Kenneth is studying the relationship between the time spent exercising per day and the time spent outside per day. Data were collected ranging from 20 minutes to 90 minutes per day exercising. The line of best fit for the data is given below. Assume the line of best fit is significant and there is a strong linear relationship between the variables. y= 0.13x + 40.5 Answer the following (each is equally weighted): 1. Identify the explanatory and response variables (make sure your answer is related to the application and not just in terms of x and y). 2. According to the line of best fit, what would be the predicted number of minutes spent outside for someone who spent 70 minutes exercising? 3. Would it be appropriate to use this model to predict the number of minutes spent outside for someone who spent 0 minutes exercising? Why or why not (remember to include appropriate terminology).
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.
Kenneth is studying the relationship between the time spent exercising per day and the time spent outside per day. Data were collected
The line of best fit for the data is given below. Assume the line of best fit is significant and there is a strong linear relationship between the variables.
y= 0.13x + 40.5
Answer the following (each is equally weighted):
1. Identify the explanatory and response variables (make sure your answer is related to the application and not just in terms of x and y).
2. According to the line of best fit, what would be the predicted number of minutes spent outside for someone who spent 70 minutes exercising?
3. Would it be appropriate to use this model to predict the number of minutes spent outside for someone who spent 0 minutes exercising? Why or why not (remember to include appropriate terminology).
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