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Unformatted text preview: Chapter 2 I. A response variable measures an outcome of a study I I. An explanatory variable explains or influences changes in a response variable I I I.Scatter plots A. The most common way to display relation between two quantitative variables is a scatter plot B. A scatter plot shows the relationship between two quantitative variables measured on the same individuals 1. The values of one variable appear on the horizontal axis, and the values of the other appear on the vertical axis 2. Each individual in the data appears as the point in the plot fixed by the values of both variable for that individual C. Always plot the explanatory on the x-axis D. Examining a scatter plot 1. In any graph of data, look for the overall pattern and for striking deviations from that pattern 2. You can describe the overall pattern of a scatter plot by the form, direction, and strength of the relationship 3. An important kind of deviation is an outlier, an individual value that falls outside the overall pattern of the relationship E. Two variables are positively associated when above average values of one tend to accompany above-average values of the other and below-average values also tend to occur together F. Two variables are negatively associated when above average values of on tend to accompany below average values of the other, and vice versa G. The strength of a relationship in a scatter plot is determined by how closely the points follow a clear form H. Use different colors or symbols to plot points when you want to add categorical variables to a scatter plot I. To study relationships between variable, we must measure the variables on the same group of individuals J. Form: Linear relationships, where the points show a straight line pattern, are an important form of relationship between two variable Curved relationships and clusters are other forms to watch for IV. Correlation A. Linear relationships are particularly important because a straight line is a simple pattern that is quite that is quite common B. We say a linear relation is strong if the points lie close to a straight line, and weak if they are widely scattered about a line C. The correlation measures the direction and strength of the linear relationship between two quantitative variables, D. Correlation is usually written as r E. The formula r begins by standardizing the observations F. The formula for correlation helps us see that r is positive when there is a positive association between the variable G. Correlation makes no distinction between explanatory and response variables H. Correlation requires that both variables be quantitative, so that it makes H....
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This note was uploaded on 04/30/2009 for the course STA 04100 taught by Professor Gemberling during the Spring '09 term at University of Texas.
- Spring '09