Applied Statistics Real World Problem Solving

Applied Statistics: Real World Problem Solving is a comprehensive course designed to equip you with the statistical tools and techniques needed to analyze real-world data and make informed decisions. Whether you’re a business analyst, data scientist, or simply looking to enhance your data analysis skills, this course will provide you with a solid foundation in applied statistics.

Key Topics Covered:

• Introduction to Business Statistics: Understand the basics of data types and their relevance in business, along with the differences between quantitative and qualitative data.
• Measures of Central Tendency: Learn about mean, median, and mode, and their importance in summarizing data.
• Measures of Dispersion: Explore standard deviation, mean deviation, and quantile deviation to understand data variability.
• Distributions and the Central Limit Theorem: Dive into different types of distributions and grasp the central limit theorem’s significance.
• Sampling and Z-Scores: Understand the concepts of sampling from a uniform distribution and calculating Z-scores.
• Hypothesis Testing: Learn about p-values, hypothesis testing, t-tests, confidence intervals, and ANOVA.
• Correlation: Study the Pearson correlation coefficient and its advantages and challenges.
• Advanced Statistical Concepts: Differentiate between correlation and causation, and perform in-depth hypothesis testing.
• Data Cleaning and Preprocessing: Master techniques for cleaning and preprocessing data, along with plotting histograms and detecting outliers.
• Statistical Analysis and Visualization: Summarize data with summary statistics, visualize relationships between variables using pair plots, and handle high correlations using heat maps.