A regression model is constructed with the goal of predicting the number of motor vehicle accidents in a city per year b

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A Regression Model Is Constructed With The Goal Of Predicting The Number Of Motor Vehicle Accidents In A City Per Year B 1 (40.69 KiB) Viewed 75 times

A Regression Model Is Constructed With The Goal Of Predicting The Number Of Motor Vehicle Accidents In A City Per Year B 2 (36.92 KiB) Viewed 75 times

A regression model is constructed with the goal of predicting the number of motor vehicle accidents in a city per year based upon the population of the city, the number of recorded traffic offenses per year, the number of vehicles per capita in the city and the average annual temperature in the town. A random sample of 50 cities were studied for this purpose. Here is an analysis output on the regression model: ANOVA DF SS MS Regression 4 172.382 43.0955 Residual 45 154.886 3.44191111... Total 49 327.268 F Probability 12.52080562... <0.001 Regression analysis R2 0.52673039... 5 1.85523883. Regression coefficients Estimate Standard Errort Probability 16.07 2.170 7.40552995... < 0.001 1.180 0.1916 6.15866388... <0.001 Intercept Pupulation of oty 0.2305 3.21475054...0.00241734... No. of vehicles 0.741 per capita No. of traffic offenses 0.622 Average annual 0.271 temp. 0.4989 0.3474 1.24674283...0.21894244... 0.7800806... 0.43942741..
MS 12.52080562... < 0.001 DF 55 Regression 4 172.382 43.0955 Residual 45 154.886 3.44191111... Total 49 327.268 Regression analysis R2 0.52673039... 5 1.85523883... Regression coefficients Estimate Standard Error t Probability Intercept 16.07 2.170 7.40552995... < 0.001 Population 1.180 0.1916 6.15866388... <0.001 of city No. of vehicles 0.741 0.2305 3.21475054... 0.00241734... per capita No. of traffic offenses 0.622 0.4989 1.24674283... 0.21894244... Average annual 0.271 0.3474 0.7800806... 0.43942741... temp. -) At a level of significance of 0.05, the result of the F test for this model is that the null hypothesis e rejected. b) Suppose you are going to construct a new model by removing the most insignificant variable. You would first remove: population of city no. of vehicles per capita no. of traffic offenses average annual temp.