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What are the sources which effect the growth study
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Table of Contents
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Introduction……………………………………
Ø
Research Question…………………………….
Ø
Variable and Their Definitions………………..
Ø
Source
of Data…………………………………
Ø
Quality
of Data…………………………………
Ø
Model…………………………………………..
Ø
Inferential Statistics…………………………….
Ø
ETA
Test……………………………………….
Ø
Correlation Test…………………………………
Pearson
Correlation…………………………
Ø
Regression Analysis………………………………
Types
of Regression……………………………..
Ø
Conclusion…………………………………………….
Ø
Reference………………………………………………
The Study of pre-and Postnatal Growth Study in Humans:
Introduction.
|
This review is divided in several items. A brief
introduction on the characterization of the
growth processes is made; the ways of assessing
fetal development and well-being, the factors
acting on fetal growth and birth weight, the
causes and post-natal consequences of
prematurity and intrauterine growth retardation
are discussed in the first part. The following
items deal mainly with: the normal pattern of
growth from birth to puberty according to sex,
race, and nutritional status, with special
mention to pubertal changes; methods for
predicting adult height from skeletal age; the
effect of hormones during pre- and post-natal
life; and the genetics of adult stature. |
|
The remainder of this review deals with genetic
causes of growth abnormalities. Constitutional
delay of growth, familial short stature,
hypothalamic-pituitary dwarfism, skeletal
dysplasias and many genetic syndromes presenting
intrauterine growth retardation are listed.
Aneuploidy effects on human growth are
extensively reviewed, and usual growth patterns
in Down and Ullrich-Turner syndrome patients as
well as other sex aneuploid individuals and
mosaics are fully described. The influences of X
and Y chromosomes on growth and maturation are
also discussed. Finally, some remarks are made
about overgrowth syndromes. |
Research Question:
What
are the source which effect the growth study.
Objective of the Study:
The
objective of the study is to explore the determinents growth study.
Variables and Their Definations:
Dependent Variable:
Defination.
A variable representing observed values of an experiment or simulation by a
model. Dependent variables may contain statistical weights.
1
Distance : This variable is used as
a dependent variable.
Independent Variables:
Defination.
A factor for which the researcher either selects or manipulates at least two
levels in order to determine its effect on behavior.
1
Gender: This variable is used as a
independent nominal variables.
2
Subject:
This variable is used as a independent scale variable.
3
Age:
This variable is also used as a independent scale variable.
Source
of Data:
www.spss.com/
programm files/ samples/ Growth study
Quality
of the Data:
Quality of the data is to the mark. No value of any variable is missing.
Model
Summary For Variables.
Distance
Subject
Gender
Age
Descriptive Analysis:
I used the scatter diagrams to show the relationship between dependent
and independent variables.
Justification of the Method:
Keeping the objective of the study in mind, diagrams present the idea
about the relationship between dependent and independent variables.
Frequency Distribution.
Table -
1
|
Statistics |
||
|
Gender |
|
|
|
N |
Valid |
108 |
|
Missing |
0 |
|
|
Gender |
|||||
|
|
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
|
Valid |
Girl |
44 |
40.7 |
40.7 |
40.7 |
|
Boy |
64 |
59.3 |
59.3 |
100.0 |
|
|
Total |
108 |
100.0 |
100.0 |
|
|
Interpretation:
Table
-1 shows that the freqency table of the nominal variable. There is a frequency
table that shows the numbers of boys and girls. Here 44 said girls and 64 said
are boys. There is no any other missing value in that table of Gender variable.
Given
this data set, it would be accurate to say that of those 40.7% were girls and
59.3% were boys.
Table -
2
Summary
Statistics
|
|
Distance |
Subject |
Age |
|
|
No. of
Observations |
Valid
|
108 |
108 |
108 |
|
Missing |
0 |
0 |
0 |
|
|
Mean |
24.023 |
14.00 |
11.00 |
|
|
Median
|
23.769 |
14.00 |
11.00 |
|
|
Std. Deviation |
2.9286 |
7.825 |
2.246 |
|
|
Variance |
8.577 |
61.234 |
5.047 |
|
|
Skewness |
.295 |
.000 |
|
|
|
Std. Error of Skewness |
.233 |
.233 |
.233 |
|
|
Range |
15.0 |
26 |
6 |
|
|
Minimum |
16.5 |
1 |
8 |
|
|
Maximum |
31.5 |
27 |
14 |
|
Interpretation:
Table 2
shows the summary statistics of the variables used in the growth study. These
summary statistics reflect the overall picture of the variables. All variables
have the postive growth study on average. Among all variables Subject variable
shows the maximun value of standard deviation which show the large variability
in the growth study of human.
