The table below gives the number of hours spent unsupervised each day as well as
ID: 2947450 • Letter: T
Question
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.
Step 3 of 6 :
Find the estimated value of y when x=5. Round your answer to three decimal places.
USING EXCEL-------------USING EXCEL-------------USING EXCEL-------------USING EXCEL-------------USING EXCEL-------------USING EXCEL-------------USING EXCEL-------------
Hours Unsupervised 0.5 2.5 3 3.5 4.5 5 5.5 Overall GradeS 97 95 92 91 83 78 72Explanation / Answer
Result:
Excel Add on Data analysis is used.
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.
The regression model is significant, F=26.233, P=0.0037.
The regression model is y= 104.4571-5.0286*x
When x=5, predicted y = 104.4571-5.0286*5 =79.3141
=79.314
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.916467802
R Square
0.839913232
Adjusted R Square
0.807895879
Standard Error
4.107136646
Observations
7
ANOVA
df
SS
MS
F
Significance F
Regression
1
442.5143
442.5143
26.23306
0.003701
Residual
5
84.34286
16.86857
Total
6
526.8571
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
104.4571429
3.770649
27.70269
1.15E-06
94.76438
114.1499
Hours Unsupervised
-5.028571429
0.981793
-5.12182
0.003701
-7.55235
-2.50479
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.916467802
R Square
0.839913232
Adjusted R Square
0.807895879
Standard Error
4.107136646
Observations
7
ANOVA
df
SS
MS
F
Significance F
Regression
1
442.5143
442.5143
26.23306
0.003701
Residual
5
84.34286
16.86857
Total
6
526.8571
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
104.4571429
3.770649
27.70269
1.15E-06
94.76438
114.1499
Hours Unsupervised
-5.028571429
0.981793
-5.12182
0.003701
-7.55235
-2.50479
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.