SRMP_Statistics.pdf

# A regression line with a slope of zero is represented

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that evaluates how the observed data plotted on the graph can be explained by a straight line. A regression line with a slope of zero is represented by a flat, straight line and is not statistically significant (see figure below). A flat, straight line indicates that there is no cause-effect relationship meaning that the X variable does not affect the Y variable. For a regression to be significant, the slope of the line has to be statistically different than a slope of zero. If a regression is significant, the slope of the line had to be statistically different that a slope of zero. If a regression is significant; the slope of the line may suggest a positive or negative correlation. A positive correlation shows there is a correlation between the X variable and the Y variable, for example, as the length of the leaf increases so does leaf width. A negative correlation shows an inverse correlation between the X and Y variables, i.e. as length increases, leaf width decreases. Positive Correlation Negative Correlation No Correlation

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Science Research Mentoring Program STATISTICS © 2013 American Museum of Natural History. All Rights Reserved. 23 WORKSHEET: Statistical Analysis (continued) R E G R E S S I O N A N D C O R R E L A T I O N Regression analysis is based on 2 parameters: 1. The R 2 value 2. The slope of the line The closeness of fit between the line and the data points is typically expressed as an R 2 value. The higher the R 2 value the tighter the plotted data points fall around the line. An R 2 value of 0.63 would mean that 63% if the variation observed between the two variables was due to a real relationship between them and 37% of the variation was due to accident, error or chance. The closer the points are to the line (higher the R 2 value) the more reliable is the prediction of the hypothesis. 1. Right click on one of the scatter data points on your graph. Click “Add Trendline.” Select the Linear box. 2. Click the Options tab within this window. Check the Display equation on chart and Display r-squared value on chart boxes. 3. Click OK. 4. What does this information reveal? 5. What about adding another variable? Can Excel produce graphs with X, Y and Z axes? What could you add to your graphs?
Science Research Mentoring Program STATISTICS © 2013 American Museum of Natural History. All Rights Reserved. 24 Session Five: T-tests & Chi square L E A R N I N G O B J E C T I V E S This activity enables students to understand and be able to apply a T-test, and understand how to use Chi square. K E Y T O P I C S T-tests and Chi-square T I M E F R A M E 1 hour, 40 minutes C L A S S O U T L I N E T I M E 20 minutes 1 hour 20 minutes T O P I C Lecture - T-tests and Chi Square Calculating T-tests and Chi Square Discussion of Results D E S C R I P T I O N Students will gain an understanding of when to use these statistical tests and how to analyze the results.

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