10) In a regression line the residual plot should not follow a pattern, for the regression line to be a good fit. If it follows a pattern then there exists a non linear relationship. So the residual plot should be completely random.
answer: a random pattern
11) In regression analysis the definition of error is predicted minus actual value.
Answer: TRUE
& a Question 10 (2 points) In a good regression model the residual plot shows an...
The predicted calories in food items based on grams of fat are represented by the residual plot. What does the pattern in the residual plot indicate about the type of model? The pattern is random, indicating a good fit for a nonlinear model. The pattern shows the points are far from the zero line, indicating a good fit for a linear model. The pattern is random, indicating a good fit for a linear model. The pattern shows the points are far from...
Which of the following is a true statement about residuals? Choose the correct answer below. 0 A. The regression line is the line that minimizes the standard deviation of the residuals. O B. The residual plot for a model that a good fit to the data should not show any pattern or have any unusual features. O C. A residual is the difference between the actual data value and the value predicted by the model 0 D. All of the...
Question 14 Below is a residual plot of a regression model. Residuals Versus the Fitted Values (response is RETURN) 4 1 3 T 1 Standardized Residual 0 -2 -3 10 20 30 Fitted Value The above plot indicates o (a) an incorrect specification of the model. o (b) a clear violation of homoscedasticity. o (c) a high correlation among the residuals. (d) the existence of an outlier o (a) and (b) o (a) and (c) o (b) and (c) o...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation Error Training Error Error Error Error Training set size Training set size Model-1 Training set size Model-3 Model-2 A) Model-1 B) Model-2 C) Model-3 Q55) [1 Point] Maximal margin classifiers are sensitive to outliers in training data. A) True B) False Q56) [1 Point] Soft margin classifiers allows for misclassification in training data. A) True B) False Q57) [1 Point] Which of the following...
Decide (with short explanations) whether the following
statements are true or false.
e) In a simple linear regression model with explanatory variable x and outcome variable y, we have these summary statisties z-10, s/-3 sy-5 and у-20. For a new data point with x = 13, it is possible that the predicted value is y = 26. f A standard multiple regression model with continuous predictors and r2, a categorical predictor T with four values, an interaction between a and...
Suppose Heat Power developed a regression model relating heating average annual pay to the percentage of households using natural gas as heating type. Below is the plot of the corresponding residuals versus the predicted values. Versus File free Rated Value What does the residual plot suggest? The spread of the residuals decreases as the fitted value increases. The plot of residuals shows no unusual pattern. The Equal Variance assumption is satisfied. The spread of the residuals remains unchanged as the...
Question 17 (1 point) In regression analysis, a coefficient of det regression model fits the data better compared to a coefficient of determination closer to0. on closer to 1 means the True False Question 18 (2 points) Which of the following terms is interchangeable with quantitative analysis? management science financial analysis none of the choices are correct economics statistics
Question 5 (1 point) The multiple regression model includes several dependent variables. True False Question 6 (1 point) Dummy variables for regression analysis can take on a value of either -1 or +1. True False Question 7 (1 point) The several criteria (maximax, maximin, equally likely, criterion of realism, minimax regret) used for decision making under uncertainty may lead to the choice of different alternatives. True False Question 8 (1 point)
(10 points) The following regression output is
available. Notice that some of the values are missing.
Predictor Coef SE
Coef T P
Constant 5.932 2.558 2.320 0.068
x 0.511 6.083 0.001
Analysis of Variance
Source DF SS MS F P
Regression 648.72 648.72 57.20 0.001
Residual
Error 56.70
Total 16 705.43
Based on the information given, what is the value of sum of
squares of the X’s (SSxx)?
7626.92
23.142
535.591
None of the above
1. (10 points) Consider the following partially completed computer printout for a regression analysis Based on the information provided, which of the following statements is true at a...