Variables are used in R to store information for computational purposes. It seems that there is almost no limit to what can be stored in a variable. To make a variable, you need to know the following information.
- The name you want to give the variable
- The information you want to store in the variable
Here is an example,
You want to make a variable that will store the following test score: 80, 81, 82, 83, 84, 85. You want to call the variable test_scores. Here is what we know
- The name of the variable is test_scores
- It will contain the values of 80, 85, 90, 95, 100
Here is how this would look in R.
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> test_score <- 80:85
There are a few things to explain
- the <- sign means “assigned to” in other words, the variable name on the left of the <- sign is being assigned to the values 80:85.
- The colon sign stands for a sequence. We wanted all whole numbers from 80 to 85 and the colon sign provides this information.
- Both the <- and : are known as operators in R. Operators symbols you place between numbers in order to make a calculation.
Know, type test_scores into the R console and press enter. You should see the following.
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> test_scores [1] 80 81 82 83 84 85
R now shows you everything that is stored in the variable. We can also created other variables and perform calculations through the use of variables. For example, let’s say that you want to add 5 points of extra credit to the scores of the test. To do this let’s make a variable called extra_credit and add test_scores to extra_credit
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> extra_credit <- 5 > test_scores + extra_credit [1] 85 86 87 88 89 90
As you can see, R took the values of test_scores and add extra_credit to each value in test_scores. This is much faster than entering each value separately to calculate it. We can also make a new variable for the new scores and we can call it revised_test_scores Let’s try
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> revised_test_scores <- test_scores + extra_credit > revised_test_scores [1] 85 86 87 88 89 90
Variables can also be used for text. The only difference is that you must put quotes around the words. Otherwise, the computer will think the words are numbers, which does not make sense. Below is an example,
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> h <- "Howdy" > h [1] "Howdy"
Lastly, variables can be used to store vectors. This is very useful in saving a lot of time in performing calculations. We will now make the variable student_names and assigned a vector to it containing the names of students.
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> student_names <- c("David", "Edward", "John") > student_names [1] "David" Edward" "John"
This is only the beginning of some of the amazing features of R.
look at the history of R as well as the Characteristics of this software.
which deals with data that is not nominal. This post is about the Goodness-of-Fit Test. The Goodness-of-Fit test compares the distribution of the observed data with an expected distribution.
The line is the best fit because it reduces the amount of error between actual values and predicted values in the model. The official name of the model is the least square model in that it is the model with the least amount of error. As such, it is the best model for predicting future values
parametric test such as the data being normally distributed. This allows a researcher to still make inferences from data that may not have normality. In addition, non-parametric test are used for data that is at the ordinal or nominal level. In many ways, Spearman correlation and Pearson product moment correlation compliment each other. One is used in non-parametric statistics and the other for parametric statistics and each analyzes the relationship between variables.
indicates a departure from independence. In other words, the higher the correlation the stronger the relationship and thus the more the variables have in common at least on the surface.
were two hypotheses one for the treatment groups and one for the blocks or subpopulations. What we are doing for two-analysis is assessing the interaction effect, which is the amount of the variation of
happening at the same time, there will be more than one set of hypotheses to consider. There will be a set of hypotheses for the treatment groups and also for the block groups. The block groups are the several subpopulations with the sample. Below are the assumptions
used when comparing one sample population to a known value or two sample populations to each other. When two or more sample populations are involved it is necessary to use analysis of variance. The simple rule is 3 or more use analysis of variance
correlated. This test is most commonly employed for “before and after” or pretest-postest design. Below are the assumptions of paired sample t-test.
between the two means. We infer that there is a difference between the population means by seeing if there is a difference between the sample means. The assumptions for testing for the difference between two means are below.
value. For example, if someone states that the average salaries for teachers are $63,000.00 you can assess this by collecting data from teachers to compare it to this known value. You collect some data and you find that the average salary for 35 teachers was $65,7000.00. The questions you have is who is right? Do teachers really make on average $63,000.00 like the report or do they make $65,700.00 as my data says? Before going further let us establish are hypotheses for this example.
researcher and they are…
amount and direction. The difference can also be discrete (finite difference) or continuous (infinite amount of difference). An example of an interval level is temperature. Temperature indicates the difference in hot and cold, you can tell the direction whether it is increasing or decreasing, and it is continuous in that there are an infinite number of potential temperatures.
serve in the experiment. In experimental research, we have independent and dependent variables. Independent variables are variables that are controlled by the researcher and are believed to have an effect on the dependent variable. Dependent variables are affected by the independent variables.