Data-driven Multiple Regression Analysis of Teaching Mode Innovation and Teaching Quality of English Education in Colleges and Universities Based on Data

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Figure 5.

Conversion table of attribute codes

Text type	Data attribute	Value
Preview	Yes	0
	No	1
Course	Basic English	0
	Professional English	1
	Business English	2
	English listening	3
Requirement	Self-directed type	0
	Self-driven type	1
	Friendly type	2
	Passive type	3

The calculation result(Ⅱ) of multi-factors linear regression model (2)

Parameter	Parametric estimate	Parametric estimate
k0	0.1864	[0.1324, 0.2379]
k1	0.1012	[0.0443, 0.1566]
k4	0.7036	[0.6311, 0.7792]
R2 = 0.7843, F = 244.1613, p = 0.0000, s2 = 0.0081

The calculation result of single factor linear regression model

Parameter	Parametric estimate	Parametric estimate
β01	0.4843	[0.4064, 0.5631]
β11	0.3185	[0.2182, 0.4192]
R2 = 0.2043, F = 39.7541, p = 0.0000, s2 = 0.0374
β02	-0.9687	[-1.945, 0.0082]
β12	1.7012	[0.7135, 2.6789]
R2 = 0.0729, F = 11.5871, p = 0.0005, s2 = 0.0421
β03	0.3712	[0.2538, 0.4957]
β13	0.5876	[0.3872, 0.7831]
R2 = 0.1879, F = 36.3412, p = 0.0000, s2 = 0.0384
β04	0.2395	[0.1728, 0.3116]
β14	0.7221	[0.6234, 0.8123]
R2 = 0.5871, F = 207.3967, p = 0.0000, s2 = 0.0198
β05	0.3387	[0.0184, 0.6621]
β15	0.3871	[0.0553, 0.7213]
R2 = 0.0354, F = 5.3687, p = 0.0213, s2 = 0.0443

Analysis of correlation coefficients

Group	Type	n	(Sm)uik	(Sn)uij	(SmRn)uijuik	SmRn	r
S1	S1R0	88	8.37	6.01	7.29	0.15	0.23
	S1R1	68	8.91	6.29	11.02	0.22
	S1R2	92	8.74	7.31	18.10	0.26
	S1R3	75	10.14	9.13	22.72	0.26
S2	S2R0	88	8.37	8.38	11.75	0.18	0.2
	S2R1	68	8.91	8.91	4.31	0.08
	S2R2	92	8.72	8.74	21.90	0.30
	S2R3	75	10.14	10.12	16.57	0.21
S3	S3R0	88	8.37	5.04	2.71	0.07	0.1
	S3R1	68	8.89	5.42	7.40	0.16
	S3R2	92	8.74	7.32	2.61	0.05
	S3R3	75	10.12	7.47	14.43	0.20
S4	S4R0	88	5.97	8.37	9.15	0.21	0.25
	S4R1	68	6.29	8.91	2.98	0.05
	S4R2	92	7.29	8.72	22.34	0.35
	S4R3	75	9.13	10.14	25.04	0.34
S5	S5R0	88	6.00	5.06	1.47	0.04	0.07
	S5R1	68	6.27	5.42	4.31	0.11
	S5R2	92	7.28	7.31	-0.11	0.00
	S5R3	75	9.16	7.45	9.52	0.15
S6	S6R0	88	8.35	5.04	0.42	0.02	0.08
	S6R1	68	8.83	5.42	1.67	0.05
	S6R2	92	8.72	7.32	5.44	0.08
	S6R3	75	10.14	7.45	14.88	0.23

Data attributes and examples of training set

ui1	ui2	ui3	ui4	ui5	ui6	ui7
1	88	91	90	85	2	3
0	89	98	84	97	1	0
1	82	93	87	90	0	1
0	89	94	98	91	0	2
1	83	86	94	81	0	0
1	81	84	87	89	0	2
0	88	80	80	92	2	2
1	78	79	88	90	0	1

The calculation result of multi-factors linear regression model (1)

Parameter	Parametric estimate	Parametric estimate
β0	0.8213	[0.0645, 1.5578]
β1	0.1073	[0.0213, 0.1984]
β2	-0.8531	[-1.7682, 0.0536]
β3	0.0573	[-0.1328, 0.2368]
β4	0.6742	[0.5574, 0.7921]
β5	0.1983	[-0.0651, 0.4622]
R2 = 0.6151, F = 46.1386, p = 0.0000, s2 = 0.0175

The calculation result(Ⅰ) of multi-factors linear regression model (1)

Parameter	Parametric estimate	Parametric estimate
k0	0.2011	[0.1276, 0.2732]
k1	0.1153	[0.0364, 0.1935]
k4	0.6531	[0.5384, 0.7633]
R2 = 0.6031, F = 112.9138, p = 0.0000, s2 = 0.0184