11 The final score (Y ) of students in a course was correlated with score (X) in entrance test. The relevant data in coded units are as follows: Y : 2 8. 6. 4 X : 1 6. 3 4 2 (a) Obtain the linear regression equation of Y on X. (b) Obtain the correlation coefficient and interpret it. (c) Predict Y when X the observed value of 6. Explain the reasons for the difference 4. If the predicted value is different from %3D

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, y = bo + 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 4 of 6: Find the estimated value of y when x = 36. Round your answer to three decimal places.

The following table shows students’ number of absences, x, and the student’s final grade,, y.# of absences x         6     2   15     9   12    5       8        Final grade  y           82   86   43   74   58   90   78a)  Calculate r, the correlation coefficient    ________________b)   Find the equation of the regression line  __________________________________c)  If a student is absent 4 times,, what grade does your regression line predict?__________________

There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?

A linear regression analysis reveals a strong, negative linear relationship between x and y. Which of the following could possibly be the results from this analysis? (A) ŷ 13.1 27.4x, r = 0.85 (B) == 27.4+ 13.1x, r = -0.95 (D) == (C) ŷ 13.1+ 27.4x, r = 0.95 542 385x, r = -0.15 (E) 0.85 0.25x, r = -0.85