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Data Processing/Statistical Analysis

Data Processing/Statistical Analysis. Calculating Reaction Rates: This is a slope - just like in math class! rate = y = y 2 -y 1 Graphing: At least two ways to graph data:. x x 2 -x 1. Data Processing/Statistical Analysis. Write down a definition for each of the following: Mean

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Data Processing/Statistical Analysis

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  1. Data Processing/Statistical Analysis Calculating Reaction Rates: This is a slope - just like in math class! rate = y = y2-y1 Graphing: At least two ways to graph data: x x2-x1

  2. Data Processing/Statistical Analysis Write down a definition for each of the following: Mean Range Standard Deviation Error Bars

  3. Data Processing/Statistical Analysis Check your work: Mean - average of data points Range - spread of data (difference between smallest and largest point) Standard Deviation - measure of spread of data around the mean (how tightly clustered the data are) Error Bars - represent variability of graphed data - can show range or standard deviation

  4. Statistical Analysis If you are going to use a statistical test, you should state a null hypothesis. What is a null hypothesis? When would you use a correlation test (Pearson r value)? When would you use a t-test?

  5. Null Hypotheses If you are going to use a statistical test, you should state a null hypothesis. What is a null hypothesis? A null hypothesis predicts no difference between your data sets. Ex. No correlation exists between dog size and hours of barking per day. There is no difference in coolness of left-handed and right-handed people.

  6. Correlation When would you use a correlation test (Pearson r value)? To determine the correlation between two variables. Shortcuts in Excel: =PEARSON(array 1, array 2) =CORREL(array 1, array 2) How to interpret: -1 0 1 Strong No Strong negative correlation positive (Reject null) (Accept null) (Reject null) quick demo

  7. Correlation r2 is the coefficient of determination How to interpret: r2 can be reported as a %, and tells you how much of the variance of one variable is accounted for by the other variable. Ex. If the correlation between dog size and amount of barking is r=.90 (strong positive correlation, reject null hypothesis), then r2=.81 This means that 81% of the variation in barking can be accounted for by dog size. You could make a good prediction of barking based on size.

  8. t-Test When would you use a t-test? To compare the means of two data sets. Shortcuts in Excel: =TTEST(array 1, array 2, tails, type) **tails=2, type=2 How to interpret: If p>0.05, Accept the null hypothesis There is no significant difference between data sets. If p<0.05, Reject the null hypothesis There is a significant difference between data sets. quick demo

  9. t-Test How to interpret: If p>0.05, Accept the null hypothesis There is no significant difference between data sets. If p<0.05, Reject the null hypothesis There is a significant difference between data sets. Ex. Null hypothesis: There is no difference in coolness of left-handed vs right-handed people. If p=0.01...will you accept or reject the null hyp? …..is there a significant difference in coolness? Review <, >

  10. Wiki Resources What page and topic will you find the “Excel Stats Activity” and “Topic 1 Notes” on? IB DP Biology SL, Topic 1 How might these resources help you with your IA?

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