a. Assess the data using descriptive statistics. Include various descriptive measures (mean, max, mode, median, min, standard deviation, etc.) Use histograms, if appropriate, and analyze any outliers you find in the data. Record what you learn.
b. Plot the data. Analyze and record what you learn from the various scatter plots.
c. Determine the correlation between the dependent variable and the various independent variables by creating a correlation matrix. Assess for multicollinearity between the independent variables.
d. Assess the predictive capability of this data using both simple and multiple linear regression, including the use of a dummy (categorical independent) variable of whether the student attended a college or university. Run the regressions, perform statistical inference in each case, and record what you learn. Use a 0.05 level of significance. If you assess that there is not a relationship between any two variables of data, redo the regression equation and assessments showing only the data points with linear relationships. If you find multicollinearity, remove it by redoing your model without one of the variables causing multicollinearity, then try it with the other variable removed and pick the best option.
e. Based on the prior steps, determine and record the best linear regression equation for this data. Discuss the meaning of the model fit and regression coefficients.
f. Assess the possibility of a better curvilinear regression line. If your findings warrant this, run the curvilinear regression and discuss the meaning of the model fit and the regression coefficients.
g. Pick the best regression equation and document it.
h. Using your best regression equation, what is your graduation rate prediction for a university whose student median SAT score is 1210, acceptance rate is 23%, expenditures per student are $25,500, and the percentage of students in the top ten percent of their graduating HS class is 79%?
i. What other independent variables (not cited in the data) may be important in improving this model?
Hi I am a very experienced statistician and academic writer. I have completed several pHD level thesis projects involving advanced statistical analysis of data. I have worked with data from several companies and have done projects involving high level quantitative analysis and data interpretation skills to study the trends, time behaviour and compare the variables in the data. I can do advanced level analysis in SPSS, R, WEKA and excel tools like machine learning, hypothesis testing, forecasting, T-test, ANOVA etc.
Looking forward to discussion,
Best Regards,
Suyash
Hi,
Below is my experience, and I can deliver you quality work with in specified or agreed time.
Myself Ph.D. in advanced analytics having 10+ years of experience in developing and delivering analytical projects using Excel and open source tools, and can deliver your requirement accompanied by a step-by-step word document.
As, freelancer.com bid does not allow for attaching; cannot showcase my expertise in this area. I am available at freelance chat (click on my name and options), to understand the requirements and explain my approach if required in detail.
Waiting for your reply (or) initiation for chat discussion.
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Dr. Kumar PM.
Will create 2 to 3 milestones after initial discussions
recently learned data science using excel, SPSS and R and looking for so real life experience. By doing this project the achieve some data science skills and some pocket money
Good at Excel formulas and deriving charts out of the data on the sheet. I am by profession into Recruitment and handle and modify excel data. Familiar with vlookup