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Mgauna
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| Question | Answer |
|---|---|
| Managerial decisions often are based on the | relationship between two or more variables |
| Regression analysis can be used to develop an equation showing how | the variables are related |
| The variable being predicted is called the | dependent variable and is denoted by y. |
| The variables being used to predict the value of the dependent variable are called the, | independent variables and are denoted by x. |
| Simple linear regression involves | one independent variable and one dependent variable. |
| The relationship between the two variables is approximated | by a straight line. |
| Regression analysis involving two or more independent variables is called, | multiple regression. |
| The equation that describes how y is related to x and an error term is called the, | regression model |
| The simple linear regression model (SLRM) is | y = B0 + B1x +e where: b0 and b1 are called parameters of the model, e is a random variable called the error term. |
| The simple linear regression equation (SLRE) is | E(y) = B0 + B1x Graph of the regression equation is a straight line b0 is the y intercept of the regression line b1 is the slope of the regression line E(y) is the expected value of y for a given x value |
| The estimated simple linear regression equation (ESLRE) | yhat = bo +b1x The graph is called the estimated regression line. b0 is the y intercept of the line b1 is the slope of the line y hat is the estimated value of y for a given x value |
| Assumptions About the Error Term e | 1. The error e is a random variable with mean of zero 2. The variance of e , denoted by e 2, is the same for all values of the independent variable. 3. The values of e are independent 4. The error e is a normally distributed random variable. |
| An outlier is an observation | that is unusual in comparison with the other data. |
| In multiple regression analysis, we interpret each regression coefficient as follows, | bi represents an estimate of the change in y corresponding to a 1-unit increase in xi when all other independent variables are held constant. |
| Assumptions About the Error Term e | The error e is a random variable with mean of zero. The variance of e , denoted by o2, is the same for all values of the independent variables. The values of e are independent The error e is a normally distributed random variable reflecting the deviat |
| In simple linear regression, the F and t tests provide | the same conclusion. |
| In multiple regression, the F and t tests have | different purposes. |
| The F test is used to determine whether a significant relationship exists between the dependent variable and, | the set of all the independent variables |
| The F test is referred to as the | test for overall significance. |
| If the F test shows an overall significance, the t test is used to determine whether each of the, | individual independent variables is significant. |
| A separate t test is | conducted for each of the independent variables in the model. |
| We refer to each of these t tests as a | test for individual significance |
| The term multicollinearity refers to | the correlation among the independent variables. |
| When the independent variables are highly correlated (say, |r | > .7) | it is not possible to determine the separate effect of any particular independent variable on the dependent variable |
| If the estimated regression equation is to be used only for predictive purposes, | multicollinearity is usually not a serious problem. |
| Every attempt should be made to avoid including independent variables that are, | highly correlated |
| The procedures for estimating the mean value of y and predicting an individual value of y in multiple regression are similar, | to those in simple regression |
| We substitute the given values of x1, x2, . . . , xp into the estimated regression equation and use the corresponding value of, | y as the point estimate. |
| For example, x2 might represent gender where x2 = 0 indicates male and x2 = 1 indicates female.In this case, x2 is called, | a dummy or indicator variable |
| In regression analysis, the model in the form y=Bo+B1x is known as | regression model |
| The model developed from sample data that has the form of y= bo +b1x is known as, | estimated regression equation |
| In regression analysis, which of the following is not a required assumption about the error term E, | a. The expected value of the error term is one b. The variance of the error term is the same for all values of x c. the values of the error term are independent d. the error term is normally distributed. a |
| Regression analysis is a statistical procedure for developing a mathematical equation that describes how, | one dependent and one of more independent variable are related |
| In regression analysis, the variable that is being predicted is the | dependent variable. |
| Correlation analysis is used to determine | the strength of the relationship between the dependent and the independent variables. |
| y hat = 120 -10x. if price increased by 2 units then demand is expected to, | decrease by 20 units |
| A least squares regression line | may be used to predict a value of y if the corresponding x value is given |
| Regression analysis between sales (in $1000) and price in dollars. Y hat - 50000 - 8x The above equation implies that an, | increase of $1 in price is associated with a decrease of $8000 in sales. |
| E(y)= Bo+B1X1+B2X2+.......+BpXp= | Multiple Regression Equation (MRE) |
| Yhat= bo+b1x1+b2x2+........bpxp= | Estimated Multiple Regression Equation (EMRE) |
| Y=Bo+B1X1+B2X2+........BpXp+e= | Multiple regression model (MRM) |
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mgauna18