With reference to Exercise 11.6 on page 399, (a) evaluate s2; (b) construct a 99% condence interval for 0; (c) construct a 99% condence interval for 1.
T wo quantitative variables: Linear Regression Purpose: How to use one variable to predict the other when there is a linear trend. Linear regression: process of fitting a line to a set of data, the line of best fit is called the regression line. Equation for a line: y=a+bx, where a is the y-intercept and b represents the slope of the line. Finding the regression line means finding values for the slope and intercept of the line that best describes the linear trend of the data. Example: If data pairs are (x,y) with as the explanatory variable and y as the response, the regression line is given by y(hat)=a+bx. Regression line is often called a prediction equation because we can use it to make predictions in certain situations, for example in a tipp
