Question

In reviewing the data in the Performance Lawn Equipment Database, Elizabeth Burke noticed that defects received from suppliers have decreased (worksheet Defects After Delivery). Upon investigation, she learned that in 2014, PLE experienced some quality problems due to an increasing number of defects in materials received from suppliers. The company instituted an initiative in August 2015 to work with suppliers to reduce these defects, to more closely coordinate deliveries, and to improve materials quality through reengineering supplier production policies. Ms. Burke noted that the program appeared to reverse an increasing trend in defects; she would like to predict what might have happened had the supplier initiative not been implemented and how the number of defects might further be reduced in the near future. Use trendlines and regression analysis to assist her in evaluating the data in this worksheet and to reach useful conclusions. Summarize your work in a formal report with all appropriate results and analyses.

Defects After Delivery
Defects per million items received from suppliers
Month	2014	2015	2016	2017	2018
January	812	828	824	682	571
February	810	832	836	695	575
March	813	847	818	692	547
April	823	839	825	686	542
May	832	832	804	673	532
June	848	840	812	681	496
July	837	849	806	696	472
August	831	857	798	688	460
September	827	839	804	671	441
October	838	842	713	645	445
November	826	828	705	617	438
December	819	816	686	603	436

Answer 1

The graph is:

Data				Forecasts and Error Analysis
Period	Demand (y)	Period(x)		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	812	1		926.2607	-114.261	114.2607	13055.5	14.07%
Period 2	810	2		919.3705	-109.37	109.3705	11961.9	13.50%
Period 3	813	3		912.4803	-99.4803	99.48027	9896.325	12.24%
Period 4	823	4		905.5901	-82.5901	82.59008	6821.121	10.04%
Period 5	832	5		898.6999	-66.6999	66.69989	4448.875	08.02%
Period 6	848	6		891.8097	-43.8097	43.8097	1919.29	05.17%
Period 7	837	7		884.9195	-47.9195	47.91951	2296.279	05.73%
Period 8	831	8		878.0293	-47.0293	47.02931	2211.756	05.66%
Period 9	827	9		871.1391	-44.1391	44.13912	1948.262	05.34%
Period 10	838	10		864.2489	-26.2489	26.24893	689.0063	03.13%
Period 11	826	11		857.3587	-31.3587	31.35874	983.3705	03.80%
Period 12	819	12		850.4685	-31.4685	31.46855	990.2694	03.84%
Period 13	828	13		843.5784	-15.5784	15.57836	242.6851	01.88%
Period 14	832	14		836.6882	-4.68816	4.688163	21.97888	00.56%
Period 15	847	15		829.798	17.20203	17.20203	295.9098	02.03%
Period 16	839	16		822.9078	16.09222	16.09222	258.9595	01.92%
Period 17	832	17		816.0176	15.98241	15.98241	255.4375	01.92%
Period 18	840	18		809.1274	30.8726	30.8726	953.1176	03.68%
Period 19	849	19		802.2372	46.7628	46.7628	2186.759	05.51%
Period 20	857	20		795.347	61.65299	61.65299	3801.091	07.19%
Period 21	839	21		788.4568	50.54318	50.54318	2554.613	06.02%
Period 22	842	22		781.5666	60.43337	60.43337	3652.192	07.18%
Period 23	828	23		774.6764	53.32356	53.32356	2843.402	06.44%
Period 24	816	24		767.7862	48.21375	48.21375	2324.566	05.91%
Period 25	824	25		760.8961	63.10395	63.10395	3982.108	07.66%
Period 26	836	26		754.0059	81.99414	81.99414	6723.039	09.81%
Period 27	818	27		747.1157	70.88433	70.88433	5024.588	08.67%
Period 28	825	28		740.2255	84.77452	84.77452	7186.719	10.28%
Period 29	804	29		733.3353	70.66471	70.66471	4993.502	08.79%
Period 30	812	30		726.4451	85.5549	85.5549	7319.642	10.54%
Period 31	806	31		719.5549	86.4451	86.4451	7472.755	10.73%
Period 32	798	32		712.6647	85.33529	85.33529	7282.111	10.69%
Period 33	804	33		705.7745	98.22548	98.22548	9648.245	12.22%
Period 34	713	34		698.8843	14.11567	14.11567	199.2522	01.98%
Period 35	705	35		691.9941	13.00586	13.00586	169.1525	01.84%
Period 36	686	36		685.1039	0.896054	0.896054	0.802914	00.13%
Period 37	682	37		678.2138	3.786246	3.786246	14.33566	00.56%
Period 38	695	38		671.3236	23.67644	23.67644	560.5737	03.41%
Period 39	692	39		664.4334	27.56663	27.56663	759.9191	03.98%
Period 40	686	40		657.5432	28.45682	28.45682	809.7907	04.15%
Period 41	673	41		650.653	22.34701	22.34701	499.389	03.32%
Period 42	681	42		643.7628	37.2372	37.2372	1386.609	05.47%
Period 43	696	43		636.8726	59.1274	59.1274	3496.049	08.50%
Period 44	688	44		629.9824	58.01759	58.01759	3366.041	08.43%
Period 45	671	45		623.0922	47.90778	47.90778	2295.155	07.14%
Period 46	645	46		616.202	28.79797	28.79797	829.3232	04.46%
Period 47	617	47		609.3118	7.688163	7.688163	59.10786	01.25%
Period 48	603	48		602.4216	0.578355	0.578355	0.334495	00.10%
Period 49	571	49		595.5315	-24.5315	24.53145	601.7922	04.30%
Period 50	575	50		588.6413	-13.6413	13.64126	186.084	02.37%
Period 51	547	51		581.7511	-34.7511	34.75107	1207.637	06.35%
Period 52	542	52		574.8609	-32.8609	32.86088	1079.837	06.06%
Period 53	532	53		567.9707	-35.9707	35.97069	1293.89	06.76%
Period 54	496	54		561.0805	-65.0805	65.08049	4235.471	13.12%
Period 55	472	55		554.1903	-82.1903	82.1903	6755.246	17.41%
Period 56	460	56		547.3001	-87.3001	87.30011	7621.309	18.98%
Period 57	441	57		540.4099	-99.4099	99.40992	9882.332	22.54%
Period 58	445	58		533.5197	-88.5197	88.51973	7835.742	19.89%
Period 59	438	59		526.6295	-88.6295	88.62954	7855.195	20.24%
Period 60	436	60		519.7393	-83.7393	83.73934	7012.278	19.21%
				Total	-2.2E-12	3002.533	206258	442.08%
Intercept	933.150847			Average	-3.6E-14	50.04222	3437.634	07.37%
Slope	-6.8901917				Bias	MAD	MSE	MAPE
						SE	59.63365
Forecast	512.849153	61
						Correlation	-0.89751
						Coefficient of determination	0.805521

	r²	0.806
	r	-0.898
	Std. Error	59.634
	n	60
	k	1
	Dep. Var.	Demand (y)
ANOVA table
Source	SS	df	MS	F	p-value
Regression	8,54,307.9812	1	8,54,307.9812	240.23	2.76E-22
Residual	2,06,258.0188	58	3,556.1727
Total	10,60,566.0000	59
Regression output				confidence interval
variables	coefficients	std. error	t (df=58)	p-value	95% lower	95% upper
Intercept	933.1508
Period(x)	-6.8902	0.4445	-15.499	2.76E-22	-7.7800	-6.0003

Therefore, we can conclude that the results are significant and the prediction analysis can be proceeded.