I need just the final answer of these question (Hint: No need to show calculatio
ID: 2922016 • Letter: I
Question
I need just the final answer of these question (Hint: No need to show calculation.... Just show Final Answer and Right one)
2) What does the regression line represent?
a) It is the "line of best fit" which minimizes that distance between the line and each of the points
on the predicted variable oni a scatterplot.
b) The line allows us to predict one score, given the value of independent variable.
c) The regression line represents the distance between each individual point and the regression
line, called the error in prediction.
d) The line allows us to see how far off each of the data points are from a perfect correlation. If
the correlation were perfect, all the data points would be lined up along a 45o angle.
e) All of the above
7) What is the standard error of estimate?
a) It is a statistical technique using a regression equation to determine the ‘line of best fit’ from
which a Y score can be predicted from an X score
b) It allows us to predict one score, given the value of independent variable.
c) It is the measurement for how well (or poorly) one variable predicts another variable.When
these differences from a distribution of data points are averaged, it reflects how good the
prediction is.
d) All of the above
Use the data below to answer questions 11 through 14 (NOTE THAT X= # of delinquent friends
and Y = # of days expelled from school).
11) Calculate the slope of the regression line (or b in the regression equation).
a) 1.66
b) 1.54
c) 1.23
d) 1.80
12) Calculate the point at which the line crosses the y-axis (or a in the regression
equation)
a) 6.63
b) 5.67
c) 3.34
d) 4.82
14) If X is equal to 12 delinquent friends, what is the predicted value of Y (or Y’)?
a) 27.31
b) 25.67
c) 23.43
d) none of the above
Explanation / Answer
Q2.
e) All of the above
Q7.
c) It is the measurement for how well (or poorly) one variable predicts another variable.When
these differences from a distribution of data points are averaged, it reflects how good the
prediction is
Q11. Line of Regression Y on X i.e Y = bo + b1 X
calculation procedure for regression
mean of X = X / n = 4.8571
mean of Y = Y / n = 14.4286
(Xi - Mean)^2 = 26.85714
(Yi - Mean)^2 = 95.71
(Xi-Mean)*(Yi-Mean) = 48.42855
slope = b1 = (Xi-Mean)*(Yi-Mean) / (Xi - Mean)^2
= 48.42855 / 26.85714
= 1.80319 ~ 1.80
Q12.
bo = Y / n - b1 * X / n
bo = 14.4286 - 1.80319*4.8571 = 5.67032
Q13.
Y'=5.67032+1.80319* 12 = 27.31
X Y (Xi - Mean)^2 (Yi - Mean)^2 (Xi-Mean)*(Yi-Mean) 3 11 3.44882 11.7553 6.36725 2 9 8.16302 29.4697 15.51005 5 13 0.02042 2.0409 -0.20415 7 17 4.59202 6.6121 5.51025 4 14 0.73462 0.1837 0.36735 5 16 0.02042 2.4693 0.22455 8 21 9.87782 43.1833 20.65325