Biostatistics and Correlation Overview

<p>The availability of statistical software. (C)</p> Signup and view all the answers

<p>Regression analysis (D)</p> Signup and view all the answers

<p>Residual (A)</p> Signup and view all the answers

<p>The residual or error term (C)</p> Signup and view all the answers

<p>The expected change in the dependent variable for a one-unit change in the independent variable (B)</p> Signup and view all the answers

<p>T-test (B)</p> Signup and view all the answers

<p>Point estimate ± 1.96 × SE(β1) (C)</p> Signup and view all the answers

<p>A confidence band represents the uncertainty in the slope coefficient, while a prediction band represents the uncertainty in the predicted value of the dependent variable (C)</p> Signup and view all the answers

<p>To assess the goodness of fit of the regression model (B)</p> Signup and view all the answers

<p>The best single-value estimate of the true population parameter (C)</p> Signup and view all the answers

<p>0.42 liters (D)</p> Signup and view all the answers

<p>3.5 liters (C)</p> Signup and view all the answers

<p>Decrease of 0.03 liters (D)</p> Signup and view all the answers

<p>1.6 liters (A)</p> Signup and view all the answers

<p>A 25-year-old male (A)</p> Signup and view all the answers

<p>Regression line (A)</p> Signup and view all the answers

<p>Homoscedasticity (B)</p> Signup and view all the answers

<p>The difference between the fitted and observed values of Y (A)</p> Signup and view all the answers

<p>The error term is normally distributed with a mean of 0 and a variance of σϵ (A)</p> Signup and view all the answers

<p>It represents the slope of the regression line for the k-th variable (C)</p> Signup and view all the answers

<p>Drawing a Q-Q plot of the residuals from the regression model (C)</p> Signup and view all the answers

<p>By allowing for more than one predictor variable (A)</p> Signup and view all the answers

<p>The independent variable (A)</p> Signup and view all the answers

<p>t-test / Mann-Whitney test (A)</p> Signup and view all the answers

<p>Chi-square / Fisher's test (B)</p> Signup and view all the answers

<p>Linear regression (D)</p> Signup and view all the answers

<p>Logistic regression (A)</p> Signup and view all the answers

<p>The association between blood pressure and body mass index (C)</p> Signup and view all the answers

<p>The association between blood sugar control and kidney function (A)</p> Signup and view all the answers

<p>It can be used to determine cause-and-effect relationships between two variables (B)</p> Signup and view all the answers

<p>Correlation analysis can be used to quantify the strength and direction of the relationship between two numeric variables (D)</p> Signup and view all the answers

<p>The random error term is normally distributed with a mean of zero and a standard deviation of σϵ. (B)</p> Signup and view all the answers

<p>To predict future values of the dependent variable yi, given a new value of the independent variable xi. (D)</p> Signup and view all the answers

<p>The standard error quantifies the uncertainty in the estimated coefficients. (C), The standard error is necessary to perform hypothesis testing for the coefficients. (D)</p> Signup and view all the answers

<p>It tests whether there is a statistically significant linear relationship between the independent and dependent variables. (B)</p> Signup and view all the answers

<p>The statement emphasizes that the model is only useful for specific applications. (B), The statement highlights that the simple linear model is not a perfect representation of reality. (D)</p> Signup and view all the answers

<p>The simple linear model is considered the 'ground truth' and is assumed to be accurate. (B)</p> Signup and view all the answers

<p>To apply the model to predict future values of the dependent variable. (B)</p> Signup and view all the answers

<p>The real world is inherently complex, and models are simplifications. (B)</p> Signup and view all the answers

The main goals of biostatistics: distinguishing true effects from random error and quantifying random error around effect estimates.

A statistical method for determining if there is enough evidence to reject a null hypothesis using p-values.

A measure that quantifies the amount of variability or dispersion in a sample's point estimate.

A range of values derived from a sample that is believed to contain the true population parameter with 95% certainty.

The method used in biostatistics depends on the research question and types of variables involved.

Analysis comparing means using continuous outcomes with a binary exposure.

Statistical test for categorical outcomes and exposures.

Statistical analysis predicting a continuous outcome from one or more continuous exposures.

Statistical representation of binary outcomes based on continuous exposure.

Statistical relationship indicating how two numeric variables change together.

An outcome measured by categories, such as illness types or survival status.

Variables that can take an infinite number of values, such as weight or blood pressure.

An outcome that has two possible states, such as sick or healthy.

A statistical method to model the relationship between a dependent variable and one independent variable using a linear equation.

The variable being tested and measured in an experiment, often denoted as yi.

The variable that is changed or controlled in an experiment, typically represented as xi.

Parameters β0 (intercept) and β1 (slope) that define the line in a linear regression model.

The expected value of the dependent variable when the independent variable is zero.

The change in the dependent variable for a one-unit change in the independent variable.

A range of values derived from sample statistics that is likely to contain the population parameter with a specified probability.

The difference between observed values (yi) and fitted values from the regression model.

A range of values around a regression coefficient estimate indicating where the true coefficient is likely to lie with 95% certainty.

A test proposing that there is no effect or no relationship, particularly that β1 = 0 in regression.

A measure that indicates the standard deviation of the sampling distribution of a statistic, commonly used for regression coefficients.

A range that shows the possible values of the dependent variable (Y) for given independent variable (X) values, accounting for uncertainty.

A statistical method used to model the relationship between multiple independent variables and a dependent variable.

The value of the dependent variable when all independent variables are zero, often denoted as the starting point.

FEV1 = 5 + 1.4 × Male - 0.03 × age describes how FVC varies with gender and age.

The estimated Forced Vital Capacity (FVC) based on the regression equation for given values of age and gender.

Avoid using regression equations to predict values outside the range of the data.

The variance of the residuals is consistent across all values of X.

The relationship between X and the expected value of Y is linear.

Observations should not influence each other.

For each X value, Y should follow a normal distribution.

A range around the regression line that indicates confidence in predictions.