Interpret data visualization consisting of : Scatter charts (X versus Y) – Analyze two different and distinct relationships: C24 (X, training hours per year per call center employee) versus C25 (Y, average monthly sales revenue generated per call center employee) Please indicate Equation, Slope, Intercept interpretation, R ^2 value, 95% confidence, and P-value using Excel regression and scatter graph data.
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.
Interpret data visualization consisting of :
Scatter charts (X versus Y) – Analyze two different and distinct relationships: C24 (X, training hours per year per call center employee) versus C25 (Y, average monthly sales revenue generated per call center employee)
Please indicate Equation, Slope, Intercept interpretation, R ^2 value, 95% confidence, and P-value using Excel regression and scatter graph data.
Is P-value significant? Do I reject or accept the hypothesis?
| C24 | C25 |
| 0 | 4779 |
| 0 | 4706 |
| 0 | 4350 |
| 20 | 5189 |
| 20 | 5140 |
| 20 | 4976 |
| 30 | 5110 |
| 30 | 5685 |
| 30 | 5618 |
| 40 | 5995 |
| 40 | 5628 |
| 40 | 5897 |
| 50 | 5746 |
| 50 | 5719 |
| 50 | 5782 |
| 60 | 4895 |
| 60 | 5030 |
| 60 | 4648 |
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.427487869 | |||||||
| R Square | 0.182745879 | |||||||
| Adjusted R Square | 0.131667496 | |||||||
| Standard Error | 460.7787188 | |||||||
| Observations | 18 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 1 | 759618.0571 | 759618.0571 | 3.577753821 | 0.076803 | |||
| Residual | 16 | 3397072.443 | 212317.0277 | |||||
| Total | 17 | 4156690.5 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 4924.595238 | 213.2990997 | 23.08774507 | 1.03491E-13 | 4472.421 | 5376.76913 | 4472.421346 | 5376.76913 |
| C24 | 10.41714286 | 5.507359073 | 1.891495129 | 0.076802657 | -1.25794 | 22.0922225 | -1.257936825 | 22.09222254 |
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