1. Definition of Linear Regression Model Multiple linear regression is a linear model accessing the relationship between a dependent variable (DV, or Y) and multiple intendent variables (IV, or X). For instance, you might want to test how consumer purchase intention can be impacted by price as well as by household income. In this case, … Read more
numpy.random.seed() provides a seed, which acts as a starting point number generator algorithm. For the same seed, we will always get the same set of random numbers on any machine. If you prefer to have different sets of random numbers every time you run the code, do not set the seed. In contrast, if you … Read more
Introduction You can use histplot() from seaborn module to do the histogram plot. The following provides 3 examples. The following is the basic syntax of using histplot() for the examples. Example 1: Core syntax sns.histplot(data=dataset, x=’column_name’) Example 2: Group by the histogram sns.histplot(data=dataset, x=’column_name’, hue=’column_groupby’) Example 3: Add a kernel density estimate sns.histplot(data=dataset, x=’column_name’, kde=True) … Read more
There are built-in datasets in Python and you can use them to do some practice. In doing so, you do not need to import external datasets. The following provides a list of built-in sample datasets in Python. 1. penguins in seaborn The penguins dataset was collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER. … Read more
This tutorial shows how to write null and alternative hypothesis for two-way ANOVA. It is an extension of my other tutorial on this same topic. 1. Introduction A two-way ANOVA is used to test whether the means from the two or more categorieal variables are significantly different from one another. For instance, below, there are two categorical variables, … Read more
The following provides 3 different methods of calculating means group by two Columns in Python. Method 1: df.groupby([“column_1″,”column_2”]).mean() Method 2: df.groupby([“column_1″,”column_2”]).agg(‘mean’) Method 3: pd.crosstab(index=df[‘column_1’], columns=df[‘column_2’],values=df[‘dv’],aggfunc=’mean’) Prepare the data Output: city store sales 0 City1 store1 10 1 City1 store2 20 2 City1 store1 20 3 City1 store2 50 4 City1 store1 30 5 City2 store2 10 … Read more
This tutorial shows how you can plot Two-Way ANOVA interaction in Python. In particular, you can use interaction_plot() function from statsmodels.graphics to plot the Two-way ANOVA. Step 1: Prepare the data Suppose that there are two categorical variables, namely city (city 1 and city 2) and store (store 1 and store 2). The dependent variable … Read more
This tutorial shows how to calculate Sum of Squares Total (SST) in R. The following is the data being used. The hypothetical data being used has two categorical IVs (cities and stores) and one DV (sales). Note that, while it has two IVs, the calculation of SST actually does not need to use these two … Read more
1. Introduction Type I, Type II, and Type III ANOVA are 3 different ways of calculating sum of squares in ANOVA. Type I ANOVA: SS(A) for factor A SS(B | A) for factor B SS(AB | A, B) for interaction AB Type II ANOVA: SS(A | B) for factor A SS(B | A) for factor … Read more
This tutorial explains what Type I, Type II, and Type III ANOVA are. Further, it provides examples showing how you can do Type I, Type II, and Type III ANOVA in R. 1. Introduction Type I, Type II, and Type III ANOVA are 3 different ways of calculating sum of squares in ANOVA. Type I … Read more