The following script does those three things to conduct the t-test of means. Do some kind of manipulations to the data (optional).There are basically three main steps to running an analysis in RStudio: " group1" and " groupl" are also not the same (the former ends with a one and the latter ends with a lower case "L"). For example, " cat" and " cat " ("cat" followed by a space) are not the same even though they look the same. This prevents the accidental assignment of grouping variables that look the same, but that are actually different character strings. It is also best when assigning grouping variables to copy the first instance and paste it into the other cells. For example: "1, 2, 3" is NOT a good series of grouping variables, while "block1, block2, block3" is fine. This can cause unpredictable behavior in R. Note: when you create a grouping variable, you should NOT use one that is composed only of numeric characters. Notice how this format is different from the format we would typically use for Excel: The height data table with grouping variable should look like this: The grouping variable is just a string (series of characters) that identifies the group to which a particular data value belongs. The format for doing a t-test of means in R requires placing all of the data in one column with a second column containing a "grouping variable". We will be doing Example 1 from the review page, so you should refer to that before starting this exercise. Link to the full guide in the BSCI 111a course guide.Scientific Literature Guide Toggle Dropdown.5 Reporting the Results of a Statistical Test.3.2 ANOVA with more than two treatment groups.3.1 ANOVA basics with two treatment groups.2.6 Conducting a chi-squared contingency test using R.2.4 Conducting a chi squared contingency test using Excel.2 Joint probability and the Chi Squared Contingency test.1.7 Conducting a Chi Squared Goodness of Fit test using R.1 Probabilities, frequencies, and the Chi Squared Goodness of Fit test.
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