasticy lies in whether the variance of error term is constant or not. Just as you rightly advised, the two concepts will be thoroughly considered in the upcoming seminar. Godwill Wilcox: Good morning all. How can we validate the reliability of a questionnaire? Students often write, validation of instrument and reliability of instrument. Is it correct? Favorite Ilo: When an instrument is developed by a researcher or the researcher adopted an instruments, the researcher takes the instrument to a experts in this field for validation. It the experts that will establish the validation by rephrasing some of the item if the need arises. Godwin Bupo: Good morning all. I have a question. Can Chi-Square be computed for data on interval scale? I have always thought that Chi-square is used when the data is on a norminal or ordinal scale. Please can someone explain to me. Ellatechy: You can convert data on interval scale to nominal or ordinal scale before applying Chi square.