A study is conducted with a group of dieters to see if the number of grams of fat each consumes per day is related to cholesterol levels. The data are shown here. Fat grams x 6.8 5.5 8.2 10 8.6 9.1 8.6 10.4 Cholesterol level y 183 201 193 283 222 250 190 218 Draw the scatter plot. Compute the value of the correlation coefficient. Test the significance of the correlation coefficient at a 0.05.
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.
A study is conducted with a group of dieters to see if the number of grams of fat each consumes per day is related to cholesterol levels. The data are shown here.
Fat grams x 6.8 5.5 8.2 10 8.6 9.1 8.6 10.4
Cholesterol level y 183 201 193 283 222 250 190 218
- Draw the
scatter plot . - Compute the value of the
correlation coefficient. - Test the significance of the correlation coefficient at a 0.05.
- Determine the regression line equation.
- Plot the regression line on the scatter plot.
- Predict y for a specific value of x.
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