The following equation describes the relationship between output and labor input at a sample of work stations in a manufacturing plant ŷ = 2.35+2.20X. Suppose, for a selected workstation, the labor input is 5, the predicted output is?

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A: The formula of the correlation coefficient is:

Q: It is thought that prehistoric Indians did not take their best tools, pottery, and household items…

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Q: Find the correlation

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A: Given,      P A=0.493

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A: Data given Y:4,28,30,36,77  n=5

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Q: It is thought that prehistoric Indians did not take their best tools, pottery, and household items…

A: Since you have asked multiple question, we will solve the first question for you. If you want any…

Q: ŷ = 1.35(1.12)x  ŷ = 1.12(1.35)x  ŷ = 1.35x + 1.12  ŷ = 1.12x + 1.35

A: To determine the best equation fit for the model, we can use EXCEL, DESMOS or any other statistical…

Q: It is thought that prehistoric Indians did not take their best tools, pottery, and household items…

A: From the provided information, x y 5.75 31 6.25 26 6.75 38 7.75 74…

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Q: It is thought that prehistoric Indians did not take their best tools, pottery, and household items…

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A: From above table Coefficient Intercept=-2.018775528Coefficient X variable 1 =2.134464237

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Q: It is thought that prehistoric Indians did not take their best tools, pottery, and household items…

A:

Correlation

Correlation defines a relationship between two independent variables. It 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.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.