ASSIGNMENT #4 PROBLEM #1 The data below is from a small dataset in which the following information was recorded about every state in the USA from 2002 data: Percentage of people in the state living below the poverty level High school dropout rate 15 and 16 years old (per 1000 people) High school dropout rate 17 and 18 years old (per 1000 people) Here are the values for each of these three variables: Percentage in poverty: 20.1, 7.1, 16.1, 14.9, 16.7, 8.8, 9.7, 10.3, 22, 16.2, 12.1, 10.3, 14.5, 12.4, 9.6, 12.2, 10.8, 14.7, 19.7, 11.2, 10.1, 11, 12.2, 9.2, 23.5, 9.4, 15.3, 9.6, 11.1, 5.3, 7.8, 25.3, 16.5, 12.6, 12, 11.5, 17.1, 11.2, 12.2, 10.6, 19.9, 14.5, 15.5, 17.4, 8.4, 10.3, 10.2, 12.5, 16.7, 8.5, 12.2 High school dropout rate 15-16 31.5, 18.9, 35, 31.6, 22.6, 26.2, 14.1, 24.7, 44.8, 23.2, 31.4, 17.7, 18.4, 23.4, 22.6, 16.4, 21.4, 26.5, 31.7, 11.9, 20, 12.5, 18, 14.2, 37.6, 22.2, 17.8, 18.3, 28, 8.1, 14.7, 37.8, 15.7, 28.6, 11.7, 20.1, 30.1, 18.2, 17.2, 19.6, 29.2, 17.3, 28.2, 38.2, 17.8, 10.4, 19, 16.8, 21.5, 15.9, 17.7 High school dropout rate 17-18 88.7, 73.7, 102.5, 101.7, 69.1, 79.1, 45.1, 77.8, 101.5, 78.4, 92.8, 66.4, 69.1, 70.5, 78.5, 55.4, 74.2, 84.8, 96.1, 45.2, 59.6, 39.6, 60.8, 47.3, 103.3, 76.6, 63.3, 64.2, 96.7, 39, 46.1, 99.5, 50.1, 89.3, 48.7, 69.4, 97.6, 64.8, 53.7, 59, 87.2, 67.8, 94.2, 104.3, 62.4, 44.4, 66, 57.6, 80.7, 57.1, 72.1 The first value in each variable represents a state. For example, the first value in each variable represents the state of Alabama which shows 20.1% of people living below the poverty line. In Alabama, the high school dropout rate among 15-16s is 31.5 per 1000 people. The high school dropout rate among 17-18 is 88.7 per 1000 people. The second number in each variable represents Alaska with values of 7.1, 18.9, 73.7 respectively. You suspect that higher levels of poverty may contribute to higher high school dropout rates among young people. You are also interested to see whether this changes as people get slightly older. For this problem, you will generate two regression models. 1. In the first one, you will create a regression model attempting to predict the high school dropout rate among 15-16-year-olds based on poverty. That is, poverty level would be the explanatory variable, and high school dropout rate would be the response variable. 2. In the second one, repeat the process but with data from the 17-18-year-olds. Page 1 of 2 For each model, provide a brief discussion. That is, imagine you are providing this analysis to someone who has to make policy decisions, yet has no background in statistics. Try to describe your regression model in real world terms. Part of this kind of discussion should always include how strong you believe the model to be. Hint: There is one numeric value in particular that should give you a sense of the strength of the model. As part of your discussion, also be sure to answer the following questions: Do you think any observations should be removed? That is, are there any outliers or influential points that you believe have an undue influence on the model? Does either model indicate that poverty predicts the high school dropout rate? For one of the age groups? For both age groups? If you believe it is predictive for both age groups, which of the two models do you think is a better predictor of the number of high school dropouts?