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 3 4 4.5 5 5.5 6 Overall Grades 83 78 70 69 67 64 63
Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.
Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places
Step 3 of 6 : Determine the value of the dependent variable yˆy^ at x=0x=0.
Step 4 of 6 : Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆy^.
Step 5 of 6 : Determine if the statement "All points predicted by the linear model fall on the same line" is true or false. Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.
independent variable x= 0,3,4,,4.5,,5,5.5,6
dependent variable- y=83, 78,70, 69,67,64,63
step1-
slope=b1=r*(SDy/SDx), where r= correlation coefficient between x and y.
SDy= standard deviation of y, SDx= standard deviation of x
r=-0.9683105, SDy=7.367884, SDx=2.020726
b1=-3.530612=slope
step2-
intercept=b0=ybar-(b1*xbar)
ybar=70.57143=mean of y, xbar=4=mean of x
b0=84.69388=intercept
step3-
at x=0
y^ is - y^=b0=84.69388
step4-
y=84.69388-3.530612*x
at x=1
y^=84.69388-3.530612=81.16327
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