Course Project Part A Math534 Raneen
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DeVry University, Chicago *
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534
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Feb 20, 2024
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Course Project Part A: Exploratory Data Analysis
Math 534
Raneen Qadoori
9/7/2023
A. Brief Introduction We have five variables which are SALES, CALLS, TIME, YEARS, and TYPE. The main purpose is to process, organize, present and summarize the given data.
1.
SALES – represent the number of sales made this week 2.
CALLS – represent the number of sales calls made this week
3.
TIME – represents the average time per call this week 4.
YEARS – represents years of experience in the call center
5.
TYPE – represents the type of training the employees received
In this course project I will be analyzing and interpreting three individual variables which will include a graph and a numerical summary. I will also be analyzing, explaining, and interpreting three of the pairings, one of which will be a qualitative variable and another that is quantitative. Quantitative data are used when a researcher is trying to quantify a problem or address the “what” or “how many” aspects of a research question. It is data that can either be counted or compared on numeric scale [ CITATION Lib19 \l 1033 ], while Qualitative data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form [ CITATION Lib19 \l 1033 ]. B. Discuss the first individual variable, using graphical, numerical summary and interpretation. Numerical Summary of Type The category TYPE of the qualitative variable is represented by sections of the pie. As you can see in the graph each section of the pie chart is proportional to the class relative frequency. The numerical summary and the pie chart signify the same result for one variable and it is the Type. 30% of employees trained in a group, the other 20% of employees didn’t train in a group, and the
last 50% of the employees trained online out of 100 employees. C. Discuss the second individual variable, using graphical, numerical summary and interpretation. Numerical Summary of Calls CALLS Mean 96.99
Standard Error 1.452305377
Median
97
Mode
94
Standard Dev
14.52305377
Sample Variance
210.9190909
Kurtosis
1.641244432
Skewness
0.12638834
Range 90
Minimun
55
Maximum
145
Sum
9699 Count 100
For this sample set of 100 weeks, the lowest number of sales calls per weeks was about 55 and the most sales calls made was about 145 per week. Therefore, the range is 90 sales calls. The call center typically made 97 calls per week on average. Sales calls between 73 and 109 were frequent. The total number of calls made within the sample set of 100 weeks is a total of 9699 sales calls. The number of sales calls that occur most often each week or the mode is 94. This variable has a slightly right-skewed histogram where in the mode is closer to the left of the graph and smaller than the mean or median D. Discuss the third individual variable, using graphical, numerical summary and interpretation
Numerical Summary of Years
CALLS Mean 4.27
Standard Error 0.100357945
Median
4
Mode
4
Standard Dev
1.003579452
Sample Variance
1.007171717
Kurtosis
1.722947863
Skewness
-0.07885948
Range 6
Minimun
1
Maximum
7
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Sum
427
Count 100
For this sample set, it represents the years of the employee work experience in the call center within 100 weeks. The lowest number of the employees’ call center work experience is 1 year. The most work experience in the call center is 7 years. Typical average work experience in the call center is approximately 4 years. The most frequent work experience in the call center is between 3 to 6 years. The histogram is symmetrical as the mean and median is close to one another. However, is is slightly left- skewed. E. Discuss the first pairing of variables, using graphical, numerical summary and interpretation.
Box and Whisker Plot A box and whisker plot and the table above are used to analyze the Sales and Type data set. This displays the five-number summary of a set of data; minimum, first quartile, median, third quartile, and maximum. The average sales of employees with training, (GROUP at 43 and ONLINE at 44) is higher than employees without training (NONE at 37). However, the lowest number of sales and the greatest number of sales were made from employees with group training and no training at all. The employees that trained in a group, the interquartile range is larger than
both employees who trained online and didn’t train at all. The standard deviation of employees who trained online is slightly lower than the employees who trained with a group or didn’t train at all. I think it’s fair to say that employees who had training didn’t make a significant difference
in sales with the employees who didn’t train at all. F. Discuss the second pairing of variables, using graphical, numerical summary and interpretation.
Box and Whisker Plot A box and whisker plot and the table above are used to analyze the CALLS and TYPES data set. The number of sales calls made by employees who had no training and trained in a group had a higher number of maximum and minimum calls versus employees that trained online. The standard deviation between the three types of how the employees were trained measures how spread out the numbers are. When analyzing the box plot and table above, it indicates that the type of training received for employees didn’t make a huge impact on the number of sales calls made. G. Discuss the third pairing of variables, using graphical, numerical summary and interpretation.
Sales and Calls – Two quantitative variables
By comparing the two quantitative variables, I may be able to see if one variable is caused or dependent on the other. The above scatter plot shows the relationship between the number of sales calls made and the number of sales made within those sales calls. By adding a linear trend line on this scatter plot the correlation of the two sets of data is a highly positive correlation as the values increase. The equation for a straight line is y = mx + c. m is the slope of the line and c is the y-intercept. The slope m =0.4437. To get the value of C, substitute the known value such
as (x, y). The final equation and regression analysis of this data calculates that the equation of the
best fit line is Y 0.4437x – 0.4875. The “R” determines how close the data are to the fitted regression line. The variance accounts for 80.76%, indicating that the data points will fall to the fitted regression line. H. Conclusion In this analysis, I conducted and focused on three individual variables which are TYPE, CALLS and YEARS by using a numerical summary, pie chart and a histogram. Along with three pairing variables with a set of data that included qualitative and not a qualitative variable. The TYPE, which represents the type of training the employees received, was broken down on a pie chart to indicate the percentage of each type. The CALLS represent the number of sales calls made each week, summarizing its data, concluding that between 91 to 109 had high frequencies of sales calls made. The YEARS represent the years of employee experience in the call center. To summarize the YEARS variable, the average employee call center experience ranged from 3.4 to 4.6 years. The pairing of SALES and TYPE and CALLS and TYPE utilized the use of a table and a box and whisker plot for analysis. The other pairing of SALES and CALLS, which are two quantitative variables utilized a scatter plot for analysis. Within the pairing I conclude that the number of sales didn’t have much relation to the type of training the employees received.
References:
•
Benson, G., McClave, J. T., & Sincich, T. T. (2014). Statistics for Business and Economics, Twelfth Edition. Boston: Pearson Learning Solutions.
•
All Guides: Data Module #1: What is Research Data?: Qualitative vs. Quantitative. (n.d.).
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