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For your chapter 4 and 5 thesis chapter. Experienced STATISTICIAN/RESEARCHER and PHD Finalist with 15 plus years’ experience in quantitative analysis.
See https://www.up.ac.za/news/post_1722144-up-student-wins-fellowship-to-study-womens-empowerment-index
My Linkdin profile: https://www.linkedin.com/in/christopher-manyamba-04490713/
Also see https://researchspace.ukzn.ac.za/handle/10413/8822
I analyse, interpret and write up Chapter 4 (15-25 pages, or more), which you then discuss in Ch5.
Overall I provide the following quantitative analysis and modelling expertise;
1. Proposal development including study design and sampling;
2. Univariate analysis: Frequencies. Means and std deviations for continuous data, tables and graphs;
3. Bivariate analysis: pairwise correlation, Spearman’s for non-parametric and Pearson’s for normally distributed data;
4. For Likert scale data, reliability (Cronbach’s alpha) and normality tests (Wilk Shapiro tests, skewness, kurtosis);
5. Hypothesis testing -parametric tests e.g. paired t-tests, and non-parametric tests (e.g. Wilcoxon sign rank, Kruskal Wallis);
6. Multivariate-Regression analyses which cover ordinal, count, categorical and binary outcomes. Ordinary Least Squares (OLS), Partial/Pooled effects; Factor/Principal component analysis;
7. Pathway Analysis and Structural Equation Modelling (SEM), and its post estimation results
8. Time-series, Econometric Models: Autoregressive Conditional Heteroscedastic (ARCH) and Auto-Regressive Distributed Lag (ARDL) family models. The stepwise analysis includes setting data to time series, white noise detection (stationarity tests), Dick Fuller or P-Peroni unit root tests (VAR), Vector error correction models (VEC), Granger causality tests;
9. Survival analysis (epidemiological): Includes setting data to survival, run descriptive stats with survival analysis family of analysis. Survival analysis: Censoring, prevalence and incident rates, regression models e.g. Cox proportionate regression, interactions (log-rank, and Kaplan Meier survival functions graphs);
10. Checking multicollinearity/endogeneity-Variance Inflation Factor (VIF in STATA), or any pre-model estimation;
11. Turnaround time for analysis, interpretation and write up: In a space of 5 days;
If you need to self-tutor STATA, you can get a STATA version 15-30-day trial
0723548043
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Chris Manyamba
Lesson 1: The first lesson is meant to be an introduction to the desktop program, solutions, and the installation of the program itself.
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Lesson 2: The second lesson involves the dashboard for the program’s desktop. It would involve going such things as excel data, dashboards, working with the creation and modification of reports, etc.
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Lesson 3: The third lesson is on data sources like, CSV
files and Excel.
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Lesson 4: The fourth is about visualizations in regards to text, charts, etc.
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Lesson 5: The fifth lesson covers data selection such as, using slicers and filtering data.
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Lesson 6: The sixth covers learning how to enhance the dashboards, like shapes, colors in the background, text boxes, etc.
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Lesson 7: In the seventh lesson, you will learn about
PowerBI.com and what it includes. Among other things, it would include learning about how to share the dashboards as well as creating new ones, gateways, and how to use the files on the Desktop.
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Lesson 8:The final lesson is about best practices, like stakeholders, system impact, and learning how to design your reports and dashboards.
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JideKuku
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