Conditional Probability Quiz

Probability and Statistics

• Probability is used to summarize data and make inferences about a population
• Descriptive statistics are used to describe a sample, while inferential statistics are used to make inferences about a population
• The mean of a sample is denoted by x, while the mean of a population is denoted by μ
• The sample variance is denoted by S2, while the population variance is denoted by σ2
• The formula for sample variance is S2 = Σ(xi - x)2 / (n - 1), where xi is each observation, x is the sample mean, and n is the number of observations

Hypothesis Testing

• A null hypothesis (H0) is a statement of no effect or no difference, while an alternative hypothesis (H1) is a statement of an effect or difference
• The level of significance is the maximum probability of rejecting a true null hypothesis
• The critical region is the region of the distribution where the null hypothesis is rejected
• The test statistic is a value that is used to determine whether to reject the null hypothesis
• The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, given that the null hypothesis is true

Correlation and Regression

• Correlation measures the strength and direction of the linear relationship between two variables
• Positive correlation means that as one variable increases, the other variable also tends to increase
• Negative correlation means that as one variable increases, the other variable tends to decrease
• No correlation means that there is no linear relationship between the two variables
• Regression analysis is used to model the relationship between a dependent variable and one or more independent variables

Data Types

• Structured data is highly organized and easily searchable, such as data in a database
• Unstructured data is unorganized and lacks a predefined format, such as images or videos
• Semi-structured data is a mix of structured and unstructured data, such as XML files
• Attributes of data can be qualitative (categorical) or quantitative (numeric)
• Qualitative data can be nominal (categories without order) or ordinal (categories with order)
• Quantitative data can be interval (equal intervals between values) or ratio (has a true zero point)

Data Mining and Data Science

• Data mining is the process of discovering patterns and relationships in large datasets
• Data exploration is the process of summarizing and visualizing data to understand its characteristics
• Data visualization is the process of creating graphical representations of data to communicate insights
• Feature engineering is the process of selecting and transforming raw data into features that are suitable for modeling
• Data cleaning is the process of ensuring that the data is accurate, complete, and consistent

Data Wrangling

• Data wrangling is the process of transforming and preparing raw data into a usable format
• The steps involved in data wrangling are:
  • Evaluate usability: determine whether the data is suitable for analysis
  • Cleanse: remove errors and inconsistencies from the data
  • Visualize: create graphical representations of the data to understand its characteristics
  • Analyze: apply statistical methods to extract insights from the data