Suppose you are looking at the results of a multiple regression using 3 ‘x’ variables and you see that the RSquared is high (close to 1.0), the F-Statistic is much greater than 10, and t-statistics are as follows: 6.1 for variable x1, -.51 for variable x2, and -4.51 for variable x3. What should you do using this information? … conclude that the regression and all of the x variables used have no value … conclude that the regression and all of the x variables used have useful value … conclude that the regression might be useful but should be rerun dropping out the x3 variable since the -4.51 t-value indicates that the x3 variable is not useful … conclude that the regression might be useful but should be rerun dropping out the x2 variable since the -.51 t-value indicates that the x2 variable is not useful

If a scatterplot is created in excel, and a line of regression is fit along with a derived functional form, what does it mean to describe and interpret them? What conclusions would be made about relationships between two recorded variables?

The table below gives the number of weeks of gestation and the birth weight (in pounds) for a sample of five randomly selected babies. Using this data, consider the equation of the regression line, ŷ = bọ + b1x, for predicting the birth weight of a baby based on the number of weeks of gestation. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Weeks of Gestation 33 34 36 38 41 Weight (in pounds) 6. 6.1 6.8 7.3 7.9 Table Copy Data Step 5 of 6: Find the error prediction when x = 36. Round your answer to three decimal places.

Sir Francis Galton, in the late 1800 s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of variables such as the size of parents and the size of their offspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest (adult) son. The data are given in tabular form and also displayed in the scatter plot below, which gives the least-squares regression line as well. The equation for this line is y =95.35 +0.48 x Height of Height of son, father, x (in centimeters) centimeters) y (in 175.8 178.1 193.5 190.3 210. 183.2 176.6 173.3 171.1 200- 183.8 186.5 190. 175.8 175.6 180 159.8 167.2 xx 170 155.5 175.8 162.0 172.1 160- 190.9 188.0 150 180.1 187.1 Tdo ito Ido 130 200 z1o 186.9 177.5 171.5 181.5 Height of father, x (in centimeters) 190.5 195.7 201.0…