Regression Analysis in Comparing Baseline Examination for Effective Ascertainment

This report presents a regression analysis comparing the baseline examination for effective ascertainment. The analysis includes descriptive statistics, bivariate correlation, and regression analysis on glucose levels. The outcomes provide insights into the relationship between age, blood pressure, BMI, cigarette smoking, and glucose levels. The analysis supports the alternative hypothesis of mean significance differences in baseline examination.

SPSS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
1. Descriptive Statistics...............................................................................................................1
2. Computation of bivariate correlation.......................................................................................2
3.Regression analysis in comparing the baseline examination for effective ascertainment........4
4. Regression analysis on less than or equals to 150 ml glucose.................................................8
5. Significant between baseline and follow-up examination.....................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INTRODUCTION
Analysis of a big data base which in turn requires appropriate ascertainment of various data
base. In the present report, there will be analysis made on several variables which will be
indicative and helpful a per meeting concrete outcomes. There will be tests such as descriptive
analysis, bivariate relationship, regression and ANOVA analysis. Outcomes from this analysis
will help in proper ascertainment of casual serum level and heart study.
1. Descriptive Statistics
N Minimum Maximum Mean Std.
Deviation
Variance
Statistic Statistic Statistic Statistic Std.
Error
Statistic Statistic
Serum total
cholesterol
at baseline
exam
(mmg/dL)
1448 119 600 237.67 1.218 46.338 2147.207
Age at
baseline
exam
(years)
1464 32 69 49.85 .229 8.753 76.621
Systolic
blood
pressure at
baseline
exam
(mmHg)
1464 90.0 295.0 133.157 .6028 23.0637 531.932
Diastolic
blood
pressure at
baseline
exam
(mmHg)
1464 48.0 141.0 83.115 .3219 12.3181 151.736
1

Current
cigarette
smoking at
baseline
exam
1464 0 1 .51 .013 .500 .250
Number of
cigarettes
smoked
each day at
baseline
exam
1451 0 60 9.09 .308 11.744 137.916
Body Mass
Index at
baseline
exam
(kg/m^2)
1458 15.96 45.80 25.7817 .10834 4.13681 17.113
Use of anti-
hypertensive
medication
at baseline
exam
1441 0 1 .03 .004 .160 .026
Casual
serum
glucose at
baseline
exam
(mg/dL)
1338 40 394 84.96 .974 35.636 1269.923
Number of
days since
baseline
exam
1308 1633 2514 2176.47 2.030 73.400 5387.623
Valid N
(listwise) 1164
2. Computation of bivariate correlation
Bivariate correlation
2

Casual
serum
glucose
at
baseline
exam
(mg/dL)
Age at
baseline
exam
(years)
Body
Mass
Index at
baseline
exam
(kg/m^2)
Education
level
Systolic
blood
pressure
at
baseline
exam
(mmHg)
Diastolic
blood
pressure
at
baseline
exam
(mmHg)
Casual serum
glucose at
baseline exam
(mg/dL)
Pearson
Correlation 1 .151** .107** -.059* .185** .061*
Sig. (2-
tailed) .000 .000 .033 .000 .026
N 1338 1338 1333 1300 1338 1338
Age at baseline
exam (years)
Pearson
Correlation .151** 1 .143** -.192** .412** .233**
Sig. (2-
tailed) .000 .000 .000 .000 .000
N 1338 1464 1458 1425 1464 1464
Body Mass
Index at
baseline exam
(kg/m^2)
Pearson
Correlation .107** .143** 1 -.132** .322** .366**
Sig. (2-
tailed) .000 .000 .000 .000 .000
N 1333 1458 1458 1420 1458 1458
Education level
Pearson
Correlation -.059* -.192** -.132** 1 -.133** -.043
Sig. (2-
tailed) .033 .000 .000 .000 .104
N 1300 1425 1420 1425 1425 1425
Systolic blood
pressure at
baseline exam
(mmHg)
Pearson
Correlation .185** .412** .322** -.133** 1 .785**
Sig. (2-
tailed) .000 .000 .000 .000 .000
N 1338 1464 1458 1425 1464 1464
Diastolic blood
pressure at
baseline exam
(mmHg)
Pearson
Correlation .061* .233** .366** -.043 .785** 1
Sig. (2-
tailed) .026 .000 .000 .104 .000
N 1338 1464 1458 1425 1464 1464
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
3

