Fifteen days ago, Harold Williams started selling macaron boxes in his coffee shop to offer customers something sweet to have with their preferred beverage. After careful observation, he believes that sales of macaron boxes may be influenced by tea sales and weather. He decided to build a linear regression model with "macaron boxes sales" as the dependent variable, and "weather" and "tea sales" as independent variables. Use the data provided in Table 3 to build this linear regression model.

Note that weather is coded in a way that 0 represents a rainy day and 1 represents a sunny day.

Table 3. Weather, tea sales, and macaron sales

	Weather	Tea sales (in cups)	Macaron sales (in boxes)
Day 101	1	116	35
Day 102	0	97	24
Day 103	1	108	33
Day 104	1	112	29
Day 105	0	106	30
Day 106	0	99	28
Day 107	1	108	33
Day 108	1	127	39
Day 109	1	123	37
Day 110	1	106	31
Day 111	0	120	44
Day 112	0	110	40
Day 113	1	102	41
Day 114	0	130	50
Day 115	1	130	49
Day 116	1	135	60

How much of the variation in the total macaron sales can be explained by the two independent variables given on Table 3 without adjustment based on the number of independent variables?