Diagram-1
Bar
Chart.
Interpretation.
In
Diagram-1 I take variable gender at x-axis and count variable take on y-axis
which shows the value of variable gender starting from 0 and end the point of
41. In gender variable of boys have a maximum value till 60. And also count
variable at on y-axis which is already exits. But the overall bar of both
variables are not equal.
Bar graphs are a very common type of graph best suited for a qualitative
independent variable. Since there is no uniform distance between levels of a
qualitative variable, the discrete nature of the individual bars are well suited
for this type of independent variable. Though you can extract trends between
bars (e.g., they are gradually getting longer or shorter), you cannot calculate
a slope from the heights of the bars.
Diagram-2
Histogrms Graph.
Interpretation:
In Diagram-2 the frequencies are
( number of subjects) which
is used on x-axis to show the the diagram of histogram. The value of
subject variable in histgram graph shows the normal curve which is shows
the postive skewed. The mean value of the subject variable is 14 and has a 7.825
std. deviation during the N=108.
Histogram:
Columns/bars touch; useful for larger sets of data points, typically used for
frequency distributions.
Diagram-3
Scatter
Plot.
Subject….
Interpretation:
In Diagram-3 the relationship between Distance and subjects.
In
scatter plot I take the distance variable in x-axis as a dependent variable and
I take subject as a independent at y-axis. The relationship between distance and
subject shows the lianer curve which shows the postive relationship between two
variables.
Diagram-4
Scatter
Plot.
Age in
Years.
Interpretation.
In Diagram-4 the relationship between Distance and Age in years.
In
scatter plot I take the distance variable in x-axis as a dependent variable and
I take age in years as a independent at y-axis. The relationship between
distance and subject shows the lianer curve which shows the postive relationship
between two variables.
The out
put shows a scatter plot for two scale variables. Distance and Subject both are
scale variables
A scatter plot or scattergraph is a type of
mathematical diagram
using
Cartesian coordinates
to display values for two
variables
for a set of data.
The data is displayed as a collection of points, each having the value of one
variable determining the position on the horizontal axis and the value of the
other variable determining the position on the vertical axis. This kind of
plot
is also called a scatter chart,
scatter diagram and
scatter graph.
A scatter plot is used when a variable exists that is under the control of the
experimenter. If a parameter exists that is systematically incremented and/or
decremented by the other, it is called the
control parameter or
independent variable
and is customarily plotted along the horizontal axis. The measured or
dependent variable
is customarily plotted along the vertical axis.
Line graphs provide an excellent way to map independent and dependent variables
that are both quantitative. Scatter plots are similar to line graphs in that
they start with mapping quantitative data points. The difference is that with a
scatter plot, the decision is made that the individual points should not be
connected directly together with a line but, instead express a trend.
Box
Plot.
Interpertations:
In the
above diagram there are three vriables which shows the different values. The
variables Distance have a great valve as compared to other variables. The valve
of upper quartile of Distance variable is 33%
approxmatly. The value of
lower quartile of subject variable is approx 3%. Which shows the lowest value of
subject varibles. The value of age in years variable has a upper quartile value
is 14% which is very low as compared to other variables.
At the
end I want to say that… The variable of Distance is overall better than other
two variables according to diagram of Box Plot.
Reference:
INFERENTIAL STATISTICS:
Inferential statistics are used to make inference about a population from
a sample based on the statistical relationships or differences between two or
more variables using statistical tests with the assunption that sample is random
in order to generalize or make predictions about the future.
Types
of Test Used in Inferential Statistics.
·
Non
Parametric Test
·
Parametric Test.
What is
Non Parametric Test.
