1. What is the predicted test score for a student whose attendance isequal to one standard deviation above mean attendance? onestandard deviation below mean attendance?2. Interpret the slope coefficient and the intercept coefficient in words.3. How many fewer classes would a student have to attend for theirtest score to go down by 10 points? sum test2 attVariableObsМeanStd. Dev.MinMaxtest22581.1616.164983698att2581.1180346· regress test2 attSourcedfMSNumber of obs25F(1, 23)2.43Model598.5333331 598.533333Prob > F0.1329Residual5672.8266723 246.644638R-squared0.0954Adj R-squared0.0561Total6271.3624 261.306667Root MSE15.705test2Coef.Std. Err.tP>|t|[95% Conf. Interval]att4.4666672.8673141.560.133-1.46482410.39816_cons45.4266723.152561.960.062-2.46805293.32139II I| || II ||||

1. From the output, the regression equation is, Y=4.4667X+45.4267. E(X)=8, SD(X)=1.118 X=one…

Answered: What is the predicted test score for a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

1. What is the predicted test score for a student whose attendance is
equal to one standard deviation above mean attendance? one
standard deviation below mean attendance?
2. Interpret the slope coefficient and the intercept coefficient in words.
3. How many fewer classes would a student have to attend for their
test score to go down by 10 points?

sum test2 att
Variable
Obs
Мean
Std. Dev.
Min
Max
test2
25
81.16
16.16498
36
98
att
25
8
1.118034
6
· regress test2 att
Source
df
MS
Number of obs
25
F(1, 23)
2.43
Model
598.533333
1 598.533333
Prob > F
0.1329
Residual
5672.82667
23 246.644638
R-squared
0.0954
Adj R-squared
0.0561
Total
6271.36
24 261.306667
Root MSE
15.705
test2
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
att
4.466667
2.867314
1.56
0.133
-1.464824
10.39816
_cons
45.42667
23.15256
1.96
0.062
-2.468052
93.32139
II I| || II ||||

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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