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On your own exercise 7 Group 1 is the row of students by the wall and group 1a is the continuation of the row on the door side of the room. OPEN House.sav Group Task Using correlation and 1 regression explain the influence of COLLEGE on POVERTY variable. Analyze> Correlate> Bivariate> enter variables: POVERTY, COLLEGE, FEMCHILD, Analyze> Regression>Linear, Dependent = POVERTY and Independent =COLLEGE. Select OK. 1a Graphs> Legacy Dialogs > Scatter/dot> Simple> Define> Y Axis (dependent variable) and X Axis is independent variable Analyze> Regression>Linear, Dependent = POVERTY and Independent =FEMCHILD. Select OK Analyze > Regression> Linear, Dependent =POVERTY and Independent =FEMCHILD and COLLEGE. Select OK Question 1. Is there as statistically significant correlation between POVERTY and COLLEGE educated or the number of FEMCHILD households in a district? What is your evidence? 2. Why is one Pearson value positive and the other negative? 3. What is the PRE value for the influence of COLLEGE on POVERTY? 4. If a district had a 30% college educated population what percentage of poverty would one anticipate? USE MODEL to predict. Y est. poverty = 5. Produce graph of relationship between College and Poverty. 1. What is the PRE for POVERTY and FEMCHILD? How much of a change in the PRE is realized when adding COLLEGE as a variable? 2. In the ideal model which factor more fully explains district poverty and what evidence do you have for your choice? 3. Should you retain or reject the null and based on what evidence? 2 Using correlation and regression explain the influence of URBAN on a support for John MCCAIN. Analyze> Correlate> Bivariate> enter variables: MCCAIN , URBAN, OWNOCC and SINGUNIT Analyze> Regression>Linear, Dependent = MCCAIN And Independent = SINGUNIT Select OK. 2a Graphs> Legacy Dialogs > Scatter/dot> Simple> Define> Y Axis (dependent variable) and X Axis is independent variable Analyze> Regression>Linear, Dependent = MCCAIN and Independent =URBAN. Select OK Analyze > Regression> Linear, Dependent =_MCCAIN and Independent =URBAN and SINGUNIT.. Select OK 1. What is the correlation and the significance of support for John MCCAIN based on a district being urbanized [URBAN] and having single unit occupied [SINGUNIT] housing? 2. Why is the coefficient or Person’s value for SINGUNIT and McCain positive? 3. What is the model of the line from which one could compute MCCAIN support from districts with high single unit housing occupancy if the formula began: Ymccain est. = 4. Graph a scatter plot for this relationship and comment on whether you think the model (line of best fit) is strong or weak? What is your evidence 1. What is the PRE value for the relationship between McCain and URBAN? What can one conclude from this value? 2. What change occurs in McCain support when one includes in the model SINGUNIT? Is this addition important and if so why? 3. Based on this output should you retain or reject the null hypothesis with what evidence? [- Reject; evidence is sig. =.000 ANOVA and sig. t-tests for both variables.] 4. If one drew a line that best reflected where URBANIZATION and SINGUNIT crossed the Y axis, the value of Y at that point would be equal to what number? 3 Using correlation and regression explain the influence of URBAN on a support for Barrak OBAMA. 1. What is the correlation and the significance of support for OPBAMA based on a district being urbanized [URBAN] and having single unit occupied [SINGUNIT] housing? Analyze> Correlate> Bivariate> enter variables: OBAMA , URBAN, and SINGUNIT 2. Why is the coefficient or Person’s value for SINGUNIT and OBAMA negative? Analyze> Regression>Linear, Dependent = OBAMA And Independent = SINGUNIT Select OK. 3a Graphs> Legacy Dialogs > Scatter/dot> Simple> Define> Y Axis (dependent variable) and X Axis is independent variable Analyze> Regression>Linear, Dependent = OBAMA and Independent =URBAN. Select OK Analyze > Regression> Linear, Dependent =OBAMA and Independent =URBAN and SINGUNIT.. Select OK 3. What is the model of the line from which one could compute OBAMA support from districts with high single unit housing occupancy if the formula began: Yobama est. = 4. Graph a scatter plot for this relationship and comment on whether you think the model (line of best fit) is strong or weak? What is your evidence? 1. What is the PRE value for the relationship between OBAMA and URBAN? What can one conclude from this value? 2. What change occurs in OBAMA support when one includes in the model SINGUNIT? Is this addition important and if so why? 3. Based on this output should you retain or reject the null hypothesis with what evidence? 4. If one drew a line that best reflected where URBANIZATION and SINGUNIT crossed the Y axis the value at that point would be equal to what number? 4 Using correlation and regression explain the influence of race LATINO and BLACK on MCCAIN support variable. Analyze> Correlate> Bivariate> enter variables: MCCAIN, BLACK, LATINO Analyze> Regression>Linear, Dependent = MCCAIN and Independent = BLACK and LATINO Select OK. 4a Analyze> Regression>Linear, Dependent = McCain and Independent =WHITE. Select OK Graphs> Legacy Dialogs > Scatter/dot> Simple> Define> Y Axis (dependent variable) and X Axis is independent variable Questions, concerns or fuzziness? 1. What is the correlation between the percentage of Blacks and Latino’s in a district and its support for McCain? 2. In a regression analysis how much is explained by the % of black and Latino populations in a district with regard to support for McCain? What is the evidence? 3. How much needs to be explained to understand McCain support? 4. If a congressional district had neither Black nor Latino residents what would be the level of support for McCain? What is your evidence? 1. What is the proportional reduction in error in estimating McCain support if one knows the percentage of the district that is White? 2. What would be McCain’s support in a district that was 70% white? What is your evidence and calculations? Ymccain = 3. Display a scatter plot that depicts the influence of white residents on support for John McCain.