For this assessment, I analyzed a dataset containing 150 rows, for the variables Date, Y, X1, X2, X3. There were no missing values except for Y, which was missing its final value, the value I have to predict with my model.

Firstly, we will take a look at the data. Since this is time-series data, it makes sense to take a look at every data point over time.

The data starts at 1900 and ends in 1912, with a data point for each month of the year. All four variables seem to fluctuate together, rising in the period 1900-1905, falling between 1905-1910, and then rising again for the remainder of the timeframe.

The data points for X1, X2 seem fairly normally distributed, while X3 and Y appear right skewed.

Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
X1 96.70736 112.0039 119.0244 119.6873 127.3004 152.5001 96.70736
X2 100.00000 113.5590 121.0807 121.4432 128.0020 148.4323 100.00000
X3 94.31512 109.5997 120.0914 123.2254 135.2127 176.1341 94.31512
Y 100.00000 116.2600 123.9600 128.1177 139.4700 155.2300 1.00000