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Question

8-"y. A, ! .0. TNorm all 1 No Spac Heading 1 Heading 2 Title Paragraph Styles The following regression looks at the murder rate in different states, and relates it to the High School graduation rate: 2) Model 1: OLS, using observations 1-50 Dependent variable: Murder coefficient std. error t-ratio p-value 6.216 1.17e-07* 19.2224 -0.223024 0.0575813 3.873 0.0003 const 3.09249 HSGrad Mean dependent var 7.378000 S.D. dependent var 3.691540 Sum squared resid 508.7450 S.E. of regression 3.255588 R-squared F (1, 48) Log-likelihood Schwarz criterion 265.7141 Hannan-Quinn 0.238116 Adjusted R-squared 0.222243 0.000325 -128.9450 Akaike criterion261. 8901 263.3463 15.00169 P-value (F) Interpret the regression coefficient. How much of the variation is explained by this one variable? a. b.

Explanation / Answer

Question 2

(a) Here regression coefficient -0.223024 means that if we increase High school graduation rate by 1 unit, then it will reduce the murder rate by 0.223024 units.

(b) Here r- square is 0.238116 that means 23.81% of variation in murder rate is explained by the variation in high school graduation rate.

Question 3

Here adjusted R - squared for the second model is less than R - square for the first model and the p - value for the new independent variable "income", is greater than 0.05. So, the variable "income"doesn't increase the ability to explain the variation in the model.

Question 4

Here in this model a new independent variable "illiteracy" is added here which has significantly increases the value of r- square and adjusted r - square. Similarly, p - value for this new variable is less than 0.05 so it is the most suitable indepenent variable.

Question 5

Here we have to eliminate the independent variables "Income" from the model and check again with only "HSgrad" and "Illiteracy". "HSgrad" may also be eliminated if its p - value is greater than 0.05 in the new suggested model.

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