Consider the following data for two variables, x and y. x 22 24 26 30 35 40 y 11 21 34 36 39 36 (a) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x. (Round b0 to one decimal place and b1 to three decimal places.) ŷ = (b) Use the results from part (a) to test for a significant relationship between x and y. Use ? = 0.05. Find the value of the test statistic. (Round your answer to two decimal places.) F = Find the p-value. (Round your answer to three decimal places.) p-value = Is the relationship between x and y significant? Yes, the relationship is significant.No, the relationship is not significant. (c) Develop a scatter diagram for the data. A scatter diagram has 6 points. The horizontal axis ranges from 20 to 45 and is labeled: x. The vertical axis ranges from 0 to 45 and is labeled: y. Moving from left to right, the leftmost point is at (22, 7), with the next 4 points extending upward, the first two rising more steeply than the next two. The last point goes back down. A scatter diagram has 6 points. The horizontal axis ranges from 20 to 45 and is labeled: x. The vertical axis ranges from 0 to 45 and is labeled: y. Moving from left to right, the leftmost point is at (22, 39), with the next 5 points extending downward in a diagonal direction. A scatter diagram has 6 points. The horizontal axis ranges from 20 to 45 and is labeled: x. The vertical axis ranges from 0 to 45 and is labeled: y. Moving from left to right, the leftmost point is at (22, 11), with the next 4 points extending upward, the first two rising more steeply than the next two. The last point goes back down. A scatter diagram has 6 points. The horizontal axis ranges from 20 to 45 and is labeled: x. The vertical axis ranges from 0 to 45 and is labeled: y. Moving from left to right, the leftmost point is at (22, 36), with the next point extending upward. The last 4 points then continue downward in a diagonal direction. Does the scatter diagram suggest an estimated regression equation of the form ŷ = b0 + b1x + b2x2? Explain. No, the scatter diagram suggests that a linear relationship may be appropriate.Yes, the scatter diagram suggests that a curvilinear relationship may be appropriate. No, the scatter diagram suggests that a curvilinear relationship may be appropriate.Yes, the scatter diagram suggests that a linear relationship may be appropriate. (d) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to one decimal place and b1 to two decimal places and b2 to four decimal places.) ŷ = (e) Use the results from part (d) to test for a significant relationship between x, x2, and y. Use ? = 0.05. Is the relationship between x, x2, and y significant? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = Is the relationship between x, x2, and y significant? Yes, the relationship is significant.No, the relationship is not significant. (f) Use the model from part (d) to predict the value of y when x = 25. (Round your answer to three decimal places.)
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.
x | 22 | 24 | 26 | 30 | 35 | 40 |
---|---|---|---|---|---|---|
y | 11 | 21 | 34 | 36 | 39 | 36 |
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