Using Index Fossils Index fossils are commonly used to match rock layers in different places (Figure below). If two rock layers have the same index fossils, then they’re probably about the same age. The presence of more than one type of index fossil provides stronger evidence that rock layers are the same age.

Index Fossils- Index fossils are the main type of fossil used in correlation. To be an index fossil, a fossil species must be: 1) Easily recognized (unique).

To this day, fossils are useful as correlation tools to geologists specializing in stratigraphy. This requires that the lifespan of the fossil species be but a moment of time relative to the immensity of geologic history. In other words, the fossil species must have had a short temporal range.

The principle of fossil correlation states that like assemblages of fossils are the same age and therefore strata containing these particular fossils are also the same age. Fossils are therefore very useful in correlating, or matching up rock sequences in exposures separated by great distances.

The goal of correlational research is to describe the relationship between variables and to measure the strength of the relationship. A correlation describes three characteristics of a relationship. The direction (positive / negative)of the relationship.

A positive correlation is a relationship between two variables in which both variables move in the same direction. A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other.

0.19 is most likely the correlation coefficient for the set of data shown. Step-by-step explanation: A correlation coefficient is a measure of how well the line of best fit fits the data. The higher the correlation coefficient, up to 1.0 or -1.0, the better the fit.

Answer: -0.85 (Option d) is the strongest correlation coefficient which represents the strongest correlation as compared to others.

(symbol: ρ) an index expressing the degree of association between two continuously measured variables for a complete population of interest.

Critical Values for the correlation coefficient r Consult the table for the critical value of v = (n – 2) degrees of freedom, where n = number of paired observations. For example, with n = 28, v = 28 – 2 = 26, and the critical value is 0.374 at a = 0.05 significance level.

How to Read a Correlation Matrix

1. -1 indicates a perfectly negative linear correlation between two variables.
2. 0 indicates no linear correlation between two variables.
3. 1 indicates a perfectly positive linear correlation between two variables.

The Pearson correlation coefficient is a number between -1 and 1. In general, the correlation expresses the degree that, on an average, two variables change correspondingly. The P-value is the probability that you would have found the current result if the correlation coefficient were in fact zero (null hypothesis).

For a product-moment correlation, the null hypothesis states that the population correlation coefficient is equal to a hypothesized value (usually 0 indicating no linear correlation), against the alternative hypothesis that it is not equal (or less than, or greater than) the hypothesized value.