Statistical Analysis and Interpretation of Linear Regression Models Using R

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Statistical Analysis and Interpretation of Linear Regression Models Using R (a) The plot of the regression line obtained using R is shown below. It is also showing the distribution of Y for X = 10, 20, 40. (b) The parameter β0 = 200 indicated that the average value of the dependent variable Y is 200 when the independent variable X = 0.
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The parameter β1 = 5 indicated that the increase in the dependent variable Y is 5 units for every one unit increase in the independent variable X . (c) If the slope of the regression model β1 = 0, then it can be concluded that there is a constant growth β0 in the response variable for whatever variation in independent variable exists. It indicated that there is no significant linear relationship between X and Y . (a) Using the following R - code we can generate 100 random observations on predictor X. (b) Using the following R - code we can generate 100 random observations on response variable Y . (c) Using the following R - code we can construct the scatter plot for X and Y generated above and calculate the correlation coefficient.
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The correlation coefficient between X and Y is, r = – 1.

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