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Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 -102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error t Stat P-value 9.754 0.0000 95.65 17.99 10.89 19.98 9.335 -9.424 -9.969 0.0000 0.0000 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keyp- Keyboard Short

Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 -102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error t Stat P-value 9.754 0.0000 95.65 17.99 10.89 19.98 9.335 -9.424 -9.969 0.0000 0.0000 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keyp- Keyboard Short

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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This image presents the output from a multiple regression analysis examining how the cost of car insurance is related to several variables: the number of car accidents in the last three years, the driver's credit score, and the safety rating of the car. The results are displayed in the following table: **Coefficients Table:** | | Coefficients | Standard Error | t Stat | P-value | |----------------------|--------------|----------------|--------|---------| | Intercept | 933 | 95.65 | 9.754 | 0.0000 | | Car Accidents | 167.94 | 17.99 | 9.335 | 0.0000 | | Credit Score | -102.63 | 10.89 | -9.424 | 0.0000 | | Safety Rating | -199.18 | 19.98 | -9.969 | 0.0000 | **Analysis:** - **Intercept:** The intercept has a coefficient of 933, indicating the baseline cost of insurance when other variables are zero. The p-value suggests significant statistical relevance. - **Car Accidents:** Each additional accident increases the insurance cost by 167.94. The t-stat and p-value signify strong significance. - **Credit Score:** A higher credit score, by one unit, decreases the cost by 102.63. This coefficient's negative value has statistical significance. - **Safety Rating:** A unit increase in safety rating reduces the cost by 199.18, demonstrating significant influence. **Question:** Does the sign of the coefficient for the variable credit score make sense? **Answer Options:** - Yes, because it is expected that as the credit score increases then the cost should decrease. - No, because it is expected that as the credit score increases then the cost should decrease. - Yes, because it is expected that as the credit score increases then the cost should also increase. - No, because it is expected that as the credit score increases then the cost should also increase.
Transcribed Image Text:This image presents the output from a multiple regression analysis examining how the cost of car insurance is related to several variables: the number of car accidents in the last three years, the driver's credit score, and the safety rating of the car. The results are displayed in the following table: **Coefficients Table:** | | Coefficients | Standard Error | t Stat | P-value | |----------------------|--------------|----------------|--------|---------| | Intercept | 933 | 95.65 | 9.754 | 0.0000 | | Car Accidents | 167.94 | 17.99 | 9.335 | 0.0000 | | Credit Score | -102.63 | 10.89 | -9.424 | 0.0000 | | Safety Rating | -199.18 | 19.98 | -9.969 | 0.0000 | **Analysis:** - **Intercept:** The intercept has a coefficient of 933, indicating the baseline cost of insurance when other variables are zero. The p-value suggests significant statistical relevance. - **Car Accidents:** Each additional accident increases the insurance cost by 167.94. The t-stat and p-value signify strong significance. - **Credit Score:** A higher credit score, by one unit, decreases the cost by 102.63. This coefficient's negative value has statistical significance. - **Safety Rating:** A unit increase in safety rating reduces the cost by 199.18, demonstrating significant influence. **Question:** Does the sign of the coefficient for the variable credit score make sense? **Answer Options:** - Yes, because it is expected that as the credit score increases then the cost should decrease. - No, because it is expected that as the credit score increases then the cost should decrease. - Yes, because it is expected that as the credit score increases then the cost should also increase. - No, because it is expected that as the credit score increases then the cost should also increase.
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