![]() ![]() Random sample of data from the population.Linearity can be assessed visually using a scatterplot of the data. This assumption ensures that the variables are linearly related violations of this assumption may indicate that non-linear relationships among variables exist.Each pair of variables is bivariately normally distributed at all levels of the other variable(s).Each pair of variables is bivariately normally distributed.The biviariate Pearson correlation coefficient and corresponding significance test are not robust when independence is violated.no case can influence another case on any variable.for any case, the value for any variable cannot influence the value of any variable for other cases.the values for all variables across cases are unrelated.There is no relationship between the values of variables between cases.Independent cases (i.e., independence of observations).Linear relationship between the variables.Cases must have non-missing values on both variables.Two or more continuous variables (i.e., interval or ratio level).To use Pearson correlation, your data must meet the following requirements: ![]() The bivariate Pearson Correlation does not provide any inferences about causation, no matter how large the correlation coefficient is. Note: The bivariate Pearson Correlation only reveals associations among continuous variables. If you wish to understand relationships that involve categorical variables and/or non-linear relationships, you will need to choose another measure of association. Note: The bivariate Pearson Correlation cannot address non-linear relationships or relationships among categorical variables. The direction of a linear relationship (increasing or decreasing).The strength of a linear relationship (i.e., how close the relationship is to being a perfectly straight line).Whether a statistically significant linear relationship exists between two continuous variables.The bivariate Pearson correlation indicates the following: Correlations within and between sets of variables.User can't export graphs in other formats (tiff, jpg, PDF, EPS, etc.The bivariate Pearson Correlation is commonly used to measure the following: No easy way to arrange several graphs for printing or export User can't include data and results as table inset within a graph No custom (additional) ticks and gridlines No interpolation from a standard curve following linear or nonlinear regression No standard errors or confidence intervals from nonlinear regression Nonlinear regression is very difficult, with incomplete results. No repeated-measures ANOVA (one- or two-way) Two-way ANOVA does not allow missing values No automatic error bars on XY graphs-user can write functions to compute error bars and to graph them on XY graphs, but with difficulty Here is a partial list of features missing from Excel, but present in Prism: The data within Prism are automatically updated. Edit the data within Excel (or edit other values which contribute to the calculation of the values you pasted) then close the Excel worksheet. ![]() Instead, you can double-click on the data block in Prism to return to Excel. In either case, you won't be able to edit those values in Prism (without breaking the link). ![]() With the Windows version, you can choose to paste a link to the original Excel file, or you can paste an embedded Excel worksheet within Prism. With the Mac version of Prism, simply paste the values into Prism. Many, perhaps most, Prism users also use Excel and copy and paste data into Prism for analysis and graphing. Prism's graphs and layouts can be exported directly to PowerPoint, or as high-resolution Tiff, PDF, or EPS files. Prism also provides tools to automate repeated analyses and graphing. Prism can create many graphs that Excel simply cannot, including scatter graphs, box-and-whiskers plots, and survival curves. Prism also allows users to create and customize presentation-quality graphs, with features such as automatic error-bars, log axes, offset and discontinuous axes, and custom ticks and grid-lines. Prism is designed specifically for research scientists and makes it easy to perform the analyses scientists need, including t-tests, one- and two-way ANOVA, parametric and non-parametric tests, curve fitting, survival analyses and more. Likewise, Excel does not have the flexibility to create the presentation-quality graphs most scientists require. While it is possible to coax Excel to fit curves, it isn't easy and the results are not complete (no standard errors or confidence intervals of the parameters). Excel does not make quality scientific graphs, and can perform only a few statistical analyses. Microsoft Excel is a general business application that is very good at storing and manipulating large amounts of data. ![]()
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