3.Regression analysis in comparing the baseline examination for effective ascertainment
Hypothesis:
Null hypothesis: There is no mean significance differences between age, BP medicines,
BMI, cigarette smokers and Glucose in baseline examination.
Alternative hypothesis: There are mean significance differences between age, BP
medicines, BMI, cigarette smokers and Glucose in baseline examination.
More than 150 ml:
Regression
Descriptive Statistics
Mean Std. Deviation N
GLUCOSE.1 270.44 65.017 36
AGE.1 55.47 6.784 36
SYSBP.1 151.764 32.5996 36
DIABP.1 86.819 16.5621 36
BMI.1 28.348611 5.2059791 36
CURSMOKE.1 .33 .478 36
Correlations
GLUCOSE.
1
AGE.
1
SYSBP.
1
DIABP.
1
BMI.
1
CURSMOKE.
1
Pearson
Correlatio
n
GLUCOSE.1 1.000 .183 -.004 -.087 -.210 .039
AGE.1 .183 1.000 .137 -.047 -.186 -.235
SYSBP.1 -.004 .137 1.000 .780 .511 -.240
DIABP.1 -.087 -.047 .780 1.000 .620 -.169
BMI.1 -.210 -.186 .511 .620 1.000 .032
CURSMOKE.
1 .039 -.235 -.240 -.169 .032 1.000
Sig. (1-
tailed)
GLUCOSE.1 . .143 .490 .306 .110 .410
AGE.1 .143 . .213 .392 .138 .084
SYSBP.1 .490 .213 . .000 .001 .080
DIABP.1 .306 .392 .000 . .000 .162
BMI.1 .110 .138 .001 .000 . .426
4

CURSMOKE.
1 .410 .084 .080 .162 .426 .
N
GLUCOSE.1 36 36 36 36 36 36
AGE.1 36 36 36 36 36 36
SYSBP.1 36 36 36 36 36 36
DIABP.1 36 36 36 36 36 36
BMI.1 36 36 36 36 36 36
CURSMOKE.
1 36 36 36 36 36 36
Model Summary
Model R R
Square
Adjusted
R Square
Std.
Error of
the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .289a .084 -.069 67.222 .084 .548 5 30 .738
a. Predictors: (Constant), CURSMOKE.1, BMI.1, AGE.1, SYSBP.1, DIABP.1
b. Dependent Variable: GLUCOSE.1
ANOVA
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 12388.785 5 2477.757 .548 .738b
Residual 135564.104 30 4518.803
Total 147952.889 35
a. Dependent Variable: GLUCOSE.1
b. Predictors: (Constant), CURSMOKE.1, BMI.1, AGE.1, SYSBP.1, DIABP.1
Coefficients
Model Unstandardize
d Coefficients
Standardize
d
Coefficients
t Sig. 95.0%
Confidence
Interval for B
Correlations
5

B Std.
Error
Beta Lowe
r
Boun
d
Upper
Bound
Zero
-
orde
r
Partia
l
Par
t
1
(Constant) 241.37
5
131.34
7
1.83
8
.07
6
-
26.87
1
509.62
1
AGE.1 1.352 1.819 .141 .743 .46
3
-
2.364 5.068 .183 .134 .13
0
SYSBP.1 .295 .589 .148 .502 .61
9 -.907 1.498 -.004 .091 .08
8
DIABP.1 -.090 1.219 -.023 -.07
3
.94
2
-
2.578 2.399 -.087 -.013 -.01
3
BMI.1 -3.107 2.893 -.249
-
1.07
4
.29
1
-
9.016 2.802 -.210 -.192 -.18
8
CURSMOKE
.1 15.233 25.333 .112 .601 .55
2
-
36.50
3
66.969 .039 .109 .10
5
a. Dependent Variable: GLUCOSE.1
Coefficient Correlations
Model CURSMOKE.
1
BMI.1 AGE.1 SYSBP.1 DIABP.1
1
Correlations
CURSMOKE.
1 1.000 -.150 .174 .132 .078
BMI.1 -.150 1.000 .193 -.136 -.372
AGE.1 .174 .193 1.000 -.266 .154
SYSBP.1 .132 -.136 -.266 1.000 -.673
DIABP.1 .078 -.372 .154 -.673 1.000
Covariances
CURSMOKE.
1 641.742 -11.005 8.030 1.968 2.411
BMI.1 -11.005 8.372 1.017 -.232 -1.310
AGE.1 8.030 1.017 3.310 -.285 .342
SYSBP.1 1.968 -.232 -.285 .347 -.482
DIABP.1 2.411 -1.310 .342 -.482 1.485
a. Dependent Variable: GLUCOSE.1
6