Non parametric tests are the statistical tests that are used in when the
level of measurement is nominal or ordinal. E.g chi-square or Kendall’s tau-b
And
when assumptions about normal distribuation in the population is not met e.g
spearman correlation.
What is
Parametric Test.
In Parametric Test that tests are involved
Correlation
Regression
T-Test
Applicable Non Parametric Test.
Ø
Eta
Applicable Parametric Tests.
Ø
Correlation
Ø
Regression
ETA
Test.
Hypothesis.
H0=there is no relationship between gender and distance.
H1=there is relationship between gender and distance
|
Case Processing Summary |
||||||
|
|
Cases |
|||||
|
|
Valid |
Missing |
Total |
|||
|
|
N |
Percent |
N |
Percent |
N |
Percent |
|
Distance (mm) from center of pituitary to pteryo-maxillary
fissure * Gender |
108 |
100.0% |
0 |
.0% |
108 |
100.0% |
|
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure * Gender Crosstabulation |
||||
|
Count |
|
|
|
|
|
|
|
Gender |
Total |
|
|
|
|
1 |
2 |
|
|
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure |
16.5 |
0 |
1 |
1 |
|
17 |
1 |
0 |
1 |
|
|
19 |
1 |
1 |
2 |
|
|
19.5 |
0 |
1 |
1 |
|
|
20 |
1 |
3 |
4 |
|
|
20.5 |
0 |
2 |
2 |
|
|
21 |
2 |
3 |
5 |
|
|
21.5 |
5 |
4 |
9 |
|
|
22 |
3 |
1 |
4 |
|
|
22.5 |
6 |
1 |
7 |
|
|
23 |
2 |
9 |
11 |
|
|
23.5 |
3 |
4 |
7 |
|
|
24 |
4 |
2 |
6 |
|
|
24.5 |
2 |
6 |
8 |
|
|
25 |
4 |
2 |
6 |
|
|
25.5 |
4 |
2 |
6 |
|
|
26 |
3 |
4 |
7 |
|
|
26.5 |
3 |
1 |
4 |
|
|
27 |
1 |
1 |
2 |
|
|
27.5 |
2 |
1 |
3 |
|
|
28 |
2 |
2 |
4 |
|
|
28.5 |
0 |
1 |
1 |
|
|
29 |
1 |
0 |
1 |
|
|
29.5 |
0 |
1 |
1 |
|
|
30 |
1 |
0 |
1 |
|
|
31 |
1 |
2 |
3 |
|
|
31.5 |
0 |
1 |
1 |
|
|
Total |
52 |
56 |
108 |
|
|
Directional Measures |
|||
|
|
|
|
Value |
|
Nominal by Interval |
Eta |
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure Dependent |
.040 |
|
Gender Dependent |
.492 |
||
Interpretation
Eta is use to check the strength of relationship between gender variables and
Distance variable. The value of dependent variable is less than 0.33this mean
that there is weak relationship between both variables. It means gender has
small effect on distance.
Correlation.
(scale versus scale)
H0=there is no relationship between distance and subject
H1=there is relationship between the subject and the distance.
Here to
check the normality of variables used the scatter plot graph.
There
is a difference between two variables is .002 thus why here pearson correlation
is applied because the difference is less than 0.05
Here
applied the Pearson correlation because the relationship is linear.
Pearson
Correlation.
Scatter
Plot:
R Square = Sq Quadratic – Sq Linear
R Square = (0.089 – 0.087) = .002
P = 0.002 is less than 0.05 so we will use Pearson correlation.
Interpretation:
I draw a scatter plot graph to check linear or non linear. This graph is
consists of both scale variables which shows the relationship. The difference
between both sq quadratic and sq linear is a .002. this shows that the relation
is Pearson correlation because the value is less than 0.05.
Correlation Test.
