1. Implement and plot a simple linear regression for the above data, where the temperature is “x", and the length is “y" 2. Implement and plot a multiple linear regression "Polynomial regression" with different degrees. For example, Degree of 3:

1. Implement and plot a simple linear regression for the above data, where the temperature is “x", and the length is “y" 2. Implement and plot a multiple linear regression "Polynomial regression" with different degrees. For example, Degree of 3:

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter24: Forecasting Models

Section24.4: Winter’s Method: Exponential Smoothing With Seasonality

Problem 16P

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Concept explainers

Decision Tree

A decision tree is a unique kind of probability tree. A popular and powerful tool used for prediction and classification. The decision tree structure is the same as a flowchart with a tree structure. The internal node refers to the attribute test, the branch refers to the outcome of the test, and the leaf node(terminal) contains the class label.

Question

We all know that when the temperature of a metal increases, it begins to expand. So,
we experimented with exposing a metal rod to different temperatures and recorded its
length as follows:
20
Length
Temp
25
30
35
40
45
50
55
60
65
0.5
1.8
5
6.2
6.5
7.8
9.4
9.8
10.9
Required:
1. Implement and plot a simple linear regression for the above data, where the
temperature is “x", and the length is "y"
2. Implement and plot a multiple linear regression "Polynomial regression" with
different degrees.
For example, Degree of 3:
Y = w,x' + w2x? + W3x³ + W4
Where w4 represents bias.
*you can use normal equation to calculate 'W' as follow:
W (X".X)².(X".Y)
Then calculate Y, Where Y = X.WT
3. Try degree of 2, 3, 5, and 8

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Suppose we use the following KB (where x, y, z are variables and r1, r2, r3, goal are constants) to determine whether a particular robot can score. (a) Open(x) ∧ HasBall(x) → CanScore(x)(b) Open(x) ∧ CanAssist(y, x) ∧ HasBall(y) → CanScore(x) (c) PathClear(x,y) → CanAsist(x,y)(d) PathClear(x,z) ∧ CanAssist(z,y) → CanAssist(x,y) (e) PathClear(x,goal) → Open(x)(f) PathClear(y,x) → PathClear(x,y) (g) HasBall(r3)(h) PathClear(r1,goal) (i) PathClear(r2,r1) (j) PathClear(r3,r2) (k) PathClear(r3,goal) Intuitively, CanScore(x) means x can score on goal. CanAssist(x, y) means there exists some series of passes that can get the ball from x to y. Open(x) means x can shoot on goal directly. And P athClear(x, y) means the path between x and y is clear. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r1. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r3. How many “distinct” derivations (i.e., involving different…
Suppose we use the following KB (where x,y,z are variables and r1, r2, r3, goal are constants) to determine whether a particular robot can score a) Open(x) ∧ HasBall(x) -> CanScore(x) b) Open(x) ∧ CanAssist(y,x) ∧ HasBall(y) -> CanScore(x) c) PathClear(x,y) -> CanAssist(x,y) d) PathClear(x,z) ∧ CanAssist(z,y) -> CanAssist(x,y) e) PathClear(x,goal) -> Open(x) f) PathClear(y,x) -> PathClear(x,y) g) HasBall(r3) h) PathClear(r1, goal) i) PathClear(r2, r1) j) PathClear(r3, r2) k) PathClear(r3, goal) Intuitively, CanScore(x) means x can score on goal. CanAssist(x,y) means there exists some series of passes that can get the ball from x to y. Open(x) means x can shoot on goal directly. And PathClear(x,y) means the path between x and y is clear. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r1. Provide a SLD-derivation for the query CanScore(x) in which the answer provided is r3. How many "distinct" derivations (i.e., involving different…