The increasing annual cost (including tuition, room, board, books, and fees) to attend college has been widely discussed.† The following random samples show the annual cost of attending private and public colleges. Data are in thousands of dollars. Private Colleges 52.8 42.2 44.0 33.3 45.0 29.6 44.8 36.8 51.5 43.0 Public Colleges 20.3 22.0 28.2 15.6 24.1 28.5 22.8 25.8 18.5 25.6 14.4 21.8 (a) Compute the sample mean (in thousand dollars) and sample standard deviation (in thousand dollars) for private colleges. (Round the standard deviation to two decimal places.) sample mean = ? thousand sample standard deviation = ? thousand Compute the sample mean (in thousand dollars) and sample standard deviation (in thousand dollars) for public colleges. (Round the standard deviation to two decimal places.) sample mean = ? thousand sample standard deviation= ? thousand (b) What is the point estimate (in thousand dollars) of the difference between the two population means? (Use Private − Public.) $ ? thousand Interpret this value in terms of the annual cost (in dollars) of attending private and public colleges. We estimate that the mean annual cost to attend private colleges is $ more than the mean annual cost to attend public college (c) Develop a 95% confidence interval (in thousand dollars) of the difference between the mean annual cost of attending private and public colleges. (Use Private − Public. Round your answers to one decimal place.) $ thousand to $ thousand
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.
52.8 | 42.2 | 44.0 | 33.3 | 45.0 |
29.6 | 44.8 | 36.8 | 51.5 | 43.0 |
20.3 | 22.0 | 28.2 | 15.6 | 24.1 | 28.5 |
22.8 | 25.8 | 18.5 | 25.6 | 14.4 | 21.8 |
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