MATH533: EXPLORATORY DATA ANALYSIS, Exams of Business Statistics

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MATH533: EXPLORATORY DATA

ANALYSIS

COURSE PROJECT: PART A

2 | P a g e

41 149 17.3 0 ONLI

NE

44 168 11.0 5 ONLI

NE

36 121 18.0 2 NONE

47 149 15.8 1 GROU

P

38 135 18.5 1 GROU

P

21 185 18.9 2 ONLI

NE

67 155 17.9 1 NONE

45 149 13.5 1 ONLI

NE

52 193 13.7 5 ONLI

NE

37 159 18.1 0 NONE

33 152 15.0 3 GROU

P

31 170 14.3 4 GROU

P

44 192 16.7 1 GROU

P

44 165 12.4 3 ONLI

NE

39 150 15.3 3 GROU

P

3 | P a g e

43 174 12.7 2 ONLI

NE

42 168 16.4 0 ONLI

NE

49 178 15.1 3 ONLI

NE

41 164 17.8 3 GROU

P

40 191 19.0 5 ONLI

NE

37 132 10.0 0 NONE

36 140 15.7 1 NONE

46 171 14.9 5 ONLI

NE

41 170 12.3 0 ONLI

NE

49 153 19.0 3 GROU

P

42 154 14.3 2 GROU

P

37 142 13.9 3 NONE

37 130 16.9 2 NONE

21 177 17.0 0 ONLI

NE

39 160 14.3 4 NONE

44 134 19.4 5 GROU

P

49 131 14.6 1 GROU

P

35 130 19.4 4 NONE

46 183 15.4 4 ONLI

NE

43 169 14.0 5 GROU

P

41 155 16.0 2 ONLI

NE

48 182 13.0 2 ONLI

NE

39 140 12.4 1 NONE

40 157 15.4 1 ONLI

NE

48 167 14.8 3 ONLI

NE

50 144 15.8 2 NONE

44 168 12.4 2 GROU

P

43 175 13.6 5 GROU

P

33 150 14.9 2 GROU

P

5 | P a g e

67 166 18.9 1 GROU

P

51 178 16.5 1 ONLI

NE

41 178 13.4 2 ONLI

NE

40 176 12.6 1 ONLI

NE

45 138 15.3 2 NONE

41 159 18.8 2 ONLI

NE

40 145 14.7 2 NONE

47 151 16.6 2 GROU

P

48 186 14.2 1 ONLI

NE

42 194 13.6 2 ONLI

NE

41 152 14.5 4 GROU

P

29 145 19.0 2 NONE

48 188 11.3 2 ONLI

NE

33 139 19.3 3 GROU

P

48 201 12.5 1 ONLI

NE

45 156 13.2 3 GROU

P

36 131 18.5 2 NONE

43 161 17.3 3 ONLI

NE

42 152 14.6 1 ONLI

NE

49 178 16.4 2 ONLI

NE

50 157 15.9 3 GROU

P

42 154 15.3 1 GROU

P

44 156 20.0 0 ONLI

NE

45 170 14.2 1 ONLI

NE

48 170 17.4 5 ONLI

NE

39 144 17.7 3 NONE

6 | P a g e B. Discuss the first individual variable, using graphical, numerical summary and interpretation. For the first individual variable, I decided to analyze the type of training, provided in the data set. There are three different types of training, which includes: ➢ Online training ➢ None or no training ➢ Group training This variable is considered to be a qualitative variable; it cannot be measured numerically. I decided to tally up the sums and create a table and pie-doughnut chart to represent my findings. The results are:

8 | P a g e what group of employees had the most experience according to the data set. According to my findings based on the table and bar graph, I noticed that employees with two-years of experience are predominate compared to the other

9 | P a g e employees with different years of experience. The data set only provides information from 0 – 5 years of experience. One can only assume that this can be a fairly new company competing in the market. If my assumption is correct, I created a simple logic chart and line graph to support my findings. BAR CHART EXPERIENCE 35 30 29 25 20 22 20 COUN T 15 10 5 0 9 Frequency (^10 ) 012345 M 0 ore NUMBER OF YEARS

LINE GRAPH YEARS OF EXPERIENCE & AMOUNT OF EMPLOYEES 35 30 29 25 22 20 20 15 10 10 10 9 5 0 1ST 2ND^ 3RD^ 4TH^ 5TH^ 6TH 11 | P a g e The line graph also shows a peak in the fourth year, which also corresponds with the employees with two-years’ experience. D. Discuss the third individual variable, using graphical, numerical summary and interpretation. For the third variable, I decided to analyze the average time on calls per week. I wanted to analyze the average time the company

12 | P a g e spent on conducting sales calls. Using the sample of 100, the company spent an average of 15.338 minutes per call. With the assistance of minitab, I was able to create a histogram to show my findings. ➢ Descriptive Statistics: Time (X2) VARIAB LE

PERC

EN

T

MEA

N

STDE

V

MEDI

A

N

N FOR MODE MO

DE

TIME

(X2)

14 | P a g e relationship is. According to Investopedia, a correlation coefficient with an absolute value of 0.9 or greater would represent a very strong relationship (Investopedia, 2018). In my findings, the result was 0.318, not close to 1. The result is not considered to be a strong relationship between the amount of calls vs sales. Another factor to consider is the coefficient of determination. According to Investopedia, the coefficient of determination, also commonly known as R 2 , is used as a guideline to measure

15 | P a g e the accuracy and is represented as a value between 0 and 1; indicates the extent that the dependent variable is predicted (Investopedia, 2018). For this analysis, amount of sales (y) is the dependent variable, and the amount sales is independent (X1) calls. Therefore, R 2 of 0. means that only 10% of the variance in (Y) Sales is predictable from (X) the calls made. Some important key details regarding particular test that have statistical significance: ➢ Value of P- does not exceed 50% ➢ P-value of .0001, statistical significance having enough evidence to suggest that the amount of calls has some sort of relationship with sales ➢ P- value =. SCATTER GRAM GRAPH

17 | P a g e employees’ years of experience and the amount of sales. The pie- doughnut and bar graph provides the amount of sales in comparison to the amount of employees’ years of experience within the company. Again, the employees with two-years of experience has the highest total of sales generated in a week. ➢ Descriptive Statistics: SALES (Y) SALES (Y)

BAR GRAPH

CHART OF SALES

(Y)

SALES (Y)

VARIABLE YEARS MEAN SUM MINIMU MEDIA MAXIMUM (X3) M N

18 | P a g e

YEARS

(X3)