Given the regression output below, what is the linear equation? call: Im(formula - sales - spend, data - dataset) Residuals: Min 1Q Median 3Q 258 1726 мах -3385 -2097 з034 coefficients: Estimate std. Error t value Pr(>[t|) (Intercept) 1383.4714 1255. 2404 1.102 0.296 0.1625 65. 378 1.71e-14 *** Spend 10. 6222 signif. codes: 0 ***** 0.001 ***' 0.01 **' 0.05 '.' 0.1 ·'1 Residual standard error: 2313 on 10 degrees of freedom Multiple R-squared: 0.9977, Adjusted R-squared: 0.9974 F-statistic: 4274 on 1 and 10 DF, p-value: 1.707e-14 Sales = 1,383 + (10.6 * Spend) Sales = 1,255 + (0.16 * Spend) Spend = 1,255 + (0.16 * Sales) O Spend = 1,383 + (10.6 * Sales)
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.

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