This is my final project for Probability and Statistic lecture. This is a statistical analysis of customer data to explore the relationship between Gender, Annual Income, and Profession with Spending Score. The analysis was performed using R and includes descriptive statistics, inferential tests, regression analysis, and visualizations.
This analysis aims to:
- Analyze the distribution of gender and profession in customer data.
- Perform descriptive statistics for Annual Income and Spending Score.
- Calculate the covariance and correlation between spending and income.
- Visualize data distributions using histograms, frequency polygons, scatter plots, pie charts, and bar charts.
- Test for normality using the Shapiro-Wilk test and Q-Q plots.
- Conduct T-tests for single and two-population mean comparisons.
- Build a simple linear regression model.
- Perform Chi-Square tests for goodness of fit and independence.
The dataset contains information from 2,000 customers. After filtering by the two most dominant professions:
- Artist (31%)
- Healthcare (17%)
A total of 951 customers were used for analysis.
Key variables:
Annual Income ($)Spending Score (1-100)Gender(F/M)Profession(Artist/Healthcare)
- Distribution tables and descriptive statistics
- Data visualizations:
- Histogram
- Frequency polygon
- Scatter plot
- Pie chart
- Bar chart
- Covariance and Pearson correlation
- Shapiro-Wilk normality test and Q-Q plot
- Confidence intervals (CI 95%) for spending score
- T-tests:
- One-sample for Annual Income vs 50,000
- Two-sample for Spending Score by Gender
- Simple Linear Regression:
- Spending Score ~ Annual Income
- Chi-Square tests:
- Goodness of Fit (Gender distribution)
- Independence test (Gender vs Profession)
- R (version 4.x)
- RStudio
- Libraries:
tidyversereadxlggplot2statse1071
- Clone this repository
git clone https://github.com/RaymussenArthur/RStudioAnalytics.git cd RStudioAnalytics/UAS_ProbStat