Final Project: Regression and Correlation Analysis Use the dependent variable (labelled Y) and one of the independent variables (labelled X1, X2, and X3) in the data file. Select and use one independent variable throughout this analysis. Use Excel to perform the regression and correlation analysis to answer the following. The week 6 spreadsheet can be helpful in this work. Final Project- Regression and Correlation Analysis Final Project- Regression and Correlation Analysis Generate a scatterplot for the specified dependent variable (Y) and the selected independent variable (X), including the graph of the “best fit” line. Interpret. Determine the equation of the “best fit” line, which describes the relationship between the dependent variable and the selected independent variable. Determine the correlation coefficient. Interpret. Determine the coefficient of determination. Interpret. Test the utility of this regression model by completing a hypothesis test of β=0 using α=0.10. Interpre

Final Project- Regression and Correlation Analysis

Generate a scatterplot for the specified dependent variable (Y) and the selected independent variable (X), including the graph of the “best fit” line. Interpret.
Determine the equation of the “best fit” line, which describes the relationship between the dependent variable and the selected independent variable.
Determine the correlation coefficient. Interpret.
Determine the coefficient of determination. Interpret.
Test the utility of this regression model by completing a hypothesis test of β=0 using α=0.10. Interpret results, including the p-value.
Based on the findings in steps 1-5, analyze the ability of the independent variable to predict the dependent variable.
Compute the confidence interval for β, using a 95% confidence level. Interpret this interval.
Compute the 99% confidence interval for the dependent variable for a selected value of the independent variable. Each student can choose a value to use for the independent variable (use the same value in the next step). Interpret this interval.
Using the same chosen value for part (8), estimate the 99% prediction interval for the dependent variable. Interpret this interval.
What can be said about the value of the dependent variable for values of the independent variable that are outside the range of the sample values? Explain.
Describe a business decision that could be made based on the results of this analysis. In other words, how might the business operations change based on these statistical results?
Final Project report is due by the end of Week 7.
The final Project is worth 130 total points.
Summarize your results from Steps 1-11 in a 3-page report. The report should explain and interpret the results in ways that are understandable to someone who does not know statistics.

Submission: The Word document summary report should be submitted for questions 1-11. The Excel output can be included as an appendix if needed.

A. Format for report:
B. Summary Report
Steps 1-11 are addressed with appropriate output, graphs, and interpretations. Be sure to number each step 1-11
NB: This order is directly related to orders # #51011 and # 50562, respectively

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Final Project: Regression and Correlation Analysis Use the dependent variable (labelled Y) and one of the independent variables (labelled X1, X2, and X3) in the data file. Select and use one independent variable throughout this analysis. Use Excel to perform the regression and correlation analysis to answer the following. The week 6 spreadsheet can be helpful in this work. Final Project- Regression and Correlation Analysis Final Project- Regression and Correlation Analysis Generate a scatterplot for the specified dependent variable (Y) and the selected independent variable (X), including the graph of the “best fit” line. Interpret. Determine the equation of the “best fit” line, which describes the relationship between the dependent variable and the selected independent variable. Determine the correlation coefficient. Interpret. Determine the coefficient of determination. Interpret. Test the utility of this regression model by completing a hypothesis test of β=0 using α=0.10. Interpre

Final Project- Regression and Correlation Analysis

Step 1

According to the scatter plot analysis, there appears to be a positive linear relationship between the number of calls made and the number of sales generated. The data points are presented in such a way that it appears that more calls are related to more sales. Furthermore, the trendline or the line of best fit shows that as the number of phone calls grows, so does the sales performance. This data might be useful for companies wanting to improve their sales strategy and overall success. Hire our assignment writing services if your assignment is devastating you.

Step 2

The best-fit line equation, obtained using the Regression option in Excel’s Data Analysis menu, is as follows:

Sales = Intercept + Coefficient of Calls * Calls.

Sales = 20.5693991 + 0.16096949 * Calls.

However, from the scatter plot, the equation of best-fit line is determined as.”

y = 1.9689x + 66.734

  Coefficients Standard Error t Stat P-value
Intercept 20.5693991 3.77319883 5.45144851 3.7342E-07
Calls (X1) 0.16096949 0.02387115 6.7432642 1.0823E-09

Step 3

The correlation coefficient, which is a mathematical measure of the relationship between two variables, reveals a slightly positive relationship between the number of calls made and the number of sales generated. A correlation coefficient of 0.318 was calculated using the function Correl(X1 array, Y array). This value shows that the two variables are moderately connected, with one variable tending to rise when the other rises. In other words, as the number of calls the sales staff makes increases, so does the likelihood of higher sales. Although the correlation is weak, it indicates a meaningful association between calls and sales.

Step 4

In a regression model, the coefficient of determination, or R-squared value, is a statistical measure that quantifies the proportion of total variability in one variable that the other variable can explain. In this case, the R-squared value is 0.317, suggesting that the number of calls made accounts for nearly 31.7% of the variability in sales. This shows that other factors, such as marketing methods, pricing, and product quality, may also have an impact on sales. Nonetheless, the positive relationship between calls and sales, as demonstrated by the correlation coefficient, shows that increasing the number of calls may result in a moderate rise in sales.

Regression Statistics
Multiple R 0.56297251
R Square 0.31693804
Adjusted R Square 0.30996802
Standard Error 4.39352388
Observations 100

Step 5

To assess the model’s reliability, we used an F-test to compare the observed variation in sales to the variation predicted if the beta coefficient of calls was zero. The null hypothesis states that no significant linear relationship exists between calls and sales. The alternative hypothesis was that the two variables had a significant linear relationship. We calculated a p-value of 0.0012 using a significance level of 0.05. We rejected the null hypothesis because the p-value was less than the significance level. We concluded that the beta coefficient of calls was not zero, implying a linear relationship between calls and sales. As a result, the regression model proved to be effective in predicting sales based on the number of calls made.

Step 6

The analysis results suggest that the number of calls made is a significant predictor of sales volume. The positive linear relationship discovered between the two variables implies that as the number of calls made increases, so does the sales volume. As a result, the number of calls is a crucial component to consider when trying to predict sales volume. The company can potentially increase its sales performance and fulfill its business goals by focusing on increasing the number of calls made.

Step 7

Based on the data evaluated, the 95% confidence

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