SPSS Analysis: Relationship Between HgbA1c And Weight In Diabetic Boys

December 22, 2023/

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Model Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	R	R Square	Adjusted R Square	Std. Error of the Estimate Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper
1	.204^a	.042	-.032	4.29418

Regression analysis of HgbA1c and weight at baseline

a. Predictors: (Constant), HgbA1c_1

The value of R-Squared is 0.042; this implies that only 4.2% of the variation in weight is explained by HgbA1c.

Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	10.429	1	10.429	.566	.465^b
Residual	239.720	13	18.440
Total	250.149	14

a. Dependent Variable: wgt1

b. Predictors: (Constant), HgbA1c_1

The coefficients table below shows that the p-value for the HgbA1c is 0.465; this value is higher than the 5% level of significance. The null hypothesis is not rejected suggesting that HgbA1c does not significantly predict weight.

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	33.218	6.227		5.334	.000
HgbA1c_1	.553	.735	.204	.752	.465

a. Dependent Variable: wgt1

In summary, only a small proportion of variation (4.2%) in weight is explained by HgbA1c and also results showed that the independent variable (HgbA1c) does not significantly predict weight.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.217^a	.047	-.026	5.76184

a. Predictors: (Constant), HgbA1c_2

Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	21.405	1	21.405	.645	.436^b
Residual	431.585	13	33.199
Total	452.989	14

a. Dependent Variable: wgt2

b. Predictors: (Constant), HgbA1c_2

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	52.987	13.773		3.847	.002
HgbA1c_2	-1.370	1.706	-.217	-.803	.436

a. Dependent Variable: wgt2

One year later the results do not significantly change. Only 4.7% of the variation in weight is explained by HgbA1c. Again, the p-value for the HgbA1c is found to be 0.436; this value is higher than the 5% level of significance leading acceptance of the null hypothesis hence implying that HgbA1c does not significantly predict weight.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.461^a	.213	.152	2.20266

a. Predictors: (Constant), HgbA1cDiff

The value of R-Squared is 0.213; this means that 21.3% of the variation in difference in weight is explained by difference in HgbA1c.

Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	17.061	1	17.061	3.517	.083^b
Residual	63.072	13	4.852
Total	80.133	14

a. Dependent Variable: wgtdiff

b. Predictors: (Constant), HgbA1cDiff

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	-3.874	.590		-6.569	.000
HgbA1cDiff	.934	.498	.461	1.875	.083

a. Dependent Variable: wgtdiff

The p-value for the difference in HgbA1c is 0.083 (a value less than 10% level of significance), hence we can say that difference in HgbA1c significantly predicts difference weight at 10% level of significance.

d. The results obtained showed that HgbA1c does not significantly predict weight. This is true even after one year. However, when we obtain the difference in weight and the difference in HgbA1c for the two periods we saw some improvements. The difference in HgbA1c was able to significantly predict the difference in weight though at 10% level of significance. Further investigation should focus on controlling for time and seeing how the results behave.

	BPMeds	N	Mean	Std. Deviation	Std. Error Mean
age	Not on antihypertensive	603	53.9088	8.07358	.32878
On antihypertensive	41	57.6341	5.99481	.93623

	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
age	Equal variances assumed	7.330	.007	-2.900	642	.004	-3.7254	1.28470	-6.248	-1.203
Equal variances not assumed			-3.754	50.423	.000	-3.7254	.99228	-5.718	-1.733

We performed an independent t-test was in order to compare the average age for those on antihypertensive (BPMEDS = 1) and those not on antihypertensive (BPMEDS = 0). Results showed that the average age for those on antihypertensive (M = 57.63, SD = 5.99, N = 41) was significantly different with the average age (M = 53.90, SD = 8.07, N = 603), t (642) = -2.900, p < .05, two-tailed. Those on antihypertensive were significantly older than those not on antihypertensive.


