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timer Asked: May 1st, 2020

Question Description

i need just edit

chapter 7


my prof give me some points to fixit he side :

I reviewed your most recent draft of Chapter 7 Exercises (multiple regression).

For question 2a, check the “id” variable to be sure you have the correct cases numbers that need to be removed. There are three cases that should be removed that you did not list. Also, 18 and 1129 will automatically be removed since there is missing data for these cases (no MAH_1 value).

Some of your values are different than what I have. Be sure you run the multiple regression with the profile-b data set and only with cases that are MAH_1 ≤ 22.458.

For question 2h, you should not include the values for the variables that are not statistically significant. The regression equation should only include those variables that are statistically significant.

For 2i, also mention that these two variables are not statistically significant and provide the p (sig) value.

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1. The following output was generated from conducting a forward multiple regression to identify which IVs (urban, birthrat, lnphone, and lnradio) predict lngdp. The data analyzed were from the SPSS country-a.sav data file. a. Evaluate the tolerance statistics. Is multicollinearity a problem? In order to evaluate the presence of multicollinearity, we can exploit the tolerance statistics, calculated as 1-R2. A small tolerance is an indication of the fact that the variable considered is almost a perfect linear combination of the other independent variables already in the equation. Usually, a value of 0.1 serves as the cutoff point. Looking at the table, we can see assess that multicollinearity is not a problem because all tolerance statistics are greater than .1 for all the independent variable in both specifications. b. What variables create the model to predict lngdp? What statistics support your response? The model summary output indicates that the variables used for the forward multiple regression are are respectively lnphone (for the simple regression) and lnphone + birthrate (for the multiple regression). If we look at the p-values, we can see that both of the coefficients are statistically significant in explaining the variation of lngdp. However, that of birthrat is significant at a 5% significance level, differently from that of lnphone which is significant at 1% significance level. Moreover, despite its significance, the coefficient of birthrat is rather small in magnitude and the R^2 change between the regression including only lnphone and the following one with the added birthrate is only 0.004. This is a suggestion of the fact that the explicative power of birthrat is not much high. c. Is the model significant in predicting lngdp? Explain. Regression results indicate an overall model of two predictors (lnphone and birthrat) that significantly predicts lngdp. The R squared = .890, the Adjusted R squared = .888 d. What percentage of variance in lngdp is explained by the model? The model accounted for 89% of the variance in lndgp, as it can be retrieved from the R^2. e. Write the regression equation for lngdp. lngdp = 6.878 + .663*(lnphone) - .013*(birthrat) 2. This question utilizes the data sets profile-a.sav and profile-b.sav, You are interested in examining whether the variables shown here in brackets [years of age (age), hours worked per week (hrs 1), years of education (educ), years of education for mother (maeduc), and years of education for father (paeduc)] are predictors of individual income (rincmdol). Complete the following steps to conduct this analysis. a. Using profile-a.sav, conduct a preliminary regression to calculate Mahalanobis distance. Identify the critical value for chi-square. Conduct Explore to identify outliers. Which cases should be removed from further analysis? In order to calculate Mahalanobis distance, I conducted a preliminary regressio Model Summaryb Std. Error of R Adjusted R the Model R Square Square Estimate 1 .580a .336 .331 4.345 a. Predictors: (Constant), Highest Year of School Completed, Father, Number of Hours Worked Last Week, Age of Respondent, Highest Year of School Completed, Highest Year of School Completed, Mother b. Dependent Variable: RESPONDENTS INCOME The model summary indicates the general statistics of the regression where all the IVs were included into the model ANOVAa Model 1 Regressio n Residual Sum of Squares Mean Square df 6136.473 5 1227.295 12123.027 642 18.883 F 64.994 Sig. .000b Total 18259.500 647 a. Dependent Variable: RESPONDENTS INCOME b. Predictors: (Constant), Highest Year of School Completed, Father, Number of Hours Worked Last Week, Age of Respondent, Highest Year of School Completed, Highest Year of School Completed, Mother The ANOVA table presents the model significantly predicts the dependent variable of rincmdol, with the F-test for the overall significance telling us that at least one of the predictors are statistically significant. F(5, 642) = 64.994, p
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