![p value for hypothesis test calculator chi square p value for hypothesis test calculator chi square](https://www.statology.org/wp-content/uploads/2021/02/chiSheets3.png)
You can use the function table() to create a contingency table or just enter the data directly with the function matrix() (conTable <- table(myData)) # phenolics
#P VALUE FOR HYPOTHESIS TEST CALCULATOR CHI SQUARE CODE#
The code is below but it is commented out because I don’t actually want to do this. You can also create a csv file from data that you entered into R with the function write.csv(). (myData <- ame(herbivore, phenolics)) # herbivore phenolics
![p value for hypothesis test calculator chi square p value for hypothesis test calculator chi square](https://i.ytimg.com/vi/HwD7ekD5l0g/mqdefault.jpg)
Below I just enter the data directly into R. You can enter the data into Excel and read the data into R with the function read.csv(), or directly enter the data into R. We go into the field and randomly select plants from our population of interest, and record whether the herbivore is present and take a tissue sample to determine whether the plant has induced a high level of phenolics as a defense. Let’s assumed we are studying a plant that can induce a chemical defense when an herbivore is present. There are two ways, record each observation as a row, or create a contingency table. The data are typically entered and archived similar to data for a Chi-squared goodness-of-fit test. Null hypothesis: The two variables are indepedentĪlternative hypothesis: The two variables are not independent The alternative is that the two variables are dependent. The null for both tests are that the two variables are independent of each other. The null and alternative hypotheses are the same for the Chi-squared and G contingency tests.