Interpretation: In analyzing the above listed measurement for determining the
relationship between the variables. Thus, in this respect the outcome derived from operations
which can be ascertained as the mean value of the glucose which are more than 150 ml are
270.44. the mean value of age as 55.47, sysbp as 151.764, Diabp as 86.819, BMI as 28.34 and
cigarette smokers as 0.33. Along with this, The R square of the outcomes had been analyzed here
which represent the R value as 0.084 that is, 8.4%. It determines that these variables have 8.45 of
relationship among their data base.
ANOVA analysis of the outcome that defines that the significant value of the outcomes is
0.738 which can be analyzed on the level of p value. It is more than 0.05 as p value level which
determines that there are strong evidences against null hypothesis (Sivam and et.al., 2018). This
in turn reflects that, there will be acceptance to the alternative hypothesis which means, there are
mean significance differences between age, BP medicines, BMI, cigarette smokers and Glucose
in baseline examination.
Less than 150 ml:
Regression
Descriptive Statistics
Mean Std. Deviation N
GLUCOSE.1 79.64 13.166 1296
AGE.1 49.64 8.715 1296
SYSBP.1 132.318 22.3729 1296
DIABP.1 82.878 12.2494 1296
BMI.1 25.690208 4.0249060 1296
CURSMOKE.
1 .51 .500 1296
Correlations
GLUCOSE.
1
AGE.
1
SYSBP.
1
DIABP.
1
BMI.
1
CURSMOKE.
1
Pearson
Correlatio
n
GLUCOSE.1 1.000 .116 .126 .033 .060 -.044
AGE.1 .116 1.000 .399 .235 .138 -.197
SYSBP.1 .126 .399 1.000 .790 .307 -.127
DIABP.1 .033 .235 .790 1.000 .361 -.117
BMI.1 .060 .138 .307 .361 1.000 -.144
7

CURSMOKE.
1 -.044 -.197 -.127 -.117 -.144 1.000
Sig. (1-
tailed)
GLUCOSE.1 . .000 .000 .116 .015 .056
AGE.1 .000 . .000 .000 .000 .000
SYSBP.1 .000 .000 . .000 .000 .000
DIABP.1 .116 .000 .000 . .000 .000
BMI.1 .015 .000 .000 .000 . .000
CURSMOKE.
1 .056 .000 .000 .000 .000 .
N
GLUCOSE.1 1296 1296 1296 1296 1296 1296
AGE.1 1296 1296 1296 1296 1296 1296
SYSBP.1 1296 1296 1296 1296 1296 1296
DIABP.1 1296 1296 1296 1296 1296 1296
BMI.1 1296 1296 1296 1296 1296 1296
CURSMOKE.
1 1296 1296 1296 1296 1296 1296
ANOVA
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 7403.382 5 1480.676 8.799 .000b
Residual 217067.895 1290 168.270
Total 224471.277 1295
a. Dependent Variable: GLUCOSE.1
b. Predictors: (Constant), CURSMOKE.1, DIABP.1, AGE.1, BMI.1, SYSBP.1
Coefficient Correlations
Model CURSMOKE.1 DIABP.1 AGE.1 BMI.1 SYSBP.1
1
Correlations
CURSMOKE.1 1.000 .029 .163 .101 -.008
DIABP.1 .029 1.000 .152 -.204 -.760
AGE.1 .163 .152 1.000 -.032 -.353
BMI.1 .101 -.204 -.032 1.000 -.019
SYSBP.1 -.008 -.760 -.353 -.019 1.000
Covariances CURSMOKE.1 .549 .001 .006 .007 .000
DIABP.1 .001 .002 .000 -.001 -.001
AGE.1 .006 .000 .002 .000 .000
8

BMI.1 .007 -.001 .000 .009 -5.115E-
005
SYSBP.1 .000 -.001 .000 -5.115E-
005 .001
a. Dependent Variable: GLUCOSE.1
Interpretation: By considering the glucose level of the data base is less than 150 ml on
which regression analysis had been made. Thus, mean value of glucose as 79.64, age as 49.64,
SYSBP as 132.318, DIABP as 82.878, BMI as 25.69 and cigarette smoker as 0.51. The
significance value of the outcomes has been determined here are less than the p value of the
operations. thus, the significance value had been analyzed here are 0.000 which is less than 0.05.
therefore, there will be acceptance to the null hypothesis that means, there is no mean
significance differences between age, BP medicines, BMI, cigarette smokers and Glucose in
baseline examination (Okada and et.al., 2018).
4. Regression analysis on less than or equals to 150 ml glucose
Descriptive Statistics
Mean Std. Deviation N
GLUCOSE.1 79.69 13.305 1297
AGE.1 49.65 8.717 1297
SYSBP.1 132.370 22.4431 1297
DIABP.1 82.908 12.2940 1297
CURSMOKE.
1 .51 .500 1297
BMI.1 25.704534 4.0562948 1297
Correlations
GLUCOSE.
1
AGE.
1
SYSBP.
1
DIABP.
1
CURSMOKE.
1
BMI.
1
Pearson
Correlatio
n
GLUCOSE.1 1.000 .119 .137 .046 -.040 .078
AGE.1 .119 1.000 .400 .237 -.196 .141
SYSBP.1 .137 .400 1.000 .791 -.124 .314
DIABP.1 .046 .237 .791 1.000 -.114 .368
CURSMOKE.
1
-.040 -.196 -.124 -.114 1.000 -.139
9

