(c) Develop the estimated regression equation that could be used to predict the percentage of games won given the average number of passing yards per attempt. (Round your numerical values to three decimal places.) - -70.391 + 17.175x (4) Provide an interpretation for the slope of the estimated regression equation. O The slope gives the percentage of games won when the average number of passes per attempt is 0. • The slope gives the change in the average number of passes per attempt for every one percentage point decrease in the percentage of games won. O The slope gives the change in the percentage of games won for every one yard increase in the average number of passes per attempt. O The slope gives the average number of passes per attempt when the percentage of games won is 0%. O The slope gives the change in the average number of passes per attempt for every one percentage point increase in the percentage of games won. (e) For the 2011 season, suppose the average number of passing yards per attempt for a certain NFL team was 6.5. Use the estimated regression equation developed in part (c) to predict the percentage of games won by that NFL team. (Note: For the 2011 season, suppose this NFL team's record was 7 wins and 9 losses. Round your answer to the nearest integer.) Compare your prediction to the actual percentage of games won by this NFL team. O The predicted value is higher than O The predicted value is identical to the actual value. O The predicted value is lower than the actual value. actual value.
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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