In: Statistics and Probability

Possum - Regression

X: headL (mm) | Y: totalL (cm) | Y-Hat | Residual |

94.1 | 89 | ||

92.5 | 91.5 | ||

94 | 95.5 | ||

93.2 | 92 | ||

91.5 | 85.5 | ||

93.1 | 90.5 | ||

95.3 | 89.5 | ||

94.8 | 91 | ||

93.4 | 91.5 | ||

91.8 | 89.5 | ||

93.3 | 89.5 | ||

94.9 | 92 | ||

95.1 | 89.5 | ||

95.4 | 91.5 | ||

92.9 | 85.5 | ||

91.6 | 86 | ||

94.7 | 89.5 | ||

93.5 | 90 | ||

94.4 | 90.5 | ||

94.8 | 89 | ||

95.9 | 96.5 | ||

96.3 | 91 | ||

92.5 | 89 | ||

94.4 | 84 | ||

95.8 | 91.5 | ||

96 | 90 | ||

90.5 | 85 | ||

93.8 | 87 | ||

92.8 | 88 | ||

92.1 | 84 | ||

92.8 | 93 | ||

94.3 | 94 | ||

91.4 | 89 | ||

90.6 | 85.5 | ||

94.4 | 85 | ||

93.3 | 88 | ||

89.3 | 82.5 | ||

92.4 | 80.5 | ||

84.7 | 75 | ||

91 | 84.5 | ||

88.4 | 83 | ||

85.3 | 77 | ||

90 | 81 | ||

85.1 | 76 | ||

90.7 | 81 | ||

91.4 | 84 | ||

90.1 | 89 | ||

98.6 | 85 | ||

95.4 | 85 | ||

91.6 | 88 | ||

95.6 | 85 | ||

97.6 | 93.5 | ||

93.1 | 91 | ||

96.9 | 91.5 | ||

103.1 | 92.5 | ||

99.9 | 93.7 | ||

95.1 | 93 | ||

94.5 | 91 | ||

102.5 | 96 | ||

91.3 | 88 | ||

95.7 | 86 | ||

91.3 | 90.5 | ||

92 | 88.5 | ||

96.9 | 89.5 | ||

93.5 | 88.5 | ||

90.4 | 86 | ||

93.3 | 85 | ||

94.1 | 88.5 | ||

98 | 88 | ||

91.9 | 87 | ||

92.8 | 90 | ||

85.9 | 80.5 | ||

82.5 | 82 | ||

88.7 | 83 | ||

93.8 | 89 | ||

92.4 | 89 | ||

93.6 | 84 | ||

86.5 | 81 | ||

85.8 | 81 | ||

86.7 | 84 | ||

90.6 | 85.5 | ||

86 | 82 | ||

90 | 81.5 | ||

88.4 | 80.5 | ||

89.5 | 92 | ||

88.2 | 86.5 | ||

98.5 | 93 | ||

89.6 | 87.5 | ||

97.7 | 84.5 | ||

92.6 | 85 | ||

97.8 | 89 | ||

90.7 | 85 | ||

89.2 | 82 | ||

91.8 | 84 | ||

91.6 | 88.5 | ||

94.8 | 83 | ||

91 | 86 | ||

93.2 | 84 | ||

93.3 | 86.5 | ||

89.5 | 81.5 | ||

88.6 | 82.5 | ||

92.4 | 89 | ||

91.5 | 82.5 | ||

93.6 | 89 |

Possums:
Head-Length (mm)
Body-Length (cm)
Y-hat
Residuals
94.1
89
92.5
91.5
94
95.5
93.2
92
91.5
85.5
93.1
90.5
95.3
89.5
94.8
91
93.4
91.5
91.8
89.5
93.3
89.5
94.9
92
95.1
89.5
95.4
91.5
92.9
85.5
91.6
86
94.7
89.5
93.5
90
94.4
90.5
94.8
89
95.9
96.5
96.3
91
92.5
89
94.4
84
95.8
91.5
96
90
90.5
85
93.8
87
92.8
88
92.1
84
92.8
93
94.3
94
91.4
89
90.6
85.5
94.4
85
93.3
88
89.3...

Consider the following data for two variables, X and Y
X
6
29
21
15
24
Y
10
30
22
14
25
a. Develop an estimated regression equation for the data of the form y-hat = bo + b1 x. Comment on the adequacy of this equation for predicting y . Enter negative value as negative number.
The regression equation is
Y = [ ] + [ ] X (to 2 decimals)
S = [ ] (to 3 decimals)...

Consider the data in the table below.YX5810555491969105952798Answer the following questions to two decimal
places.1. The slope coefficient for a regression of Y on X is2. The constant of a regression of Y on X is3. The residual for the first observation in the table is4. The correlation of the residuals and X is

Describe the each scatterplot's usefulness in terms.
(x and y has simple linear regression relationship.)
(Y~X)
1. scatter plot of x and y
2. scatter plot of x and residuals
3. scatter plot of order of the x and residuals
4. scatter plot of yhat and residuals

Identify and interpret the smallest positive residual. Provide
the complete list of residuals.
X
Y
1870
3.38
1330
1.16
1760
1.58
1520
2.65
1300
1.98
1520
2.39
1640
2.49
1490
2.81
1300
2.95
1360
1.69
1940
3.49
1730
2.8
1790
2.95
1780
3.8
1730
2.64
1380
2.36
1580
3.1
1900
1.96
1640
3.08
1540
2.24
1350
2.59
1380
2.43
1780
1.95
1700
2.07
1610
2.34
1720
3.59
2070
3.59
1210
2.12
1720
2.48
1510
2.37
1790
2.1
2100
2.55
1690...

Identify and interpret the smallest positive residual. Provide
the complete list of residuals.
X
Y
1870
3.38
1330
1.16
1760
1.58
1520
2.65
1300
1.98
1520
2.39
1640
2.49
1490
2.81
1300
2.95
1360
1.69
1940
3.49
1730
2.8
1790
2.95
1780
3.8
1730
2.64
1380
2.36
1580
3.1
1900
1.96
1640
3.08
1540
2.24
1350
2.59
1380
2.43
1780
1.95
1700
2.07
1610
2.34
1720
3.59
2070
3.59
1210
2.12
1720
2.48
1510
2.37
1790
2.1
2100
2.55
1690...

In a multiple regression Y = β0+β1X+β2D, where Y is the annual
income (in dollars), X is number of years of education, and D is
gender (1 for male, and 0 for female). Below is a part of the
regression output:
Coefficient p-value
Intercept 24563 0.0054
X 1565 0.0003
D 3215 0.0001
1. Interpret the coefficient of D.
2. Is there a significant difference in the annual incomes
earned by male and female?

The function y(x, t) = (20.0 cm)
cos(πx - 17πt), with x
in meters and t in seconds, describes a wave on a taut
string. What is the transverse speed for a point on the string at
an instant when that point has the displacement y = +17.0
cm?
Number
Enter your answer in accordance to the question statement
Units
Choose the answer from the menu in accordance to the question
statement
This answer has no units° (degrees)mkgsm/sm/s^2NJWN/mkg·m/s or
N·sN/m^2...

Python:Create a class defined for Regression. Class attributes are data
points for x, y, the slope and the intercept for the regression
line. Define an instance method to find the regression line
parameters (slope and intercept). Plot all data points on the
graph. Plot the regression line on the same plot.

The main objective of a regression analysis is to make
predictions of Y using X?
True
False

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