Model Analysis 2 Flashcards
What is inferential statistics?
Inferential statistics involves methods that draw conclusions about a population based on a sample, often using hypothesis testing.
What is hypothesis testing?
Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample to infer a condition about a population.
What are the key steps in hypothesis testing?
- State the null hypothesis (H₀) and alternative hypothesis (H₁). 2. Choose confidence or significance level (α). 3. Calculate the degrees of freedom (Df). 4. Find the critical value from a standard distribution table. 5. Compute the test statistic and compare it with the critical value. 6. Draw conclusions about the population.
What are the different types of hypothesis tests?
Left Tail Test, Right Tail Test, Two Tail Test.
What is a critical value in hypothesis testing?
A critical value is a threshold that defines the boundary for rejecting the null hypothesis.
What is a confidence interval in hypothesis testing?
A confidence interval is a range of values within which a population parameter is expected to lie with a certain level of confidence (e.g., 95%).
What are the Z-Test, T-Test, and F-Test used for?
Z-Test: Used for normally distributed data with a large sample size (≥30). T-Test: Used for small sample sizes (<30) with unknown population variance. F-Test: Used to compare the variances of two datasets.
What is the Chi-Square Test used for?
The Chi-Square Test is used to compare categorical data distributions and determine if observed frequencies differ from expected frequencies.
What is ANOVA (Analysis of Variance)?
ANOVA is a statistical method used to compare means across multiple groups to determine if significant differences exist.
What is the coefficient of determination (R²)?
R² measures how well a regression model explains the variability of the dependent variable.
What does the correlation coefficient indicate?
The correlation coefficient measures the strength and direction of a linear relationship between two variables, ranging from -1 to +1.
What are common error metrics for evaluating regression models?
Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE).
What are common error metrics for evaluating classification models?
Accuracy, Precision, Recall (Sensitivity), F1 Score.
What is linear regression?
Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables using a straight-line equation.
When should ANOVA be used?
One-Way ANOVA: Used when comparing means of a single independent variable across multiple groups. Two-Way ANOVA: Used when analyzing the effect of two independent variables on a dependent variable.
