Calculate the correlation between the IQ of a person with the number of hoursspent in the class per week and test the hypothesis at 0.05 level of significance,conduct Regression Analysis, Correlation Analysis and construct ANOVA Table. IQ106Hours of Class per Week7861002710150992810329972011312112611017Regression Analysis:IQHours of Class per WeekX2XYY2106711236742498673961002710000270072910150102015050250099289801277278429201031060929878419794091940400113121276913561441126125446723611017121001870289102196106065 20089 57727 ANOVA:ObsIQ Hrs/WK Y-huty- y(y: y)^2y-y(y-y)^2y-y(v-yJ^2(12.37110619.37(0.23)0.05153.02(12.60)158.76(20.7728620.771.171.37431.39(19.60)384.1631002719.790.190.047.2151.987.4054.7641015019.720.120.0130.28916.8830.40924.165992819.860.260.078.1466.268.4070.5661032919.58(0.02)0.009.4288.749.4088.367972020.000.400.160.400.1681131218.88(0.72)0.52(6.88)47.33(7.60)57.769(12.95167.701126.18.95(0.65)0.42(13.60)184.96101101719.09(0.51)0.26(2.09)4.37(2.60)6.76Total10271962.901,927.671,930.40dfSum of SquareMean SquareF Value=0.01regression12.902.90Significance F-5.32residual1,927.67240.96total101,930.40

Given data: X Y 106 7 86 0 100 27 101 50 99 28 103 29 97 20 113 12 112 6 110…

Calculate the correlation between the IQ of a person with the number of hours spent in the class per week and test the hypothesis at 0.05 level of significance, conduct Regression Analysis, Correlation Analysis and construct ANOVA Table

Calculate the correlation between the IQ of a person with the number of hours spent in the class per week and test the hypothesis at 0.05 level of significance, conduct Regression Analysis, Correlation Analysis and construct ANOVA Table

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

IQ
106
Hours of Class per Week
7
86
100
27
101
50
99
28
103
29
97
20
113
12
112
6
110
17
Regression Analysis:
IQ
Hours of Class per Week
X2
XY
Y2
106
7
11236
742
49
86
7396
100
27
10000
2700
729
101
50
10201
5050
2500
99
28
9801
2772
784
29
20
103
10609
2987
841
97
9409
1940
400
113
12
12769
1356
144
112
6
12544
672
36
110
17
12100
1870
289
102
196
106065 20089 5772
7

ANOVA:
Obs
IQ Hrs/WK Y-hut
y- y
(y: y)^2
y-y
(y-y)^2
y-y
(v-yJ^2
(12.37
1
106
19.37
(0.23)
0.05
153.02
(12.60)
158.76
(20.77
2
86
20.77
1.17
1.37
431.39
(19.60)
384.16
3
100
27
19.79
0.19
0.04
7.21
51.98
7.40
54.76
4
101
50
19.72
0.12
0.01
30.28
916.88
30.40
924.16
5
99
28
19.86
0.26
0.07
8.14
66.26
8.40
70.56
6
103
29
19.58
(0.02)
0.00
9.42
88.74
9.40
88.36
7
97
20
20.00
0.40
0.16
0.40
0.16
8
113
12
18.88
(0.72)
0.52
(6.88)
47.33
(7.60)
57.76
9
(12.95
167.70
112
6.
18.95
(0.65)
0.42
(13.60)
184.96
10
110
17
19.09
(0.51)
0.26
(2.09)
4.37
(2.60)
6.76
Total
1027
196
2.90
1,927.67
1,930.40
df
Sum of Square
Mean Square
F Value=
0.01
regression
1
2.90
2.90
Significance F-
5.32
residual
1,927.67
240.96
total
10
1,930.40

Definition Definition Relationship between two independent variables. A correlation tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

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