Dear Student
Welcome to the CCBC102 (2CBC102) module within the NHC Marketing Programme. This module entails a study of introduction to basic statistical skills for business science programmes like Marketing, Business Management Accounting and Economics. You will be introduced to elementary statistics through explaining the meaning of data and their different types and their properties. Primary vs secondary data will be visited and the distinction between samples vs population will be covered. An explanation of the 2 statistical laws governing numbers will be outlined and the concepts of probability and non-probability sampling will be explained. Moreover, the different ways of representing the data in the form of tables and graphs to summarise their essential characteristics will be demonstrated. It will be emphasised that to make effective business and policy decisions it is essential to transform raw data by applying descriptive statistical methods like table graphs and summaries including mean median modes, variances and standard deviations. These transformations assist one to see clear trends and patterns in the data that can assist one to make proper business and policy related decisions. You will be taught how to construct the various types of tables, graphs including pie charts, bar charts stacked-bar charts, dot plots, stem and leaf diagrams, box whisker plots X-Y plots and contingency tables. The differences between small and big data sets and the methods used to summarise them will also be demonstrated. Other the core items in the descriptive statistics methodology are covered include absolute frequency versus relative frequency which is critical for constructing tables and graphs. The 5 number summary of small datasets will be taught and the box whisker plots to understand and summarise the essential characteristics which will include mean median modes, interquartile range and the distribution of the data. Then we will progress to big datasets and will learn how to construct frequency tables histograms, Ogive curves and frequency polygons. Thereafter we will progress to higher level problems involving the understanding of dispersion through learning first the concept of central tendency to describe the mean and then progress to learn how statisticians measure dispersion (or spread/scatter/distribution) around the mean. This will assist us to then move forward to embrace the notions of variance and standard deviation as well as learning how to construct a confidence interval. We then will study Cherbysheff’s Theorem that give rise to the Empirical Rule. Further, the measurements of skewness and kurtosis, which assist us to understand the important concept of a normal distribution. All of the above analyses will involve single variable analyses but the key ideas learnt here will assist us in progressing to bivariate analyses through understanding how construct an X-Y scatter plot and to visualise the possible relationship between two variables and estimate and interpret the correlation between them. Further we will learn how to draw (estimate the coefficients) a straight line through the scatterplot.
The final section of the course will involve leaning about the concept of probability its meaning. Then simple and complex probability events as well as the addition, multiplication and complement rules will be studied. Calculating probabilities using Tables and Tree Diagrams will be demonstrated finally you will be introduced to the idea of a normal distribution and a standard normal distribution. If time permits also student t-distributions will be covered. You will also be introducded to Z-table and if time permits t-tables as well.
Welcome to the CCBC102 (2CBC102) module within the NHC Marketing Programme. This module entails a study of introduction to basic statistical skills for business science programmes like Marketing, Business Management Accounting and Economics. You will be introduced to elementary statistics through explaining the meaning of data and their different types and their properties. Primary vs secondary data will be visited and the distinction between samples vs population will be covered. An explanation of the 2 statistical laws governing numbers will be outlined and the concepts of probability and non-probability sampling will be explained. Moreover, the different ways of representing the data in the form of tables and graphs to summarise their essential characteristics will be demonstrated. It will be emphasised that to make effective business and policy decisions it is essential to transform raw data by applying descriptive statistical methods like table graphs and summaries including mean median modes, variances and standard deviations. These transformations assist one to see clear trends and patterns in the data that can assist one to make proper business and policy related decisions. You will be taught how to construct the various types of tables, graphs including pie charts, bar charts stacked-bar charts, dot plots, stem and leaf diagrams, box whisker plots X-Y plots and contingency tables. The differences between small and big data sets and the methods used to summarise them will also be demonstrated. Other the core items in the descriptive statistics methodology are covered include absolute frequency versus relative frequency which is critical for constructing tables and graphs. The 5 number summary of small datasets will be taught and the box whisker plots to understand and summarise the essential characteristics which will include mean median modes, interquartile range and the distribution of the data. Then we will progress to big datasets and will learn how to construct frequency tables histograms, Ogive curves and frequency polygons. Thereafter we will progress to higher level problems involving the understanding of dispersion through learning first the concept of central tendency to describe the mean and then progress to learn how statisticians measure dispersion (or spread/scatter/distribution) around the mean. This will assist us to then move forward to embrace the notions of variance and standard deviation as well as learning how to construct a confidence interval. We then will study Cherbysheff’s Theorem that give rise to the Empirical Rule. Further, the measurements of skewness and kurtosis, which assist us to understand the important concept of a normal distribution. All of the above analyses will involve single variable analyses but the key ideas learnt here will assist us in progressing to bivariate analyses through understanding how construct an X-Y scatter plot and to visualise the possible relationship between two variables and estimate and interpret the correlation between them. Further we will learn how to draw (estimate the coefficients) a straight line through the scatterplot.
The final section of the course will involve leaning about the concept of probability its meaning. Then simple and complex probability events as well as the addition, multiplication and complement rules will be studied. Calculating probabilities using Tables and Tree Diagrams will be demonstrated finally you will be introduced to the idea of a normal distribution and a standard normal distribution. If time permits also student t-distributions will be covered. You will also be introducded to Z-table and if time permits t-tables as well.