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rusak2 [61]
2 years ago
10

Use the Manufacturing database from "Excel Databases.xls" on Blackboard. Use Excel to develop a multiple regression model to pre

dict Cost of Materials by Number of Employees, Number of Production Workers, Value Added by Manufacture, New Capital Expenditures, and End-of-Year Inventories. Use Excel to perform a backward elimination regression analysis at a 5% level of significance. What is the test statistic of the independent variable that is dropped from the linear model in the first step. Write your answer as a number and round to 2 decimal places.

Computers and Technology
1 answer:
Dennis_Churaev [7]2 years ago
6 0

Answer:

52.64

Explanation:

Multiple Regression:

Multiple regression generally explains the relationship between multiple independent or predictor variables and one dependent or criterion variable.

Please kindly check attachment for the step by step solution of the given problem.

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Write a function named max that accepts two integer values as arguments and returns the value that is the greater of the two. Fo
RoseWind [281]

Answer:

The solution code is written in Python

  1. def max(a, b):
  2.    if(a > b):
  3.        return a  
  4.    else:
  5.        return b
  6. num1 = int(input("Please input the first number: "))
  7. num2 = int(input("Please input the second number: "))
  8. print(max(num1, num2))

Explanation:

Firstly create a function max that accepts two input, a and b, as required by question (Line 1). In the function, create if statement to check if a greater than b return a and vice versa (Line 2 -5).

In the main program (Line 7 - 10), prompt user to input two numbers (Line 7 - 8). At last call the function by passing num1 and num2 as arguments and print the result (Line 10).

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5. You just bought a new hard drive for youYou plan to use this as a secondary hard drive to store allcomputeryour UMA files. On
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Mounting the drive allows the system to have access to the drive and read and write to it. 

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Answer:

b

Explanation:

First, we need to initialize the classifier.

Then, we are required to train the classifier.

The next step is to predict the target.

And finally, we need to evaluate the classifier model.

You will find different algorithms for solving the classification problem. Some of them are like  decision tree classification  etc.

However, you need to know how these classifier works.  And its explained before:

You need  to initialize the classifier at first.

All kinds of classifiers in the scikit-learn make use of the method fit(x,y) for fitting the model or the training for the given training set in level y.

The predict(x) returns the y which is the predicted label.And this is prediction.

For evaluating the classifier model- the score(x,y) gives back the certain score for a mentioned test data x as well as the test label y.

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