A residual is
Multiple Choice
the difference between the mean of Y and its actual value.
the difference between the regression prediction of Y and its actual value.
None of the options are correct.
the difference between the mean of Y conditional on X and the unconditional mean.
the difference between the sum of squared errors before and after X is used to predict Y.
A residual is:
The difference between the regression prediction of y and its actual value.
Explanation : the above option for residual isthing but the definition of residual.Residual is denoted by e = predicted y - actual y.Reisdual is a error that is not explained by regression line.
We can see the residuals in regression as , the vertical distance between the regression line and given observations lie above and below the line.
A residual is Multiple Choice the difference between the mean of Y and its actual value....
The t value is a ratio of Multiple Choice the difference between standard deviations and the squared means. O sums of squared means and variability between groups. sums of squared means and variability within groups. the difference between group means and variability within groups.
Which statement is not correct? Multiple Choice R-squared is a measure of the degree of variability in the dependent variable about its sample mean explained by the regression line. The adjusted R-squared measure should be used in the case of more than one independent variable. The null hypothesis that R2 = 0 can be tested using the F-statistic. Forecasters should always select independent variables on the basis of R2. All of the options are correct.
are the assumptions behind any multiple regression model? (b). For a multiple regression model Y-Bo + βιΧ. + β2X2 +β3Xs + € where is the error term, to represent the relationship between Y and the four X- variables. We got the following results from the data: Source Sum of Squares degrees of freedom mean squares Regression 1009.92 Residual Total 2204.94 34 And also you are given: Variable X1 Σ.tx-xr 123.74 72.98 12.207 -Pr values -11.02 5.13 X2 X3 Y-intercept is...
The least squares regression line is the line: Multiple Choice which is determined by use of a function of the distance between the observed Y ’s and the predicted Y’s. which has the smallest sum of the squared residuals of any line through the data values. for which the sum of the residuals about the line is zero. which has all of the above properties. which has none of the above properties.
1. a. At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values has? b. What is the point estimate for the constant variance? c.Which of the following is the sum of the squared differences between the predicted values of the dependent variable and the mean of the dependent variable, the explained variation? d.The null hypothesis for the overall F-test states that: At least one ββis not equal...
I need help answering these questions MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answer the question. 1) The purpose of a linear regression line is to A) calculate the correlation coefficient B) display the bivariate distribution of X and Y C) identify the mean of the X and Y variables D) predict one set of scores from another set 2) The general equation for a straight line is expressed as A) Y - X- B)...
Residual value is Multiple Choice The cost of an asset minus its accumulated depreciation An estimate of the asset's value at the end of its useful life All of these The same as an asset's service life O Another name for market value
The difference between actual quantity of input used and the standard quantity of input used results in a Multiple Choice Controllable variance Standard variance o O Budget variance Quantity variance
1.) What is the difference between a simple regression model and a multiple regression model? a.) There isn’t one. The two terms are equivalent b.) A simple regression model has a single predictor whereas a multiple regression model has potentially many c.) A simple regression model can handle only limited amounts of data whereas a multiple regression model can handle large data sets d.) A simple regression is appropriate for a dichotomous outcome variable, whereas a multiple regression model should...
1. Basic concepts of linear regression Aa Aa Match the following key linear regression terms with their respective descriptions Residual Least Squares Criterion Response Variable Explanatory Variable Regression Equation A procedure used to develop an estimate of the regression equation that minimizes the sum of the squared errors The variable that you are predicting or explaining The variable that is doing the predicting or explaining The equation that describes the relationship between the response variable and the explanatory variable The...