Introduction to Statistical Analysis and RStatistics / R
For Undergraduate Theses, Master's Theses, Journal Manuscripts, and Corporate Research
Statistical Analysis Is Not “Pressing a Button”; It Is Choosing a Method That Fits the Data
R is widely used as a free software environment for statistical computing and graphics. Although convenient, statistical analysis can be misleading if you select tests without checking Levels of measurement, sample size, distribution, outcome variables, explanatory variables, and research purposethe relevant characteristics of the data and research design.
Basic Statistical Analysis Workflow
The analytical method changes depending on whether you want to examine differences, relationships, or prediction.
Check whether variables are nominal, ordinal, interval, or ratio scale, and select appropriate summaries and tests.
Check means, standard deviations, medians, proportions, missing values, and outliers.
Select methods such as t-tests, analysis of variance, correlation, regression analysis, or logistic regression according to the purpose.
Present the results in tables or graphs and explain the meaning of the numbers in relation to the research purpose.
Analytical Methods by Purpose
| What You Want to Know | Representative Method | Points to check |
|---|---|---|
| Difference Between Two Group Means | t-test | Paired or independent data, normality, and treatment of variance |
| Mean Differences Among Three or More Groups | Analysis of Variance | Multiple comparisons and between-group variability |
| Relationship Between Variables | Correlation Analysis | Scatterplots, outliers, and avoiding confusion with causation |
| Predict an Outcome | Regression Analysis | Outcome variables, explanatory variables, and multicollinearity |
| Handle a Binary Outcome | logistic regression | Odds ratios, confidence intervals, and number of events |
What You Can Do With R
Data Preparation
Import CSV files, handle missing values, create variables, and organize categorical variables.
statistical analysis
Descriptive statistics, statistical tests, regression analysis, multivariate analysis, and reproducible analysis code.
Visualization
Create bar charts, line charts, scatterplots, box plots, and other figures.
Reliable Sources to Check
Common Checkpoints
Can I request only statistical analysis?
Yes. You can consult us only about the analytical part, including data checking, aggregation, graphing, statistical tests, regression analysis, and explanation of results.
Should I use R or SPSS?
It depends on the research purpose, submission requirements, reproducibility needs, and ease of operation. R is well suited to reproducible code-based analysis, while SPSS offers a user-friendly GUI.
Can I consult you even if the result is not statistically significant?
Yes. Results can be organized not only by statistical significance but also by effect size, confidence intervals, sample size, and research significance.
Contact
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