statistic 4

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Question 1


1	A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this the company randomly chooses 6 small cities and offers the candy bar at different prices. The following data table and analysis shows the results of the survey.
		Data Table			Data Table
		City	Unit Price	Unit Sales	City	Unit Price	Unit Sales
		River Falls	$ 1.30	100	River Falls	$ 1.30	100	$ 2.00	$ (0.70)	0.49
		Hudson	$ 1.60	90	Hudson	$ 1.60	90	$ 2.00	$ (0.40)	0.16
		Ellsworth	$ 1.80	90	Ellsworth	$ 1.80	90	$ 2.00	$ (0.20)	0.04
		Prescott	$ 2.00	40	Prescott	$ 2.00	40	$ 2.00	$ - 0	0
		Rock Elm	$ 2.40	38	Rock Elm	$ 2.40	38	$ 2.00	$ 0.40	0.16
		Stillwater	$ 2.90	32	Stillwater	$ 2.90	32	$ 2.00	$ 0.90	0.81
						$ 2.00				1.66
	Be prepared to answer any questions on the relationship of this data. Use simple linear regression.

Question 2


2	A Human Relations manager believes that an individual's wage rate at the factory depends on the individual performance rating and the number of training courses the employee successfully completed in the training program. The manager randomly selects 6 workers and collects the following information.
		Data Table Problem 2
		Employee	Wage Rate	Performance Rate	# of Training Courses
		1	$ 10.00	3	0
		2	$ 12.00	1	5
		3	$ 15.00	8	1
		4	$ 17.00	5	8
		5	$ 20.00	7	12
		6	$ 25.00	10	9
	Be prepared to answer any questions on the relationship of this data. Use multiple regression.

Question 3


3	The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings for membersof the Tour.
			Scoring Average = average score per 18 holes.
			Driving Distance = average number of yards per drive
			Fairways = percentage of drives landing in the fairway.
			Greens = percentage of times able to hit the green in regulation.
			Putts = average number of putts per round
			Sand Saves = percentage of time to get "up and down" from green side sand bunker.
			Player	Scoring Avg.	Driving Distance	Fairways	Greens	Putts	Sand Saves
			Annika Sorenstam	70.04	249.00	75.4	73.2	29.67	47.3
			Karrie Webb	70.00	254.60	72.1	75.1	30.20	38.0
			Kelly Robbins	70.35	256.00	69.3	78.6	29.96	44.9
			Chris Johnson	70.84	249.40	66.9	70.4	30.17	39.6
			Tammie Green	71.24	239.40	70.4	68.6	29.64	47.2
			Juli Inkster	70.64	251.60	69.0	69.8	29.35	46.0
			Liselotte Neumann	71.28	243.50	66.6	65.9	29.36	48.4
			Laura Davies	70.86	258.40	56.4	68.8	29.90	40.0
			Nancy Lopez	70.70	245.50	73.4	73.0	30.23	43.1
			Betsy King	71.52	245.70	65.9	66.9	29.63	37.6
			Lorie Kane	71.47	243.30	74.9	69.4	30.22	47.8
			Michelle McGann	71.52	256.20	57.8	69.6	30.05	51.0
			Donna Andrews	71.01	230.60	79.1	71.7	29.94	42.3
			Colleen Walkeer	72.31	230.00	73.6	63.3	29.58	40.9
			Rosie Jones	71.77	227.20	77.0	66.0	29.86	41.0
			Lisa Hackney	71.34	246.00	69.8	65.1	29.91	36.4
			Jane Geddes	71.34	261.20	64.5	70.0	30.15	44.2
			Alison Nicholas	72.31	241.70	71.8	66.4	30.43	39.4
			Pat Hurst	72.14	251.50	62.4	69.1	30.58	29.9
			Cindy Figg-Currier	71.90	237.20	70.1	68.0	30.40	39.3
		If the "Scoring Average" is the dependent variable be prepared to answer any question of finding the best predictor variable(s). Use the "Backward Elimination" process only.