Answer & Explanation:Module 7/2
Considering the variables and design that you described in
the first discussion question in this module, what information would a multiple
regression analysis provide you? Why would this be significant to your
research? (Research support is not required for this question.)
Subsequently, multiple regressions
models stipulate various results useful in the course of deducing precise and
informed conclusions. Besides, the models reveal the appropriateness of the
information in the study; thus, reflecting the actual situation being
investigated by the research. First, a multiple regression provides information
on the R-squared value, which depicts data variability and the dispersion of
the data from the mean; thus, describing the fitness of the information. High
R-squared score depicts that the information is close to the mean, while the
lower value reveals higher dispersion of the data. Additionally, the model
provides information on the standard error of the variables in the study as
well as the number of the observed elements.
Similarly, multiple regressions
provide essential information on ANOVA analysis, which gives the value of
degrees of freedom that reveal the numbers at which the model could shift.
Besides, the ANOVA output from a regression analysis depicts a very important
figure on F distribution as well as the value of the probability also knows as
the P-Value in statistics. Additionally, the regression analysis output reveals
vital information regarding the model, such as the coefficient values that
represents the constants in the model, in respect to the intercept as well as
the data variables. Nonetheless, the multiple regression models depict
important information regarding the probability value for both the intercept
and variables
essential in the testing for the significance of the elements.
The
Significance of Regression Information in Research
Moreover, the regression information
is imperative in the research because it enhances the ability to make a
conclusion regarding the null hypothesis, through a test of statistical
significance of the variables. Notably, if the probability value in the regression
output is less than the assumed P- Value at a specified confidence level say
95%, we reject or fail to accept the null hypothesis statement and conclude
that there was statistical significance between the variables. On the other
hand, if the calculated p-value by the model is greater than the assumed
P-value say 0.05 at 95% confidence level then we can conclude that there was no
statistical significance and accept or fail to reject the null hypothesis.
Therefore, in the course of conducting the dissertation, the multiple
regression information would play an important role in rejecting or accepting
the null hypothesis, as well as making an informed conclusion regarding the
topic of the study.
In brief, while carrying out a
statistical research, multiple regression plays a crucial role in the process
of calculating the probability value, which is imperative in the process of
testing the hypothesis. Indeed, statistical research would intensify the
capacity to make informed decisions regarding a particular experiment in the
study.
Reference
Jackson, S. L. (2015). Research
methods and statistics: A critical thinking approach. Cengage Learning.
This is what the prof said to me in response ΒRandall, thanks for the detailed post. See my reply about
R-squared. It looks like you only picked up part of explanation from Jackson.ΒWhat should I say in my reply to himΒ
I need this to be more than a paragraph long