After performing a statistical regression on a set of data the value of the correlation coefficient r was -0.2041. What does that imply about the data?

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 79 78 P Left Arm 177 170 150 148 148 Click the icon to view the critical values of the Pearson correlation coefficient r |x. (Round to one decimal place as needed.) The regression equation is y = Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)

Suppose that you run a correlation and find the correlation coefficient is -0.281 and the regression equation is y = 27.68 2.2x. The mean for the data values was 4.9, and the mean for the Y data values was 17. AT Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when x is 2.3.

The correlation between X and Y is -.27, and the regression equation is Ŷ = - 0.49X + 9.27 before the data are standardised. A: What is the correlation after the data are standardised? B: What would be the standardised regression equation?     A: .27, B: ?̂y = .27?x     A: -.49, B: ?̂y = - .49?x + 9.27     A: -.27, B: ?̂y = - .27?x     A: -.49, B: ?̂y = - .49?x

A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=6.22 b=21.6 r2=0.477481 r=0.691 Use this to predict the final exam score of a student who studies 8 hours per week, and please round your answer to a whole number.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. 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. Age    Bone Density35    35043    34053    33954    32155    310 Step 3 of 6 :   Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.

A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a.  For each 1-cm increase in height, weight tends to increase by about 0.32 kg   b.  There is no association between weight and height   c.  Height accounts for about 32% of the total variability in weight   d.  The correlation between weight and height is about 0.32

c) Show that the coefficient of determination, R², can also be obtained as the squared correlation between actual Y values and the Y values estimated from the regression model where Y is the dependent variable. Note that the coefficient of correlation between Y and X is Eyixi r = And also that ỹ = ŷ (18.75)

Listed below are systolic blood pressure measurements​ (in mm​ Hg) obtained from the same woman. Find the regression​ equation, letting the right arm blood pressure be the predictor​ (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05.   Right Arm 103 102 96 76 76   Left Arm 174 167 149 148 148

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. 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. Hours Unsupervised 0 1 1.5 2.5 4 5.5 6 Overall Grades 98 86 85 83 80 78 67 Table   Step 1 of 6: Find the estimated slope, y intercept, correlation cofficient  Round your answers to three decimal places.