	BPMeds	N	Mean	Std. Deviation	Std. Error Mean
sysBP	Not on antihypertensive	603	141.6924	25.34485	1.03212
On antihypertensive	41	171.9512	30.08463	4.69843

	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
sysBP	Equal variances assumed	2.625	.106	-7.305	642	.000	-30.259	4.14235	-38.393	-22.125
Equal variances not assumed			-6.290	43.947	.000	-30.259	4.81046	-39.954	-20.564

We performed an independent t-test was in order to compare the mean systolic BP for those on antihypertensive (BPMEDS = 1) and those not on antihypertensive (BPMEDS = 0). Results showed that the mean systolic BP for those on antihypertensive (M = 171.95, SD = 30.08, N = 41) was significantly different with the mean systolic BP (M = 141.69, SD = 25.34, N = 603), t (642) = -7.305, p < .05, two-tailed. Those on antihypertensive had higher mean systolic BP than those not on antihypertensive.

	BPMeds	N	Mean	Std. Deviation	Std. Error Mean
diaBP	Not on antihypertensive	603	86.2736	13.72689	.55900
On antihypertensive	41	97.3902	14.43542	2.25443

	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
diaBP	Equal variances assumed	.003	.955	-5.001	642	.000	-11.117	2.22276	-15.481	-6.752
Equal variances not assumed			-4.786	45.058	.000	-11.117	2.32270	-15.795	-6.439

We performed an independent t-test was in order to compare the mean diastolic BP for those on antihypertensive (BPMEDS = 1) and those not on antihypertensive (BPMEDS = 0). Results showed that the mean diastolic BP for those on antihypertensive (M = 97.39, SD = 14.44, N = 41) was significantly different with the mean diastolic BP (M = 86.27, SD = 13.73, N = 603), t (642) = -5.001, p < .05, two-tailed. Those on antihypertensive had higher mean diastolic BP than those not on antihypertensive.

	Mean	N	Std. Deviation	Std. Error Mean
Pair 1	Initial Weight	151.7000	10	27.42687	8.67314
Final Weight	145.1000	10	24.86162	7.86193

	N	Correlation	Sig.
Pair 1	Initial Weight & Final Weight	10	.980	.000

	Paired Differences	t	df	Sig. (2-tailed)
Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
Lower	Upper
Pair 1	Initial Weight – Final Weight	6.60000	5.81569	1.83908	2.43971	10.76029	3.589	9	.006

A paired-samples t-test was conducted to compare initial weight and final weight of individuals after a weight-loss program. There was a significant difference in the initial weight (M = 151.70, SD = 27.43) and final weight (M = 145.10, SD = 24.86) conditions; t(9) = 3.589, p = 0.006. These results suggest that the weight-loss program really does have an effect weight of individuals. Specifically, our results suggest that before the weight-loss program, the individual’s weight more as compared after the weight loss program.

Age
Levene Statistic	df1	df2	Sig.
2.064	2	25	.148

Age
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	9.254	2	4.627	4.991	.015
Within Groups	23.175	25	.927
Total	32.429	27

Dependent Variable: Age
Bonferroni
(I) Region	(J) Region	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
Lower Bound	Upper Bound
Rural	Sub-Urban	.27500	.45670	1.000	-.8969	1.4469
Urban	-1.02500	.45670	.102	-2.1969	.1469
Sub-Urban	Rural	-.27500	.45670	1.000	-1.4469	.8969
Urban	-1.30000^*	.43058	.017	-2.4049	-.1951
Urban	Rural	1.02500	.45670	.102	-.1469	2.1969
Sub-Urban	1.30000^*	.43058	.017	.1951	2.4049

The mean difference is significant at the 0.05 level.

First, we checked for the homogeneity of variances where we observed the variances to be homogenous (p = 0.148). For the ANOVA test, results showed that there is significant difference in the mean age at completion of year 7 for at least one region. Post-hoc test using Bonferroni showed that significance difference exists in the mean age at completion of year 7 for the urban and Sub-Urban regions.

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