Design and Analysis

This document discusses various methods and tools used in design and analysis, such as SEM, GLM, Multilevel modeling, Regression analysis, Anova, and Correlation analysis. It explains how these methods are used to examine the interdependency of variables, test hypotheses, and improve models for better results.

Design and Analysis

Table of Content
Q 1.1............................................................................................................................................2
Q 1.2............................................................................................................................................2
Q 1.3............................................................................................................................................2
Q 1.4............................................................................................................................................2
Q 1.5............................................................................................................................................2
Q 1.6............................................................................................................................................3
Q 1.7............................................................................................................................................3
Q 1.8............................................................................................................................................3
Q 1.9............................................................................................................................................3

Q 1.1
To conduct the study, SEM and GLM and Multilevel modeling as an analytical method
are used.
Q 1.2
Before running any analyzis, Coding is perform for the easiness of work.
Q 1.3
In this, data pertaining to standard deviation, variances, average and median will be collected
about control, user and competitive feedback group.
Q 1.4
For conducting the test, Regression analysis is used that will help to determine the
interdependency of the two variables and by developing the fitness online application will assist
to examine the interdependency of two variables (Schroeder, Sjoquist and Stephan, 2016).
For this, rain and weather are also taken as a parameter for the study in order to conduct
it in better manner.
For model improvement, Multicolinearity analysis is used that in which different factors
are also added in order to get the better response.
Q 1.5
For test the hypothesis 3, Anova is used which clearly determine the hypothesis is
accepted or rejected such that it is actually used to examine the statistically significant difference
between two or more independent variable (Leonzio, 2019).
For extra parameter, some of the people are selected who regularly go to gym. So this
will help to collect the data better manner.
Multicolinearity analysis, is used as a model improvement against the previous model.
No, there is no specific decision or choice need to make.

Q 1.6
For hypothesis 4, correlation analysis is used that change the value of one variable to the
another such that when there is a negative variable that means one value increases while another
is decreases and vice versa (Reimann and et.al., 2017). Such that if the correlation is comes
positive which means there is a positive relationship with the number of steps taken on the
subsequent days.
For the extra parameter, pearson is used that will help to generate the best results and
gain better outcomes.
Process correlation analysis is used as an improvement model that is used against the
correlation test under Multilevel analytic model.
No, there is no specific changes needs to make under the hypothesis 5
Q 1.7
For hypothesis 5, regression analysis is used that will help to analyze the relationship
between two variables.
P is used as an extra parameter for this hypothesis.
For model improvement, Multicolinearity analysis is used against the previous model.
By proper coding, the estimated change over time in steps for the each group.
Q 1.8
Increase is number of steps as per weather is likely to be flawed because the demand of
customers is continuously changes as per the weather changes and that is why, this hypothesis is
consider as likely to be flawed.
Q 1.9
By using regression tool, hypothesis 2 is change the analysis and get the results. Such that
earlier, this tool is used because it describe the relationship between two variables or more.

REFERENCES
Books and Journals
Leonzio, G., 2019. ANOVA analysis of an integrated membrane reactor for hydrogen production
by methane steam reforming. International Journal of Hydrogen Energy. 44(23). pp.11535-
11545.
Reimann, C. and et.al., 2017. A new method for correlation analysis of compositional
(environmental) data–a worked example. Science of the Total Environment. 607. pp.965-
971.
Schroeder, L. D., Sjoquist, D. L. and Stephan, P. E., 2016. Understanding regression analysis:
An introductory guide (Vol. 57). Sage Publications.