The lecture Linear Regression by David Spade, PhD is from the course Statistics Part 1. It contains the following chapters:
Which of the following is not true of the linear model?
Which of the following is not true of the relationship between linear regression and correlation?
Suppose the variables X and Y are linearly related through the regression equation Y= 12.5  0.275 X. What do we know from this equation?
Which of the following is not true of the R² quantity?
When we look at a scatterplot, what indicates a lack of violations of these assumptions?
Suppose the relationship between age and earnings could be represented by the linear regression equation Earnings = 100 + 0.8*Age. What would be the estimated earnings of a person whose age is 43?
Suppose the relationship between age and earnings could be represented by the linear regression equation Earnings = 100 + 0.8*Age. The actual earnings of a person aged 43 are $150. What is the residual error?
Suppose the correlation between two variables is 0.75, standard deviation of y variable is 5 and the standard deviation of x variable is 2.5. What is the slope of the regression line?
Suppose the slope of regression line is 2 and Y variable is 10 when X variable is 3. What is the value of the intercept of the regression equation?
If a rise in health expenditure leads to a positive linear rise in GDP then which regression equation is likely to represent their relationship?
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