Regression Statistics Multiple R R Square ANOVA 0.79 df MS ignificance F 0.62 Regression 4 6548.49 1637.12 88.367068 1.384E-44 Adjusted R 0.61 Residual 219 4057.28 18.53 Standard Er 4.30 Total 223 10605.77 Observation 224.00 Coefficients itandard Errc t Stat P-value Lower 95% Upper 95% Lower 95.0%Jpper 95.0% Intercept 231.89 3.370 68.82 2.57E-150 225.253 238.535 225.25349 238.53537 STR -0.50 0.131 -3.79 0.0001915 -0.754 -0.238 -0.7538457 -0.2384408 TSAL -0.02 0.075 -0.31 0.7546528 -0.170 0.124 -0.17037 0.1236846 INC 0.29 0.034 8.51 2.776E-15 0.225 0.361 0.2250472 0.3607169 SGL -0.88 0.174 -5.06 9.052E-07 -1.220 -0.536 -1.2202964 -0.535734
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the
(a) What proportion of the variation in MCAS score is explained by the explanatory variables?
(b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly.
(c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly.
(d) Suppose a second regression model (Model 2) was generated using only the variables STR, INC, and SGL. For that model
Which model is better, Model 2 or Model 1, the model given above in the table with explanatory variables STR, INC, TSAL, SGL? Explain briefly.
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