Terms
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
Help!
|
|
||||
|---|---|---|---|---|---|
| ANOVA Table | show 🗑
|
||||
| Coefficient of Determination | show 🗑
|
||||
| show | The interval estimate of the mean value of y for a given value of x
🗑
|
||||
| show | A measure of the strength of the linear relationship between two variables
🗑
|
||||
| show | The variable that is being predicted or explained. It is denoted by y.
🗑
|
||||
| show | The estimate of the regression equation developed from sample data by using the least squares method. For simple linear regression, the estimated regression equation is yˆ = b0 + b1x.
🗑
|
||||
| show | Observations with extreme values for the independent variables
🗑
|
||||
| show | The variable that is doing the predicting or explaining. It is denoted by x
🗑
|
||||
| show | An observation that has a strong influence or effect on the regression results.
🗑
|
||||
| Ith Residual | show 🗑
|
||||
| show | A procedure used to develop the estimated regression equation. The objective is to minimize o( yi − yˆi)2
🗑
|
||||
| show | The unbiased estimate of the variance of the error term s2. It is denoted by MSE or s2.
🗑
|
||||
| show | A graph of the standardized residuals plotted against values of the normal scores. This plot helps determine whether the assumption that the error term has a normal probability distribution appears to be valid
🗑
|
||||
| Outlier | show 🗑
|
||||
| Prediction Interval | show 🗑
|
||||
| show | The equation that describes how the mean or expected value of the dependent variable is related to the independent variable; in simple linear regression, e(y)=b0 +b1x.
🗑
|
||||
| Regression Model | show 🗑
|
||||
| show | The analysis of the residuals used to determine whether the assumptions made about the regression model appear to be valid. Residual analysis is also used to identify outliers and influential observations
🗑
|
||||
| Residual Plot | show 🗑
|
||||
| Scatter Diagram | show 🗑
|
||||
| Simple Linear Regression | show 🗑
|
||||
| show | The square root of the mean square error, denoted by s. It is the estimate of s, the standard deviation of the error term e
🗑
|
||||
| show | The value obtained by dividing a residual by its standard deviation
🗑
|
||||
| Adjusted Multiple Coefficient of Determination | show 🗑
|
||||
| show | An independent variable with categorical data
🗑
|
||||
| Cook's Distance Measure | show 🗑
|
||||
| show | A variable used to model the effect of categorical independent variables. A dummy variable may take only the value zero or one
🗑
|
||||
| show | The estimate of the logistic regression equation
based on sample data ; that is, yˆ=estimate of P(y=1ux ,x ,...,x )
🗑
|
||||
| Influential Observation | show 🗑
|
||||
| Least Squares Method | show 🗑
|
||||
| show | A measure of how far the values of the independent variables are from their mean values
🗑
|
||||
| Multicollinearity | show 🗑
|
||||
| Multiple Coefficient of Determination | show 🗑
|
||||
| show | Regression analysis involving two or more independent variables
🗑
|
||||
| show | The mathematical equation relating the expected value or mean value of the dependent variable to the values of the independent variables; that is, E(y)=b0 +b1x1 +b2x2 +...+bpxp
🗑
|
||||
| Multiple Regression Model | show 🗑
|
||||
| show | An observation that does not fit the pattern of the other data
🗑
|
||||
| Studentized Deleted Residuals | show 🗑
|
||||
| show | in an additive decomposition model the actual time series value at time period t is obtained by adding the values of a trend component, a seasonal component, and an irregular component
🗑
|
||||
| Cyclical Pattern | show 🗑
|
||||
| Deseasonalized Time Series | show 🗑
|
||||
| Exponential Smoothing | show 🗑
|
||||
| Forecast Error | show 🗑
|
||||
| Horizontal Pattern | show 🗑
|
||||
| Mean Absolute Error | show 🗑
|
||||
| show | the average of the absolute values of the percentage forecast errors
🗑
|
||||
| Mean Squared Error | show 🗑
|
||||
| show | a forecasting method that uses the average of the most recent k data values in the time series as the forecast for the next period
🗑
|
||||
| Multiplicative Decomposition Model | show 🗑
|
||||
| Seasonal Pattern | show 🗑
|
||||
| show | a parameter of the exponential smoothing model that provides the weight given to the most recent time series value in the calculation of the forecast value
🗑
|
||||
| Stationary Time Series | show 🗑
|
||||
| Time Series | show 🗑
|
||||
| show | a time series method that is used to separate or decompose a time series into seasonal and trend components
🗑
|
||||
| Time Series Plot | show 🗑
|
||||
| show | a trend pattern exists if the time series plot shows gradual shifts or movements to relatively higher or lower values over a longer period of time
🗑
|
||||
| Weighted Moving Averages | show 🗑
|
Review the information in the table. When you are ready to quiz yourself you can hide individual columns or the entire table. Then you can click on the empty cells to reveal the answer. Try to recall what will be displayed before clicking the empty cell.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
Created by:
Erika.Meakins
Popular Business sets