Interpret the slope of the least-squares regression line in context. b. Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins. c. What is the value of the correlation coefficient for the sample?
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.
| Term | Coef | SECoef | T-Value | P-Value |
| Constant | 10834 | 9716 | 1.12 | 0.274 |
| Wins | 235 | 119 | 1.98 | 0.058 |
| S=7,377 | R−sq=12.29% | AdjR−sq=9.16% |
a. Interpret the slope of the least-squares regression line in context.
b. Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins.
c. What is the value of the
d. If the point representing 64 wins and attendance of 40,786 people per game is removed from the set of data and a new
- The slope of the least-squares line:
- The correlation coefficient:
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