Model Analysis Flashcards
What are descriptive statistics?
Descriptive statistics summarize and describe data using visual or numerical methods, focusing on central tendency, variability, and distribution.
What are the key measures of central tendency?
Mean (average), Median (middle value), and Mode (most frequent value).
What are the main measures of dispersion?
Range, Variance, Standard Deviation, and Interquartile Range (IQR).
What are measures of position?
Percentiles and quartiles, which help in understanding the distribution of values in a dataset.
What are some common graphical methods for data representation?
Bar graphs, histograms, pie charts, box plots, and scatter plots.
What are measures of association in statistics?
Measures like correlation and covariance that indicate the relationship between variables.
How do covariance and correlation differ?
Covariance measures how two variables change together, while correlation measures the strength and direction of their relationship.
What are the merits of descriptive statistics?
Simplifies large datasets, identifies patterns and trends, assesses data quality and outliers, lays foundation for further statistical analysis.
What are the demerits of descriptive statistics?
Oversimplifies data and loses details, sensitive to outliers (mean), cannot make predictions, does not establish causality.
Where are descriptive statistics used?
Business, healthcare, education, finance, and manufacturing for data analysis and decision-making.
What is regression analysis?
A statistical method used to model relationships between a dependent variable and one or more independent variables.
What are the main types of regression?
Linear Regression, Logistic Regression, Bayesian Regression.
What is linear regression?
A regression model that predicts the dependent variable based on one (simple regression) or multiple (multiple regression) predictor variables using a straight-line equation.
Where is linear regression used?
Sales forecasting, price elasticity analysis, risk assessment in insurance, sports performance analysis.
What is logistic regression?
A classification model that estimates the probability of an event occurring based on independent variables.
What are the different types of logistic regression?
Binary Logistic Regression (two outcomes) and Ordinal Logistic Regression (ordered categories).
Where is logistic regression applied?
Fraud detection in banking, disease prediction in healthcare, customer churn prediction, credit risk assessment.
What is Bayesian regression?
A regression technique that incorporates prior knowledge and uncertainty into the model using probability distributions.
Why is regression analysis important in machine learning?
It helps in predicting outcomes, making informed decisions, and understanding variable relationships in predictive models.
What are the main objectives of descriptive statistics?
To summarize and describe datasets, allowing researchers to analyze and interpret data efficiently.
