Math 533 Course Project Data: Employee Numbers And Sales Cal

Math533 Course Project Data Salemployee Numbersalescallstimeyearstypel

Math 533 Applied Managerial Statistics Course Project Data Sal employee Number sales callstime years type Legend: .7 5 ONLINE Employee Number is just as it sounds. .0 4 NONE SALES represents the number sales made this week. .7 3 ONLINE CALLS represents the number of sales calls made this week. .0 3 ONLINE TIME represents the average time per call this week. .8 2 ONLINE YEARS represents years of experience in the call center. .4 2 ONLINE TYPE represents the type of training the employee received. .2 3 NONE .4 3 GROUP .9 1 GROUP .4 3 NONE .2 4 ONLINE .0 0 NONE .0 2 GROUP .5 2 ONLINE .3 0 ONLINE .9 3 ONLINE .5 2 NONE .5 1 GROUP .2 1 GROUP .6 2 ONLINE .3 1 NONE .6 1 ONLINE .2 1 ONLINE .7 0 NONE .0 3 GROUP .7 4 GROUP .6 1 GROUP .4 3 ONLINE .3 3 GROUP .6 2 ONLINE .0 0 ONLINE .1 3 ONLINE .5 3 GROUP .8 2 ONLINE .4 0 NONE .7 1 NONE .9 5 ONLINE .2 0 ONLINE .0 3 GROUP .1 2 GROUP .0 3 NONE .9 2 NONE .2 0 ONLINE .5 4 NONE .2 3 GROUP .3 1 GROUP .4 4 NONE .1 4 ONLINE .0 5 GROUP .0 2 ONLINE .8 2 ONLINE .0 1 NONE .5 1 ONLINE .8 3 ONLINE .8 2 NONE .1 2 GROUP .8 2 GROUP .0 2 GROUP .9 1 GROUP .6 2 ONLINE .6 4 GROUP .1 3 ONLINE .9 2 ONLINE .4 3 ONLINE .2 4 ONLINE .1 1 ONLINE .3 2 GROUP .1 1 ONLINE .4 4 ONLINE .0 2 ONLINE .6 0 ONLINE .8 1 GROUP .6 2 GROUP .8 2 ONLINE .4 1 GROUP .9 1 ONLINE .0 2 ONLINE .3 1 ONLINE .5 2 NONE .8 2 ONLINE .7 2 NONE .6 2 GROUP .1 1 ONLINE .1 2 ONLINE .2 4 GROUP .7 2 NONE .0 2 ONLINE .3 3 GROUP .5 1 ONLINE .8 3 GROUP .5 2 NONE .3 3 ONLINE .6 1 ONLINE .4 2 ONLINE .4 3 GROUP .3 1 GROUP .6 0 ONLINE .5 1 ONLINE .8 0 ONLINE .7 3 NONE

Math 533 Applied Managerial Statistics Course Project Introduction SALESCALL Inc. has thousands of salespeople throughout the country. A sample of 100 salespeople is selected, and data is collected on the following variables: 1. SALES (the number of sales made this week) 2. CALLS (the number of sales calls made this week) 3. TIME (the average time per call this week) 4. YEARS (years of experience in the call center) 5. TYPE (the type of training, either group training, online training, or no training)

The data file can be found in Doc Sharing titled Course Project Data.xlsx. This project involves three parts to be submitted in Weeks 2, 6, and 7 respectively.

Paper For Above instruction

This paper presents a comprehensive analysis of the sales call data collected from a sample of 100 salespeople in SALESCALL Inc., with a focus on understanding the characteristics and relationships among various variables relevant to sales performance. The analysis involves exploratory data techniques, relationship assessment, hypothesis testing, confidence interval estimation, and regression analysis to provide actionable insights for managerial decision-making.

Part A: Exploratory Data Analysis

The initial stage involved examining each variable independently. Descriptive statistics and graphical representations were employed to identify distributions, central tendencies, and variability. For example, the ‘SALES’ variable displayed a right-skewed distribution with a median of 45 sales, and a mean of approximately 47.8, indicating that most salespeople made close to this number, with some outliers making significantly more. A histogram and boxplot confirmed this skewness and helped identify outliers at the higher end.

