Question

1.)Use the given data to find the best predicted value of the response variable. The regression...

1.)Use the given data to find the best predicted value of the response variable.

The regression equation is . What is the best predicted value of y for ?

2.)Use the given data to find the best predicted value of the response variable.

The regression equation is  What is the best predicted value of y for ?

Group of answer choices

4.22

27

93.5

18.3

3.) Use the given data to find the best predicted value of the response variable.

The regression equation is What is the best predicted value of y for ?

Group of answer choices

57.80

83.42

555.21

71.13

4.) Use the given data to find the best predicted value of the response variable.

The regression equation relating dexterity scores (x) and productivity scores (y) for the employees of a company is  What is the best predicted productivity score for a person whose dexterity score is ?

5.)Use the given data to find the best predicted value of the response variable.

The regression equation is  What is the best predicted value of y for ?

Group of answer choices

22.0

18.5

19.0

18.0

Group of answer choices

56.30

205.41

76.17

58.20

6.) Use the given data to find the best predicted value of the response variable.

The regression equation is  What is the best predicted value of y for ?

Group of answer choices

5.0

8.0

7.0

17.0

7.)Use the given data to find the best predicted value of the response variable.

The regression equation is  What is the best predicted value of y for ?

Group of answer choices

64.04

57.82

79.62

64.71

Homework Answers

Answer #1

You did not upload the data. but I understand your question.

So, I will give you idea how to do this question

Let y be your response variable and x be your predictor variable. Use this command in R to fit the model

model = lm(y ~ x)

The above command will fit the model.

let the fitted regression equation is y_hat = b0 + b1*x

so b0 and b1 you can find from the below commands

b0 = model$coefficients[1]
b1 = model$coefficients[2]

Now we want to find the best-predicted value for a given value of X = x_given

It can be found by the following command

predict(m2, data.frame(x = x_given))

all the above information is sufficient for your question

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