In regression, what is the best description of a residual? a. the predicted values b. the...

Question

Question

In regression, what is the best description of a residual?

a. the predicted values

b. the square of actual values

c. the sum of the squared difference between the predicted and actual values

d. error

business accounting

Answer 1

Answer #1

Option C is the answer

Residual - the difference between the predicted value and the dependant varie. So option C is the answer

Answer 2

Similar Homework Help Questions

On a regression output, which gives the average size of deviation between observed y and predicted...

On a regression output, which gives the average size of deviation between observed y and predicted y? a. Multiple R-Squared b. F-Statistic c. Adjusted R-Square d. Residual standard error
In linear regression, what are we doing to determine the parameter estimates for the best fit...

In linear regression, what are we doing to determine the parameter estimates for the best fit line? Minimizing the sum of the squared residuals Minimizing the average value of the residuals Minimizing the average difference between our observed and predicted values. Minimizing the sum of the absolute values of the residuals
The least squares regression line minimizes the sum of theA. Sum of Differences between actual and...

The least squares regression line minimizes the sum of theA. Sum of Differences between actual and predicted Y valuesB. Sum of Squared differences between actual and predicted X valuesC. Sum of Absolute deviations between actual and predicted X valuesD. Sum of Absolute deviations between actual and predicted Y valuesE. Sum of Squared differences between actual and predicted Y values

1. a. At any given combination of values , the assumptions for the multiple regression model...

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...
The “least square regression model” is based on the “best fit” line to the data. This...

The “least square regression model” is based on the “best fit” line to the data. This will determine a line equation for LINEAR data that will minimize “residual” values (difference between actual and “predicted” ) True or False Correlation tells us if there is a relationship between two numeric variables and how strong that relationship is: True or False
Sum of Squared Error (SSE) measures the dispersion between actual and predicted values of the dependent...

Sum of Squared Error (SSE) measures the dispersion between actual and predicted values of the dependent variable. true or false

The multiple regression model represents pricing for residential housing in a certain market. Predicted Price ̂...

The multiple regression model represents pricing for residential housing in a certain market. Predicted Price ̂ = 19,856.56 + 6,985.25 bedrooms + 87.53 square foot. A house in the local market has 5 bedrooms and 3,200 square feet of living area. Use the multiple regression model to determine the price and the residual if the house sells for $352,200. A. predicted price = $334,879, error = $17,321 B. predicted price = $334,879, error = – $17,700 C. predicted price =...
Which of the following measures the difference between an estimate from a linear regression model and...

Which of the following measures the difference between an estimate from a linear regression model and an actual data point? A. R squared B. Residual C. Standard error D. P value
Given a list of predicted values and a list of their corresponding observed values, complete the...

Given a list of predicted values and a list of their corresponding observed values, complete the function FilterOutliers that computes the error of the data, removes outliers, and re-computes the error. The input arguments: • predicted: A double precision 10 array of size n containing the predicted values. . observed: A double precision 1D array of size n containing the observed values • threshold: A double precision positive scalar that determines if an observation is an outlier. The output arguments:...

2. a. What two conditions for linear regression are violated, based on the residual by predicted...

2. a. What two conditions for linear regression are violated, based on the residual by predicted plot at right? For each of the two conditions, briefly explain what aspects of the pattern show that a violation occurred. OOO PE 10 15 20 25 Predicted Value Residual Normal Quantile Plot 500 400 b. What condition for linear regression is violated based on the residual normal quantile plot shown at right? Briefly explain your reasoning Income Normal Quantile