1: Definition and Importance of Statistics
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2: Types of Data: Qualitative vs. Quantitative
3: Levels of Measurement: Nominal, Ordinal, Interval, Ratio
4: Measures of Central Tendency: Mean, Median, Mode
5: Data Visualization: Histograms, Bar Graphs, and Pie Charts
6: Data Visualization: Histograms, Bar Graphs, and Pie Charts
7: Basic Probability Principles
8: Conditional Probability and Independence
9: Probability Distributions: Binomial, Normal
10: Sampling Methods: Random, Stratified, Cluster
11: The Central Limit Theorem
12: Sampling Distributions of Sample Mean
13: P-Value and Significance Level
14: Constructing Confidence Intervals for Means and Proportions
15: Interpretation of Confidence Intervals
16: t-Tests
17: Analysis of Variance (ANOVA)
18: Analysis of Variance (ANOVA)
19: Analysis of Variance (ANOVA)
20: Correlation Coefficient: Pearson and Spearman
21: Simple Linear Regression
22: Multiple Linear Regression
23: Wilcoxon Rank-Sum Test
24: Wilcoxon Rank-Sum Test
25: The Kruskal-Wallis Test
26: The Kruskal-Wallis Test
27: Introduction to Time Series Data
28: Moving Averages and Exponential Smoothing
29: ARIMA Models
30: Multivariate Analysis
31: Logistic Regression
32: Data Mining Techniques
33: Introduction to R programming
34: Introduction to Machine Learning in Python