21. The linear regression equation is: a. y-hat = 79.96 + 0.0094x b. y-hat = 0.0094 – 79.96x c. y-hat = 0.0094 – 79.96x d. y-hat = 79.96 – 0.0094x 22. Predict how many bathrooms a new site with 1150 employees should have. a. 91 b. 70 c. 121 d. The linear regression equation should not be used to make predictions.
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.
Use the following table and information to answer questions 21 and 22: A company wants to build a
new site with the number of bathrooms commensurate with the number of employees who will work
there. The analytics team decides to analyze a linear model of these data from extant sites to inform this
decision before construction.
| Number of employees | 650 | 730 | 810 | 900 | 102 | 107 | 1150 |
| Number of bathrooms | 40 | 50 | 54 | 61 | 82 | 110 | 121 |
21. The linear regression equation is:
a. y-hat = 79.96 + 0.0094x
b. y-hat = 0.0094 – 79.96x
c. y-hat = 0.0094 – 79.96x
d. y-hat = 79.96 – 0.0094x
22. Predict how many bathrooms a new site with 1150 employees should have.
a. 91
b. 70
c. 121
d. The linear regression equation should not be used to make predictions.
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