Applied Statistics

“Statistics: the art of learning from data.”

Statistics courses teach important ideas like how to collect data, analyze it, understand probability and distributions, and test assumptions using data. Learning statistics helps you make better and more reliable decisions in many different fields.

📌What You will Learn

1. Learn the basic ideas of statistics in a simple and clear way
2. Understand descriptive statistics and probability
3. Learn common probability distributions like the Normal and Poisson distributions
4. Understand hypothesis testing, p-values, and Type I and Type II errors
5. Learn regression methods such as logistic regression, multiple linear regression, and regression trees
6. Understand correlation, R-square, RMSE, MAE, and other measures used to evaluate models

📊 Statistics Learning Roadmap (Step-by-Step)

🔹 Step 1: Data and Statistics

1. Understanding data and its types
2. Importance of statistics in decision-making

🔹 Step 2: Descriptive Statistics – Tabular and Graphical Displays

1. Frequency tables and cross-tabulations
2. Bar charts, histograms, pie charts, and other graphs

🔹 Step 3: Descriptive Statistics – Numerical Measures

1. Measures of Location (Central Tendency): Mean, Median, Mode
2. Measures of Variability (Dispersion): Range, Variance, Standard Deviation
3. Five-number summaries and Box Plots
4. Measures of association between two variables

🔹 Step 4: Introduction to Probability

1. Basic probability concepts and rules
2. Conditional probability
3. Bayes’ Theorem

🔹 Step 5: Discrete Probability Distributions

1. Random variables
2. Expected value and variance
3. Binomial distribution
4. Poisson distribution
5. Discrete uniform distribution

🔹 Step 6: Continuous Probability Distributions

1. Continuous uniform distribution
2. Normal distribution
3. Normal approximation of binomial probabilities
4. Exponential distribution

🔹 Step 7: Sampling and Sampling Distributions

1. Selecting a sample
2. Point estimation
3. Sampling distribution of the mean
4. Sampling distribution of the proportion

🔹 Step 8: Interval Estimation

1. Confidence intervals for population mean
2. Confidence intervals for population proportion

🔹 Step 9: Hypothesis Tests

1. Formulating null and alternative hypotheses
2. Type I and Type II errors
3. Hypothesis testing and decision-making

🔹 Step 10: Inference About Means and Proportions with Two Populations

1. t-Test for comparing means
2. Chi-square tests for independence and goodness of fit

🔹 Step 11: Analysis of Variance (ANOVA)

1. Introduction to ANOVA
2. F-test for comparing multiple groups

🔹 Step 12: Regression Analysis

1. Covariance and correlation
2. Simple Linear Regression:
3. Coefficient of determination
4. Regression model
5. Testing for significance
6. Multiple Regression:
7. Regression equation
8. Estimated multiple regression equation
9. Logistic Regression

📌Sample Responses (Statistics Session Related):

📊 Statistics concepts and fundamentals

📈 Data analysis using R/Python

🧮 Probability and distributions

📉 Hypothesis testing and inference

🤖 Statistics for Data Science / ML

🎓 Exam preparation / academic support

📂 Research / project discussion

❓Invitee Questions

🔹Question 1: Do you need assistance with assignment and exam preparation or academic support in statistics?

🔹Question 2: Are you looking to understand core statistics concepts and fundamentals?

🔹Question 3: Do you need help with data science using R and/or Python?

🔹Question 4: Are you seeking clarification on probability theory and probability distributions?

🔹Question 5: Do you want support with hypothesis testing and statistical inference?

🔹Question 6: Do you need assistance with assignment and exam preparation or academic support in statistics?

🔹Question 7: Are you looking for guidance on research work or project discussions in statistics and data analysis?