The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.

Increase in life expectancy is a key indicator to gauge the economic development of a country. Enormous studies have been done to test this hypothesis, and the conclusion is still undecided. This study aims to explore the impact of life expectancy on economic growth in G7 countries via regression approach. Keeping in view the unique population structure of each of these G7 countries, the trend of life expectancy for each country is also observed. Findings of the study indicate that life expectancy in G7 countries increases with constant rate. The increase in life expectancy is accompanied with the increase in Gross Domestic Product (GDP) per capita income. We have also included the population growth rate as another important factor contributing towards GDP. It is worth mentioning here that increase in life expectancy directly affects per capita real income due to higher expenditure on health. Moreover, it is also found that increase in GDP lessens the population growth.

An experiment using factorial design allows one to examine simultaneously the effects of multi-independent factors and their degree of interactions. In this paper, a replicated fullfactorial (RFF) design is run to determine the factors that have significant impact on the response of soft drink experiment. We consider the four factors each with two levels and observe the impact of these factors on the volume of foam of soft drink when pour into a glass. Our investigation finds that the significant main effects are soft drink type (A), amount of soft drink (C), and diameter of glass (D), whereas the significant two-factor interactions B (temperature) with C, and C with D. Furthermore, to support our analysis we do modeling using regression approach based on significant factors and interactions. From the analysis of model adequacy, it is observed that the assumptions underlying the estimated model are appropriate.

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