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(a)

The temperature and Chirps/second data table is

\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \

SNO	Temperature °C X	Chirps/second Y	XY	X²	Y²
1	20.0	89	1780	400	7921
2	16	72	1152	256	5184
3	19.8	93	1841.4	392.04	8649
4	18.4	84	1545.6	338.56	7056
5	17.1	81	1385.1	292.41	6561
6	15.5	75	1162.5	240.25	5625
7	14.7	70	1029	216.09	4900
8	15.7	72	1130.4	246.49	5184
9	15.4	69	1162.6	237.16	4761
10	16.3	83	1352.9	265.69	6889
11	15.0	80	1200	225	6400
12	17.2	83	1427.6	795.84	6889
13	16.0	81	1296	256	6561
14	17.0	84	1428	289	7056
15	14.4	76	1094.4	207.36	5776
∑	∑X = 248.5	∑Y = 1192	∑XY = 19988.5	∑X² = 4657.89	∑Y² = 95412

We make use of Linear Regression.

A linear Regression is in the form of y = a + bx

Where y is the chirps/second

x is the Temperature.

The values of a and b is given by

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