Learning Objectives
5 objectives- Understand fundamental concepts and terminology in statistics and their applications.
- Apply descriptive statistics techniques to summarize and interpret data sets.
- Gain proficiency in probability principles and common probability distributions.
- Develop skills in inferential statistics including hypothesis testing and regression analysis.
- Utilize data visualization tools to effectively communicate statistical insights.
Content Outline
PreviewUnit 2941: Introduction to Applied Statistics
1. Introduction to Statistics
- Definition and scope of statistics
- Types of data: qualitative vs quantitative
- Levels of measurement: nominal, ordinal, interval, ratio
- Importance and applications of statistics in various fields (e.g., business, healthcare, social sciences)
2. Descriptive Statistics
2.1 Measures of Central Tendency
- Mean: calculation and interpretation
- Median: determination and use cases
- Mode: identification and significance
2.2 Measures of Dispersion
- Range: definition and limitations
- Variance: computation and meaning
- Standard deviation: interpretation and use
- Importance of dispersion in data analysis
3. Probability Fundamentals
- Definition and concept of probability
- Calculating probability: classical, relative frequency, and subjective approaches
- Probability rules: addition rule, multiplication rule, complement rule
- Theoretical vs empirical probability: differences and examples
4. Probability Distributions
4.1 Discrete Distributions
- Binomial distribution: assumptions, probability mass function, examples
- Poisson distribution: characteristics and applications
4.2 Continuous Distributions
- Normal distribution: properties, standard normal curve, empirical rule
- Applications of distributions in real-world scenarios
5. Sampling and Sampling Distributions
- Sampling methods: simple random, stratified, systematic, cluster sampling
- Sampling bias and how to avoid it
- Sampling distributions: concept and importance
- Central Limit Theorem: statement and implications
6. Hypothesis Testing
- Formulating hypotheses: null and alternative hypotheses
- Types of errors: Type I and Type II
- Test statistics and significance levels
- Steps in hypothesis testing procedure
- Interpreting p-values and confidence levels
7. Correlation and Regression Analysis
- Correlation analysis: Pearson’s correlation coefficient, interpretation
- Simple linear regression: model, assumptions, estimation of parameters
- Using regression for prediction
- Limitations and diagnostics of regression analysis
8. Inferential Statistics
- Confidence intervals: construction and interpretation
- t-tests: one-sample, independent, and paired samples
- Analysis of Variance (ANOVA): purpose and basic concepts
- Chi-square tests: goodness-of-fit and test of independence
9. Data Visualization
- Importance of visual representation of data
- Common visualization tools: bar charts, histograms, box plots, scatter plots
- Best practices for effective visualization
- Using software tools for creating visualizations (e.g., Excel, R, Python libraries)
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