APPLIED GENERAL STATISTICS: THEORY AND METHODS The data is given below: enotaln ods sbulonoso d Class 8. loins 5 9 5 2 Co 4 attendance Academic 51 28 49 60 32 47 40 39 35 42 101 performance Required: Identify the key variables in the study i. Jsdi novioS Classify the variables into dependent and independent ii. State the nature of the variables State the simple linear regression model that can capture the study (Prob abtistíc rodel v. Use the method of the ordinary least squares to estimate the regression coefficients and interpret your iv. result vi. Use the fitted equation to predict the value of the dependent variable when the independent is 30 X vii. State three factors that can be captured by the error term viii. Assess the adequacy of the fittèd regression model using the ANOVA method (Global F-test) assume ɑ = 5% IX. Estimate the standard error of the slope and the inter An Shot on S11 lite 6 1, 3.
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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