Correlation is a statistical process that determines the mutual (reciprocal)
relationship between two or mare variables which are thought to be mutually
related in a way that systematic changes in the value of one variable are
accompanied by systematic changes in the other and vice versa.
|
Correlations |
|||
|
|
|
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure |
Subject |
|
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure |
Pearson Correlation |
1 |
.295** |
|
Sig. (2-tailed) |
|
.002 |
|
|
N |
108 |
108 |
|
|
Subject |
Pearson Correlation |
.295** |
1 |
|
Sig. (2-tailed) |
.002 |
|
|
|
N |
108 |
108 |
|
|
**. Correlation is significant at the 0.01 level
(2-tailed). |
|
||
Interpretation:
To investigate if there was a statistically significant association
between distance and subject a correlation was computed. Both variables were
approximately normal there is a linear relationship between them hence fulfuling
the assumptions for Pearson’s correlation.
Thus the pearson’s r is calucated, r =.295, p<0.05. so p value is .002.
relating this that there is highly significant relationship between variables.
The positive sign of the pearson’s test value shows that there is positive
relationship.
Regression Analysis.
|
|
Regression Analysis is used to measure the relationship between two or more
variables. One variable is called dependent variable and the other is called
independents.
It is
used to check that due to one unit changes in the independent variables how much
changes occurs in dependent variables.
Types
of Regression:
1)
Simple
Regression
2)
Multiple Regression
Multiple Regression:
Multiple regression is used to check the contribution of independent varible in
the dependent variable if the independent variables are more than one.
Here I
use only Multiple regression because I used one dependent and three
independents.
Hupothesis
H0=there is no relationship netween subject and distance
H1=there is relationship between subject and distance
H0=
there is no relationship netween distance and age
H1=there is relationship between distance and age
H1 H0=
there is no relationship netween distance and gender
H1=there is relationship between subject and distance gender.
|
Descriptive Statistics |
|||
|
|
Mean |
Std. Deviation |
N |
|
Distance (mm) from center of
pituitary to pteryo-maxillary
fissure |
24.023 |
2.9286 |
108 |
|
Subject |
14.00 |
7.825 |
108 |
|
Gender |
1.52 |
.502 |
108 |
|
Age in years |
11.00 |
2.246 |
108 |
|
Model Summary |
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.586a |
.344 |
.325 |
2.4068 |
|
a. Predictors: (Constant), Age in years,
Subject, Gender |
||||
|
ANOVAb |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
315.263 |
3 |
105.088 |
18.142 |
.000a |
|
Residual |
602.429 |
104 |
5.793 |
|
|
|
|
Total |
917.692 |
107 |
|
|
|
|
|
a. Predictors: (Constant), Age in years,
Subject, Gender |
|
|
||||
|
b. Dependent Variable: Distance (mm) from center
of pituitary to pteryo-maxillary fissure |
||||||
|
Coefficientsa
|
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
15.174 |
1.469 |
|
10.326 |
.000 |
|
Subject |
.110 |
.030 |
.295 |
3.713 |
.000 |
|
|
Gender |
.024 |
.465 |
.004 |
.051 |
.959 |
|
|
Age in years |
.661 |
.104 |
.507 |
6.364 |
.000 |
|
|
a.
Dependent Variable: Distance (mm) from center of
pituitary to pteryo-maxillary fissure.
|
||||||
|
Equation:
Y = a+bx1+cx2+dx3
|
||||||
Y= 15.174+ .110X1+
.024X2+ .661X3
Y = dependent variable
A =
Constant
b, c, d = Slop of coefficients
X1, X2,X3= Independent variable
Interpretation:
Simultaneously multiple regression was conducted to investigate the best
predictors of distancewhich is dependent variable. The means, standard
deviation, and inter correlations can be found in table. The combination of
variables to predict distance form subject, age of store location and gender was
statistically significant, F=18.142, p < 0.05. The beta coefficients are
presented in last table. Note that subject and age significantly predict
distance when all four variables are included. The adjusted R value is .325.
And according to ANOVA Table the
significant value is .000 which is less than .05 so the model is good fit.
Conclusion
According the research there are three types of independent variables
Distance, Age , and gender. And distance is a dependent variable. Independent
variables have direct effect on the dependent variables we can enhance our
ability to distance our in the study through subject. Subject
plays an important role for the
improvement of study.
Reference
Testmarket.sav from SPSS
statistical software ver.16 sample file(s)
www.answers.com/topic/independentvariable
www.answers.com/topic/five-number-summary
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