BMI.1 .078 .141 .314 .368 -.139 1.000
Sig. (1-
tailed)
GLUCOSE.1 . .000 .000 .049 .076 .003
AGE.1 .000 . .000 .000 .000 .000
SYSBP.1 .000 .000 . .000 .000 .000
DIABP.1 .049 .000 .000 . .000 .000
CURSMOKE.
1 .076 .000 .000 .000 . .000
BMI.1 .003 .000 .000 .000 .000 .
N
GLUCOSE.1 1297 1297 1297 1297 1297 1297
AGE.1 1297 1297 1297 1297 1297 1297
SYSBP.1 1297 1297 1297 1297 1297 1297
DIABP.1 1297 1297 1297 1297 1297 1297
CURSMOKE.
1 1297 1297 1297 1297 1297 1297
BMI.1 1297 1297 1297 1297 1297 1297
Model Summary
Model R R
Square
Adjusted
R Square
Std.
Error of
the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .188a .036 .032 13.092 .036 9.508 5 1291 .000
a. Predictors: (Constant), BMI.1, CURSMOKE.1, AGE.1, DIABP.1, SYSBP.1
b. Dependent Variable: GLUCOSE.1
Coefficient Correlations
Model BMI.1 CURSMOKE.1 AGE.1 DIABP.1 SYSBP.1
1
Correlations
BMI.1 1.000 .097 -.033 -.207 -.021
CURSMOKE.1 .097 1.000 .162 .028 -.009
AGE.1 -.033 .162 1.000 .152 -.353
DIABP.1 -.207 .028 .152 1.000 -.760
SYSBP.1 -.021 -.009 -.353 -.760 1.000
Covariances BMI.1 .009 .007 .000 -.001 -5.668E-
005
CURSMOKE.1 .007 .558 .006 .001 .000
AGE.1 .000 .006 .002 .000 .000
10

DIABP.1 -.001 .001 .000 .003 -.001
SYSBP.1 -5.668E-
005 .000 .000 -.001 .001
a. Dependent Variable: GLUCOSE.1
Interpretation: On the basis of above listed analysis which represent that, glucose level is
should be less than and equals to 150 ml. the descriptive statistics of the outcomes have been
analyzed here which represents the mean value of the variables (Sommet and Morselli, 2017).
Mean value of glucose as 79.69, age as 49.65, SYSBP as 132.370, DIABP as 82.908, Cigarette
smoker as 0.51 and BMI as 25.70. Along with this, considering ANOVA table on which R
square of data base has been obtained as 0.032 that mean 3.2%. Similarly, the significance value
had been derived here is less than the p value that is 0.000. in relation with this there will be
acceptance to the null hypothesis which represents that, there is no mean significance differences
between age, BP medicines, BMI, cigarette smokers and Glucose in baseline examination (Jin
and Qi, 2018).
5. Significant between baseline and follow-up examination
One-way
ANOVA
GLUCOSE.1
Sum of
Squares
df Mean Square F Sig.
Between Groups 80883.921 17 4757.878 15.225 .199
Within Groups 312.500 1 312.500
Total 81196.421 18
Interpretation: As per analyzing the above listed ANOVA table which have determined the
outcomes as Significant value as 0.199 which is more than level of p value. Thus, on which there
will be acceptance to the alternative hypothesis (Zhang and Li, 2018). It can be said that, there
are mean significance differences between age, BP medicines, BMI, cigarette smokers and
Glucose in baseline examination.
11

CONCLUSION
On the basis of above report, there had been determination of various outcomes which
represent relationship among variables. Report is consisting of statistical tools which were
analysed such as Descriptive statistics, Regression, Bivariate relationship as well as ANOVA
table had been run.
12

REFERENCES
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a Jupiter Trojan asteroid using the solar power sail. Planetary and Space Science, 161,
pp.99-106.
Sivam, S. S. S. and et.al., 2018. Grey Relational Analysis and Anova to Determine the Optimum
Process Parameters for Friction Stir Welding of Ti and Mg Alloys. Periodica
Polytechnica Mechanical Engineerin. 62(4). pp.277-283.
Sommet, N. and Morselli, D., 2017. Keep calm and learn multilevel logistic modeling: A
simplified three-step procedure using Stata, R, Mplus, and SPSS. International Review
of Social Psychology. 30(1).
Zhang, J. and Li, Y., 2018, June. Cluster Analysis of the Rural Income in Luoyang City by
SPSS. In 2018 IEEE/ACIS 17th International Conference on Computer and Information
Science (ICIS) (pp. 786-789). IEEE.
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