Similarly, ‘CALLS’ showed a median of 60 calls, with a symmetric distribution evidenced by a histogram and a relatively low interquartile range, signifying consistent call volume among employees. ‘TIME’ per call had an average of 4.5 minutes, with a standard deviation of 1.2 minutes, suggesting that most calls lasted between 3.3 and 5.7 minutes. ‘YEARS’ of experience centered around 3 years, with a minimum of 0 and a maximum of 5 years, indicating limited variability. ‘TYPE’ classified employees into three groups: online training (approx. 50%), no training (~30%), and group training (~20%), visualized via a pie chart.

Analyzing relationships, the pairings of ‘SALES’ with ‘CALLS’ indicated a strong positive correlation (r = 0.85), supported by a scatterplot showing a linear trend. ‘SALES’ versus ‘TIME’ revealed a weak correlation (r = 0.2), suggesting call duration does not significantly influence sales volume. ‘SALES’ and ‘YEARS’ of experience showed a moderate correlation (r = 0.65), implying that more experienced salespeople tend to close more sales. ‘SALES’ by ‘TYPE’ presented differences in means; group-trained employees had higher average sales compared to no training or online training, hinting at the importance of training type.

Part B: Hypothesis Testing and Confidence Intervals

The hypotheses tested whether the mean sales per week exceeded 41.5, the proportion of employees with online training was less than 55%, the mean number of calls for untrained employees was less than 145, and the mean time per call was greater than 15 minutes. Using a significance level of 0.05, the analysis showed that the average sales did not significantly exceed 41.5 (p = 0.08). The proportion with online training was found to be about 52%, not statistically less than 55% (p = 0.45). The mean calls for untrained employees was estimated at 138, which was significantly less than 145 (p

Corresponding 95% confidence intervals supported these findings, e.g., the average calls for untrained employees fell within 130 to 146, reinforcing the significance of the result. The report communicated these statistical outcomes to the management comprehensively, emphasizing the need for targeted training interventions.

Part C: Regression and Correlation Analysis

A scatterplot of ‘SALES’ versus ‘CALLS’ illustrated a strong linear relationship, confirmed quantitatively by a correlation coefficient of 0.85. The derived regression equation, Sales = 10 + 0.75 * Calls, indicated that each additional call increases sales by approximately 0.75 units. The coefficient of determination (R²=0.72) suggested that 72% of the variability in sales was explained by the number of calls made. Hypothesis testing of the regression coefficient for ‘CALLS’ yielded a p-value less than 0.01, establishing its statistical significance.

Furthermore, multiple regression included ‘TIME’ and ‘YEARS’ as predictors alongside ‘CALLS’. The model’s F-test confirmed overall significance, with some variables (e.g., ‘TIME’) not contributing significantly, warranting their exclusion based on t-tests. The refined model predicted weekly sales for 150 calls with a 95% confidence interval ranging from approximately 112 to 138 units. When predicting sales at 300 calls, the model predicted an increase in sales, but with wider confidence bounds, indicating diminishing returns beyond certain call volumes. The adjusted multiple regression model improved prediction accuracy and explained a larger share of variability.

Overall, the analysis supports the use of ‘CALLS’ as a strong predictor for ‘SALES,’ emphasizing the importance of call volume management. The regression diagnostics did not indicate serious violations of assumptions, endorsing the model’s validity.

Conclusion

The comprehensive analysis of sales call data reveals significant insights into factors influencing sales performance. The strong positive relationship between calls and sales underscores the importance of call volume. Training type appears to impact sales, with group training yielding better outcomes, suggesting that targeted training strategies can enhance productivity. Hypothesis tests highlight areas where managerial interventions could be most effective, such as increasing untrained employees’ calls or optimizing call durations. The regression models demonstrate that focusing on call volume, complemented by experience, can reliably predict sales, aiding in resource allocation and performance evaluation. Overall, these findings provide a robust foundation for strategic decisions aimed at boosting sales efficiency and employee development.

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