Course Title: Introduction to Statistics
Course Description: This course provides an introduction to statistical concepts, methods, and applications. Topics include descriptive statistics, probability theory, hypothesis testing, regression analysis, and data visualization techniques. Students will learn how to analyze and interpret data, make informed decisions, and draw meaningful conclusions.
Course Outline:
- Week 1: Introduction to Statistics
- Definition and importance of statistics
- Types of data (qualitative vs. quantitative)
- Levels of measurement (nominal, ordinal, interval, ratio)
- Week 2: Descriptive Statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (range, variance, standard deviation)
- Data visualization techniques (histograms, box plots)
- Week 3: Probability Theory
- Basic probability concepts (events, sample space, probability rules)
- Probability distributions (discrete and continuous)
- Expected value and variance
- Week 4: Statistical Inference I – Estimation
- Point estimation and interval estimation
- Confidence intervals for means and proportions
- Margin of error and sample size determination
- Week 5: Statistical Inference II – Hypothesis Testing
- Null and alternative hypotheses
- Type I and Type II errors
- One-sample and two-sample hypothesis tests
- Week 6: Correlation and Regression Analysis
- Correlation coefficient and scatter plots
- Simple linear regression
- Multiple regression and model interpretation
- Week 7: Analysis of Variance (ANOVA)
- One-way ANOVA and post-hoc tests
- Factorial ANOVA and interactions
- ANOVA assumptions and diagnostics
- Week 8: Nonparametric Statistics
- Introduction to nonparametric tests
- Mann-Whitney U test and Wilcoxon signed-rank test
- Kruskal-Wallis test and Chi-square test for independence
- Week 9: Statistical Software and Tools
- Introduction to statistical software (e.g., R, SPSS, Excel)
- Data management and analysis using software
- Generating statistical reports and visualizations
- Week 10: Applications of Statistics
- Statistical analysis in research studies
- Real-world applications of statistics (business, healthcare, social sciences)
- Ethical considerations in statistical analysis
Course Assignments:
- Weekly problem sets and quizzes
- Data analysis and interpretation projects
- Hypothesis testing and regression analysis exercises
- ANOVA and nonparametric tests simulations
- Statistical software tutorials and practice assignments
- Final project: Statistical analysis and report on a chosen topic
Course Materials:
- Textbook: “Statistics for Business and Economics” by Paul Newbold et al.
- Statistical software tutorials and documentation
- Datasets for hands-on analysis and practice
- Online resources for statistical concepts and techniques
- Case studies and examples of statistical applications
Assessment:
- Quizzes, exams, and assignments on statistical concepts and methods
- Data analysis projects and reports
- Statistical software proficiency evaluation
- Final project assessment based on depth of analysis and communication skills
- Class participation and engagement in discussions and activities
Prerequisites: Basic mathematics knowledge (algebra, calculus) and familiarity with spreadsheet software (e.g., Excel) would be beneficial. No prior statistics background is required.