Consider the following data generating process (dgp): yi = 10 + 2xi + ui , ui/x ∼ i.i.N(0, σ2 ) where observations for x are generated by a uniform distribution [0, 30]. (a) Write a R script that, using seed 123, generates a sample

4. Outliers can be detected using a statistical approach, one of popular approaches is the Inter Quartile Range (IQR) method. A sample is categorized as an outlier if its value is less than (Q1 - 1.5 * IQR) or greater than (Q3 + 1.5 * 1QR). Use these conditions to detect outliers in the following data: 130,132,138,136,131,153,131,133,129,133,110,132,129,134,135,132, 135,134,133,132,130,131,134,135,135,134,136,133,133,130 note: Q1 = quartile 1, Q3 = quartile 3 Draw the Boxplot to show the outliers %3D

Please implement Multinomial Logistic Regression on the following data. Please continue from the given code:

List experimental_data is read from input. The elements of experimental_data are separated into thirds. The first third of the data is collected in the beginning of a month, the second third in the middle of a month, and the last third in the end of a month. Integer slice_size represents the number of data in each third. Assign slice_size with len(experimental_data) // 3. Assign early_group with a slice of experimental_data from the beginning up to and excluding index slice_size. Assign mid_group with a slice of experimental_data from index slice_size up to and excluding index (2 * slice_size). Assign late_group with a slice of experimental_data from index (2 * slice_size) up to and including the end.