Suppose that a researcher, using data on class size (CS) and average test scores from 100 third-grade classes, estimates the OLS regression TestScore = 478.7680+ (-5.3544)x CS, R = 0.07, SER= 10 6 (18.7680) (2.1658) Construct a 95% confidence interval for B,, the regression slope coefficient. The 95% confidence interval for B1, the regression slope coefficient, is ( ) (Round your responses to two decimal places.) The t-statistic for the two-sided test of the null hypothesis Ho: B1 =0 is (Round your response to four decimal places.) Note: Assume a normal distribution. The p-value for the two-sided test of the null hypothesis Ho: B1 = 0 is (Round your response to four decimal places.) Do you reject the null hypothesis at the 1% level? O A. No, because the p-value is greater than 0.01. O B. Yes, because the p-value is less than 0.01. O C. Yes, because the t-statistic is greater than 2.58. O D. Yes, because the t-statistic is less than 2.58. The p-value for the two-sided test of the null hypothesis Ho: B1 = - 5.2 is (Round your response to four decimal places.) Without doing any additional calculations, determine whether -5.2 is contained in the 95% confidence interval for B,
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.
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