Conditional Probability Quiz: Test Your Knowledge with Flashcards

Study Notes

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

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 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

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 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 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