C H A P T E R 7 Two-way traffic – summarizing and representing relationships between two variables Chapter objectives This chapter will help you to: ■ ■ ■ ■ ■ ■ explore links between quantitative variables using bivariate analysis measure association between quantitative variables using Pearson’s product moment correlation coefficient and the coefficient of determination quantify association in ordinal data using Spearman’s rank correlation coefficient represent the connection between two quantitative variables using simple linear regression analysis use the technology: . | CHAPTER 7 Two-way traffic -summarizing and representing relationships between two variables Chapter objectives This chapter will help you to explore links between quantitative variables using bivariate analysis measure association between quantitative variables using Pearson s product moment correlation coefficient and the coefficient of determination quantify association in ordinal data using Spearman s rank correlation coefficient represent the connection between two quantitative variables using simple linear regression analysis use the technology correlation and regression in EXCEL MINITAB and SPSS become acquainted with business uses of correlation and regression 224 Quantitative methods for business Chapter 7 This chapter is about techniques that you can use to study relationships between two variables. The types of data set that these techniques are intended to analyse are called bivariate because they consist of observed values of two variables. The techniques themselves are part of what is known as bivariate analysis. Bivariate analysis is of great importance to business. The results of this sort of analysis have affected many aspects of business considerably. The establishment of the relationship between smoking and health problems transformed the tobacco industry. The analysis of survival rates of micro-organisms and temperature is crucial to the setting of appropriate refrigeration levels by food retailers. Marketing strategies of many organizations are often based on the analysis of consumer expenditure in relation to age or income. The chapter will introduce you to some of the techniques that companies and other organizations use to analyse bivariate data. The techniques you will meet here are correlation analysis and regression analysis. Suppose you have a set of bivariate data that consist of observations of one variable X and the associated observations of another variable Y and you want to see if X and Y are related. For instance the Yvariable .
