For the project, there are two parts: A and B. You should use a
total of three files. Two of the files are for part A, and one file
is for part B. Please download three files
here:https://1drv.ms/f/s!An6yRjtqJUs5cGl4YmWoZ1MUDss

        For part A, each file
will contain a column for subject ID and a column for either the
dependent variable value or the independent variable value. First,
you are expected to sort the two files by subject ID and merge
them. You should not just use “cut and paste” to merge your data.
Second, you are expected to deal with missing data. Your report
should contain the count of the number of subject IDs that had at
least one independent variable value or dependent variable value.
It should also include the count of the number of subject IDs that
had an independent variable. There are a number of missing data
procedures. Often a statistical package has imputation algorithms
in the software. For example, R has 5 different algorithms
available. You may choose any algorithm except for listwise
deletion. Specify your choice in your report. Often, the choice of
imputation method has little effect on the results if the fraction
of missing data is 30% or less. Then, you should use the
statistical package of your choice to find the fitted linear
model.

      The data file for part B will
contain one line for each subject ID. The line will contain the
subject ID, the value of the independent variable, and the value of
the dependent variable. A transformation of either IV or DV or both
may be required. You should read the text for suggestions on
fitting a model. A lack of fit (LOF) test should be applied if
there are repeated values in the data sets. It is your group’s
responsibility to find repeated (or near repeated) independent
variable values. That is, your group should bin near repeated data
into one level. For example, suppose that and . While there are not
exactly repeated x values, your group could bin these
points into one group of nearly repeated points. That is, choose
the average x-value as the value of x after binning. Then your
binned data would be and .   Then perform a LOF test on
the data set after binning all near repeated values.

You must submit a one-page report on Problem A and a one-page
report on Problem B. Each report should have four sections. The
introduction should contain a statement of the problem and the
objective of the paper. This part is easy: your problem is to
recover the function that was used to generate the dependent
variable value based on the value of the independent variable. The
data you receive will be generated by a simulation program. The
second section should describe your methodology. Specifically, how
the files were merged, the program used to perform the statistical
analysis, whether you used linear regression and additional
procedures such as a lack of fit test, how much missing data was
present in the data, and the procedure for dealing with missing
data. The third section should contain your results: what fraction
of the variation of the dependent variable was explained, the
analysis of variance table, the fitted function, confidence
intervals for slope and test of the null hypothesis that the slope
was zero. The fourth section should be conclusions and discussion.
This section should focus on “big picture” issues. Was there an
association between the variables? How important was it? That is,
what was the r-squared value. What is your fitted function? You may
submit a longer appendix of computer work and programs.

Important note:

Simply submitting your computer output is not
acceptable and will receive a grade of 0. You must submit a formal
report to begin to get non-zero credit.