# Navigating Statistics and Probability Landscapes: A Proficient Journey with Skills in R

Embark on a rich exploration of statistics and probability, where the intricacies of data analysis and chance unfold. This comprehensive journey spans from unveiling the story behind datasets using descriptive statistics to unravelling the foundations of probability with combinatorics and conditional probability. Guided by the proficiency of the R programming language, this exploration not only illuminates the nuances of statistical concepts but also showcases the practical application of R in transforming theory into actionable insights. Whether you're delving into correlations, understanding different perspectives on probability, or calculating probabilities in random experiments, this collection encapsulates a holistic understanding of statistical and probabilistic realms, harmonized with the skillful use of R for Probability assignment using R Programming.

## Question 1: Descriptive Statistics for Tribe Data

Problem Description:

Analyze the dataset 'tribe.RData' containing height and weight information.

• Mean height: 167.8419 cm
• Mean weight: 64.5607 kg
• Median height: 168.115 cm
• Standard deviation of height: 7.068682 cm
• Standard deviation of weight: 11.88245 kg
• Correlation between weight and height: 0.1949294

## Question 2: Correlation Analysis of Midterm and Final Exam Grades

Problem Description:

• Sample size: 73 students
• Correlation for all students: 0.6701399
• Correlation for a subset of 30 students: 0.2593948
• Explanation: The correlation in part (e) is smaller due to the reduction in sample size. With a larger sample, the correlation approaches the population correlation coefficient.

## Question 3: Frequentist vs Bayesian Perspectives on Probability

Problem Description:

Discuss the differences between the frequentist and Bayesian perspectives on the definition of probability.

• Frequentists see probability as relative frequency: Bayesians view it as a function of belief.
• Frequentists rely on experiments: Bayesians use prior knowledge.
• Frequentists treat parameters as fixed: Bayesians treat them as random variables.

## Question 4: Sample Space of an Experiment

Problem Description:

List all elements in the sample space of an experiment.

• Sample space: {HHH, HHT, HTH, THH, HTT, THT, TTH, TTT}

## Question 5: Meal Combinations

Problem Description:

Calculate the number of meal combinations possible.

• Number of main dishes: 4
• Number of desserts: 3
• Number of meal combinations: 24

## Question 6: Arrangement of Letters in a Word

Problem Description:

Calculate the ways the letters of the word "CATHY" can be arranged.

• Ways to arrange: 120

## Question 7: Number of Different Committees

Problem Description:

Calculate the number of different committees possible.

• Number of committees: 1,140

## Question 8: Committees with Women and Men

Problem Description:

Calculate the number of different committees consisting of 2 women and 3 men.

• Number of committees: 350

## Question 9: Combinations Symmetry

Problem Description:

Explain why (n¦r) = (n¦(n-r)).

• Whenever r items are selected, n−r items are left over. Hence the number of combinations of n things taken r at a time is equal to the number of combinations of n things taken (n-r) at a time.

## Question 10: Conditional Probability

Problem Description:

Calculate conditional probabilities for coin-flipping events.

• P(A│B): 1/2
• P(A│C): 1/3

## Question 11: Probability of Correct Guessing

Problem Description:

Calculate the probability of correctly guessing four milk-first cups out of eight.

• Probability: 1 in 70 or 1.4%

## Question 12: Coin Flipping Probability

Problem Description:

Calculate the probability of getting all heads when flipping four fair coins.

• Probability: 1/16