Week 9 Quiz: Regression

Front 1

Adjusted R-squared

Back 1

used to avoid overestimating the percentage of variance in the outcome explained by the model, such as when there are a large number of independent variables with a relatively small sample size.

Front 2

Linear Regression

Back 2

analyzes the relationship between a single independent variable and a single interval or ratio dependent variable, enabling the researcher to make a prediction about a future outcome based on the research data included in the analysis. Normal distribution, homogeneity of variance, independence of errors.

Front 3

Logistic Regression

Back 3

analyzes the relationship between multiple independent variables and a single dependent or outcome variable when the outcome is binary (only has 2 categories).odds ratio is 1. Dichotomous outcome (2 categories). Multivariable.

Front 4

Multiple Regression

Back 4

a statistical method used to look at the relationship between a dependent variable and multiple independent variables to develop a prediction equation based on the research data included in the analysis. controls co-founding variables.

Front 5

Odds ratio (OR)

Back 5

the odds or probability of the outcome occurring divided by the odds or probability of the outcome not occurring.

Front 6

R-squared change

Back 6

the change in the percentage of the variavce in the outcome variable (R2) that is eplained bye the model with the addition of another independent variable.

Front 7

R-squared value (R2)

Back 7

the percentage of the variance in the dependent or outcome variable that is explained by the model

Front 8

Regression

Back 8

a statistical technique that allows the researcher to make a prediction about a future outcome based on the research data included. Tests the relationship. (ex: framington cigs and sex).

Front 9

Regression coefficient

Back 9

the b value, which tells you the rate of change in the outcome or dependent variable with a one-unit increase in the correspondent independent variable.

Front 10

Residual

Back 10

the amount of prediction error in a regression equation.

Front 11

standard error of the estimate

Back 11

the average amount of error there will be in the predicted outcome using a model. The higher the correlation between 2 variables, the lower the

error will be.

Front 12

ordinary least squares (OLS) regression

Back 12

linear, continuous outcome variable, explanatory variables (continuous or categorical). Continuous outcome. Multivariable. *NOTE: null hypothesis slope is 0. *also called simple linear.

Front 13

***Regression summary

Back 13

-uses correlations & powerful tool for examining complex relationships.

Front 14

***Regression Analysis (define)

Back 14

used to make predictions, 1 independent variable is used to predict a dependent variable!

Front 15

*** Linear Regression (define)

Back 15

used to determine a straight-line fit to the data that minimizes deviations from the line.

Front 16

standard regression is also called...

Back 16

Ordinary least squares (OLS)

Front 17

***Logistic regression (define)

Back 17

used to predict the probability of an outcome; includes an odds ratio for relative risk for each predictor.