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activity_13 - Georgia Southern University College of...

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Georgia Southern University, College of Business Administration BUSA 3131 Business Statistics Fall 2011 Activity 13 Name: ___ 11/15/11 ___ 1. Simple Linear Regression A statistical process called simple linear regression is often used to predict future values of variables using data from past measures of the variables. This process uses a basic mathematical equation, to show how variables are related. The objective is to compare values of an independent variable , x , to values of a theoretically dependent variable , y . The analysis assesses if there is a statistically significant relationship between the variables, and if so, it attempts to describe and quantify that relationship. 2. Why this name for the process? This statistical process uses historical data ( regressive or past trends of data rather than progressive data, which would be collected as time progresses) to estimate likely future trends. The trends of the existing data are plotted on a scatter plot graph with x and y axes to develop a line (hence “linear” analysis) that approximates the manner in which past trends can reasonably be expected to occur in the future. 3. Many of the linear regression terms are familiar to statistics students. The idea of independent and dependent variables apply in any study of the effects of value changes in one variable causing changes in another variable. For example, increases in the numbers of customers entering a retail store (the independent variable) are likely to result in greater sales (the dependent variable) other factors being relatively constant. The variable that is hypothesized as being the cause of changes in another variable is referred to as the independent variable . The variable that is hypothesized as being affected by changes in the independent variable is referred to as the dependent variable . The independent variable is identified as values of x and is quantified on the horizontal axis of a scatter plot. The dependent variable is identified as values of y and is quantified on the vertical axis of a scatter plot. Because of traditional
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