XYZ Training wants to know if they can use data from the past 12 months of numbe of monthly clients (x) and profit for each corresponding month (y) to perform a regression analysis. Examine their Excel regression printout. Identify the y intercept of the regression line. Round answers to 3 decimal places. SUMMARY OUTPUT Regression Sta tistics Multiple R 0.891759168 R Square 0.795234414 Adjusted R Square 0.774757855 Standard Error 1841.207101 Observations 12 ANOVA df SS MS F Regression 1 131656855.8 1.32E+08 38.836331 Residual 10 33900435.9 3390044 Total 11 165557291.7 Coefficients Standard Error t Stat P-value 1489.764874 2.219215 Intercept XVariable 1 3306.109257 0.050762 186.8522243 29.9832823 6.23188 9.732E-05 -3306.109 3306.109 186.852 -186.852
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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