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3.3
Multiple Regression
Term | Definition |
---|---|
Multiple regression uses more than one _____ variable. | Independent |
Occurs when a given data point on a time series analysis is affected by a previous data point for that time series. | Autocorrelation |
Occurs when all of the random variables have the same general finite variance. | Homoscedasticity |
The random variables have an unequal spread of variances | Heteroscedasticity |
Can be applied when the dependent variable is a categorical, binary variable. | Logistic Regression |
We use the amount of snow fall in inches to predict if Suzie calls off work. Which regression is this? | Logistic. Calling off work is binary. Yes or no. |
We use the amount of snow fall in inches monthly to predict employee call-off rates. Which regression is this? | Linear or Least Squares regression. Call-off rates are numbers. |
We use the amount of snow fall in inches monthly and the employee's previous call off rates to predict call offs next month. Which regression is this? | Multiple. We have more than one independent variable. |
We use the amount of snow fall in inches monthly and the employee's previous call off rates to predict call offs next month. This analysis can be affected by ______. | Autocorrelation. Whenever time is an independent variable, you can may this. |
y = 50 + 20x1 - 10x2. If x1 = 2 and x2 = 6, solve for y. | y = 50 + 20(2) - 10(6) y = 50 + 40 - 60 y = 30 |