Interpret the following three sets of data using scatter chart and regression analysis, using slope , intercept, Coefficient of Determiniation (R2) , regression data (using excel), 95% confidence interval, Regression and P value ,Do I reject the null hypothesis. Generate and explain the derivation of insights using regression analysis and its associated visualization. Differentiate between the signals identified by business analytics and the noise that is inherent in the system. 1.C20 (annual number of training hours) versus Y: C21 (time to serve customers in minutes) C20 100 100 100 100 100 125 125 125 125 150 150 150 150 150 175 175 175 175 C21 21.8 21.9 21.7 21.6 21.7 21.7 21.4 21.5 21.4 21.9 21.8 21.8 21.6 21.5 21.9 21.7 21.8 21.4 check_circle Expert Answer thumb_up thumb_down Step 1 X:annual number of training hour and Y:time to serve customers in minutes Steps to construct scatter plot in Excel: Enter the data for X and Y in Excel sheet Select the columns of X and Y. Go to Insert menu. Click on “Insert scatter or bubble chart” option. Select Scatter under Charts. Excel Output: Step 2 Excel Procedure: Enter X and Y data in Excel Go to Data Click on Data Analysis……..> ‘Regression’. Select Y under ‘Input Y Range’. Select X under ‘Input X Range’. Click on ‘OK’. Output: From the output, slope:0.0002, represents there is 0.0002 units increase in Y as 1 unit increase in X. Intercept: 21.6426, represent constant increase in Y. Coefficient of determination(R2): 0.0013 or 0.13%, represents 0.13% of the variation in Y is explained by the variable X. 95% confidence interval: (-0.0029, 0.0034), represents there is 95% chance that the coefficient of X variable is lies between -0.0029 and 0.0.0034.
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 the following three sets of data using scatter chart and
Generate and explain the derivation of insights using regression analysis and its associated visualization.
Differentiate between the signals identified by business analytics and the noise that is inherent in the system.
1.C20 (annual number of training hours) versus Y: C21 (time to serve customers in minutes)
| C20 |
| 100 |
| 100 |
| 100 |
| 100 |
| 100 |
| 125 |
| 125 |
| 125 |
| 125 |
| 150 |
| 150 |
| 150 |
| 150 |
| 150 |
| 175 |
| 175 |
| 175 |
| 175 |
| C21 |
| 21.8 |
| 21.9 |
| 21.7 |
| 21.6 |
| 21.7 |
| 21.7 |
| 21.4 |
| 21.5 |
| 21.4 |
| 21.9 |
| 21.8 |
| 21.8 |
| 21.6 |
| 21.5 |
| 21.9 |
| 21.7 |
| 21.8 |
| 21.4 |
Expert Answer
X:annual number of training hour and Y:time to serve customers in minutes
Steps to construct
- Enter the data for X and Y in Excel sheet
- Select the columns of X and Y.
- Go to Insert menu.
- Click on “Insert scatter or bubble chart” option.
- Select Scatter under Charts.
Excel Output:
Excel Procedure:
- Enter X and Y data in Excel
- Go to Data
- Click on Data Analysis……..> ‘Regression’.
- Select Y under ‘Input Y
Range ’. - Select X under ‘Input X Range’.
- Click on ‘OK’.
Output:
From the output,
- slope:0.0002, represents there is 0.0002 units increase in Y as 1 unit increase in X.
- Intercept: 21.6426, represent constant increase in Y.
- Coefficient of determination(R2): 0.0013 or 0.13%, represents 0.13% of the variation in Y is explained by the variable X.
- 95% confidence interval: (-0.0029, 0.0034), represents there is 95% chance that the coefficient of X variable is lies between -0.0029 and 0.0.0034.
Step by step
Solved in 2 steps with 2 images
