Customer	Sales	Christmas	State of Residence
Holden	362.97	72.59	WA
Cary	116.28	33.26	WA
Lingg	292.28	58.46	WA
Mundy	115.88	26.13	WA
Flores	797.31	143.19	WA
Rankin	996.78	188.84	WA
Domingo	347.46	69.49	WA
Price	188.79	37.76	ID
Chow	179.88	35.98	ID
Schieber	554.27	115	ID
Hendrickson	820.28	385	ID
Malone	537.38	107.48	ID
Kagen	982.28	196.46	ID
Lyman	337.65	69.95	ID
Gross	504.87	94.97	OR
Osborne	172.5	34.5	OR
Stevens	361.5	72.3	OR
Carruth	828	172.25	OR
Field	359.37	71.87	OR
Thiesen	332	66.4	OR
McClaren	185.37	39.07	OR
Knorr	237.32	47.46	MT
Smith	169.32	33.86	MT
Knoxx	353.93	72.79	MT
Ellis	1,497.68	299.54	MT
Buchannan	829.32	165.86	MT
Pierce	188.37	36.5	MT
Hayes	225	45	MT
Garfield	169.49	33.9	NV
Arthur	985.99	204.2	NV
Cleveland	188.78	37.76	NV
MCKinnley	582.81	116.56	NV
Taft	228	55.13	NV
Wison	342.75	68.55	NV
Harding	988.83	194.77	NV
Coolidge	398.82	79.76	UT
Hoover	345	72.31	UT
Roosevelt	387	77.4	UT
Truman	192.9	39.4	UT
Bilson	216.42	43.28	UT
Michele	227	46.84	UT
Field	176.33	35.27	UT

The 2016 Sales data contains customers’ historical annual sales and their Christmas spend.

Build a model to estimate upcoming Christmas spend based on their annual sales.

Evaluate the goodness of fit; try to improve the model’s fit to over 90%.

Answer rating: 100% (QA)

We will build a linear regression model to predict Christmas spend based on annual sales The model w... View the full answer