Alzheimer's disease (AD) is one of the leading causes of brain degeneration, memory impairment and physical functionality of elderly people around the world. In addition, this disease might impact patients’ family members and the financial, economic, and social aspects of their societies. Such prevailing disease necessitates the diagnosis and prognosis of its inception, development and progression as early as possible. Researchers have recently investigated different statistical, data analytics and machine learning approaches to detect such disease at an earlier stage in order to help patients to recover from it successfully and with the minimal harm. This paper reports the empirical study employing data analytics and statistics performed on the Alzheimer’s Disease Neuroimaging Initiative longitudinal data repository (ADNI). Furthermore, the study highlights several factors such as gender, age, education, race, ethnicity and marital status that influence the diagnosis of AD through its progression from cognitively normal (CN) status to mild cognitive impairment (MCI) clinical state and eventually the dementia disease. Additionally, this study aimed to investigate and assess the role and effect of demographic factors of patients on the prognosis, prevalence and development of AD in older people. This effect was assessed using several statistical techniques including descriptive analytics, cross-tabulation distributions, Chi-square tests, ANOVA analyses and Box plots visualizations on the ADNI dataset. Moreover, a considerable significant relationships has been observed between some demographic factors and the progression of AD through the three clinical states (CN, MCI, Dementia) that can significantly assist in the diagnosis and determination of AD in older patients.