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Lesson 10 : Simple Regression and correlation Introduction to Correlation and Regression Analysis In this section we will first discuss correlation analysis, which is used to quantify the association between two continuous variables (e.g., between an independent and a dependent variable or between two independent variables). Regression analysis is a related technique to assess the relationship between an outcome variable and one or more risk factors or confounding variables. The outcome variable is also called the response or dependent variable and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "y" and the independent variables are denoted by "x". [NOTE: The term "predictor" can be misleading if it is interpreted as the ability to predict even beyond the limits of the data. Also, the term "explanatory variable" might give an impression

LESSON 4 MEASURES OF DISPERSION LESSON 4 MEASURES OF DISPERSION Why dispersion? Measures of central tendency, Mean, Median, Mode, etc., indicate the central position of a series. They indicate the general magnitude of the data but fail to reveal all the peculiarities and characteristics of theseries. In other words, they fail to reveal the degree of the spread out or the extent of the variability inindividual items of the distribution. This can be explained by certain other measures, known as ‘Measures ofDispersion’ or Variation. We can understand variation with the help of the following example : ---------------------------------------------- Series 1      Series 11      Series III --------------------------------------------- 10              2                     10 10              8                     12 10              20                    8 --------------------------------------------- ∑X = 30       30                    30 ---------------------------------------------- In all t

Measures of Central Tendency Introduction A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode. The mean, median and mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others. In the following sections, we will look at the mean, mode and median, and learn how to calculate them and under what conditions they are most appropriate to be used. Mean (Arithmetic) The mean (or average) is the most popular and well known measure of central tendency. It can be used with both discrete and conti