Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4.100 450 5,100 550 5,400 600 6,000 700 6.500 750 7.000 This data was used to develop an estimated regression equation, - 1,405.33 + 7.44x, relating production volume and cost for a particular manufacturing operation. Use a - 0.05 to test whether the production volume is significantly related to the total cost. (Use the F test.) State the null and alternative hypotheses. O Hại 8, 20 O Hại e -0 O Mei -0 O Hei 0 Set up the ANOVA table. (Round your p-value to three decimal places and all other values to two decimal places.) Source of Variation Sum of Squares Degrees of Freedom Mean Square F p-value Regression Error Total

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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.