The chi-square independence test is a statistical test used to determine if there is a significant association between two categorical variables. It assesses whether the observed frequencies of the variables in a contingency table differ significantly from the expected frequencies under the assumption of independence. SPSS Data for Chi-square Independence Test This hypothetical data set … Read more
This tutorial explains the definition of McNemar’s test, the writing of the null and alternative hypotheses for McNemar’s test, the steps of conducting it in SPSS, and how to interpret the output of McNemar’s test as well as how to report the results. When to Use McNemar’s Test? The McNemar test is used to analyze … Read more
This tutorial includes the explanation of what a linear mixed model is, how to structure its statistical model, data example, as well as steps for linear mixed models in SPSS. Definition of Linear Mixed Models Linear mixed models (LMMs) are statistical models used to analyze data that have both fixed and random effects. They are … Read more
This tutorial discusses how dummy and contrast codings impact p-values in SPSS for linear regressions. Single Categorical Variable We can start with only one Y (numerical data, or continuous data) and one X (categorical data). We keep it simple to only have 4 observations. The cell means are (3+4)/2=3.5 vs. (5+6)/2=5.5, and the difference is … Read more
This tutorial compares t-distribution and normal distribution by explaining the similarities and connections between t-distribution and normal distribution. Similarities between t-distribution and normal distribution There are a few similarities between t-distribution and normal distribution. The following figure shows the t-distribution density function curve and the standard normal curve. As we can see, as the sample … Read more
You can use Numpy column_stack() or row_stack() to combine lists into an array. As Columns: np.column_stack((list1, list2,…)) As Rows: np.row_stack((list1, list2,…)) Example 1 of lists to columns The following combines lists into an array using column_stack(). Thus, lists become columns in the array. [[6 2 4] [2 1 1] [3 3 2] [4 4 4] … Read more
This tutorial shows examples of checking if an item is in a Python list. Method 1: item in list_name Method 2: list_name.index(item) Method 3: list_name.count(item) Example for method 1: Check if an item in a list The following code checks if the item of number 6 is in a list. The following is the output, … Read more
random.seed() function can help save the state of random functions. Thus, by using seed(), these random functions can generate the same numbers on multiple code executions. Example 1 Example 1 shows how to use random.seed() and how it impacts the generated numbers. Note that, random.random() generates a floating point number in the range 0.0 <= X < 1.0. The following … Read more
The following is the rule of using ddof in np.std() in Numpy. Rule 1: If you are calculating standard deviation for a sample, set ddof = 1 in np.std(). np.std(sample_name, ddof=1) Rule 2: If you are calculating standard deviation for a population, set ddof = 0 in np.std(). np.std(population_name, ddof=0) Example of ddof = 1 … Read more
This tutorial shows how you can use Numpy to generate random numbers in Python. The following is the basic syntax summarizing 3 functions. 1. Integers: np.random.randint() 2. Normal distribution: np.random.randn() 3. Uniform distribution: np.random.rand() Example 1: Integer np.random.randint(low, high=None, size=None, dtype=int) np.random.randint() will return integer numbers. Given that there are quite a few parameters in randint(), it is … Read more