True and False 1. There are no more than two separate phases to decision making and problem solving. 2. Every manager always has complete control over all inputs and factors. 3. Opportunity cost is only considered by accountants as a way to calculate profits 4. Standard error is always used to evaluate the overall strength of the regression model 5. The t-Stat is used in a similar way as the P-valued is used 6. The P-value is used as R-square is used. 7. R-square is used to evaluate the overall strength of the model. 8. Defining the problem is one of the last things that a manager considers Interpreting Regression Printouts (very brief answers) R² = .859 Intercept T N = 51 Coefficients 13.9 F= 306.5 Standard Error .139 SER=.1036 t Stat P value 99.8 0 .275 .0157 17.5 0 The above table examines the relationship between the nunber, of poor central city households in the U.S. and changes in the costs of college tuition from 1967 to 2019. 9. What is the direction of this relationship? 10. Is it statistically significant? What indicates it? 11. Does the independent variable appear to be an accurate variable for measuring the the independent variable? What indicates it? 12. What percent of the variation in the dependent variable can be explained by variations in dependent variable? 13. What measurement should be used to tell you about the explained variations? 14. Based on the table, what does the R² tell you about the model? 15. Using the results in the table, construct the regression equation (linear equation). 16. How do you interpret the T coefficient?
True and False 1. There are no more than two separate phases to decision making and problem solving. 2. Every manager always has complete control over all inputs and factors. 3. Opportunity cost is only considered by accountants as a way to calculate profits 4. Standard error is always used to evaluate the overall strength of the regression model 5. The t-Stat is used in a similar way as the P-valued is used 6. The P-value is used as R-square is used. 7. R-square is used to evaluate the overall strength of the model. 8. Defining the problem is one of the last things that a manager considers Interpreting Regression Printouts (very brief answers) R² = .859 Intercept T N = 51 Coefficients 13.9 F= 306.5 Standard Error .139 SER=.1036 t Stat P value 99.8 0 .275 .0157 17.5 0 The above table examines the relationship between the nunber, of poor central city households in the U.S. and changes in the costs of college tuition from 1967 to 2019. 9. What is the direction of this relationship? 10. Is it statistically significant? What indicates it? 11. Does the independent variable appear to be an accurate variable for measuring the the independent variable? What indicates it? 12. What percent of the variation in the dependent variable can be explained by variations in dependent variable? 13. What measurement should be used to tell you about the explained variations? 14. Based on the table, what does the R² tell you about the model? 15. Using the results in the table, construct the regression equation (linear equation). 16. How do you interpret the T coefficient?
Excel Applications for Accounting Principles
4th Edition
ISBN:9781111581565
Author:Gaylord N. Smith
Publisher:Gaylord N. Smith
Chapter28: Statistics And Linear Regression (stats)
Section: Chapter Questions
Problem 4R
Related questions
Question
Transcribed Image Text:True and False
1. There are no more than two separate phases to decision making and problem solving.
2. Every manager always has complete control over all inputs and factors.
3. Opportunity cost is only considered by accountants as a way to calculate profits
4. Standard error is always used to evaluate the overall strength of the regression model
5. The t-Stat is used in a similar way as the P-valued is used
6. The P-value is used as R-square is used.
7. R-square is used to evaluate the overall strength of the model.
8. Defining the problem is one of the last things that a manager considers
Interpreting Regression Printouts (very brief answers)
R² = .859
Intercept
T
N = 51
Coefficients
13.9
F= 306.5
Standard Error
.139
SER=.1036
t Stat
P value
99.8
0
.275
.0157
17.5
0
The above table examines the relationship between the nunber, of poor central city
households in the U.S. and changes in the costs of college tuition from 1967 to 2019.
9. What is the direction of this relationship?
10. Is it statistically significant? What indicates it?
11. Does the independent variable appear to be an accurate variable for measuring the
the independent variable? What indicates it?
12. What percent of the variation in the dependent variable can be explained by variations in
dependent variable?
13. What measurement should be used to tell you about the explained variations?
14. Based on the table, what does the R² tell you about the model?
15. Using the results in the table, construct the regression equation (linear equation).
16. How do you interpret the T coefficient?
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