Sales Managers Craybill Instrument Company: The data found in the Sales Managers dataset contains a handful of variables that the Human Resources (HR) department investigated in order to get a better description of some key characteristics of their hired sales managers. These variables are: Sales Ratio of yearly sales divided by target sales valu for that region WonderScore Score from the Wonderlic Personnel Test; The higher the score, the higher the applicant's perceived ability to manage. SCScore Score on the Strong-Campbell Interest Inventory Test; The higher the score, the higher the applicant's perceived interest in sales. Experience The number of years selling experience prior to becoming a sales manager CodeExp 1 - experienced 5 or more years 0-less experienced, less than 5 years. Engineer 1 - sales manager has a degree in engineering 0 - sales manager does not have a degree in engineering Research Aims: For this analysis, you will only use engineer degree, CodeExp and sales. 1. Determine if the proportion Engineers who are experienced (code = 1) is significantly higher than the proportion of non- Engineers who are experienced (code = 1). (Two-way frequency table followed by a two proportion Z test). 2. Determine if the mean sales performance is significantly higher for those with an Engineer degree compared to the mean sales of those without an Engineer degree. (side-by- side boxplot and two-sample t-test assuming unequal variances followed by a confidence interval). Two-proportion Z test: Visually summarize the relationship between Engineer deumo Two-proportion Z test: Visually summarize the relationship between Engineer degree and CodeExp by constructing a pivot table with Engineer degree (Yes/No) on the rows and CodeExp on the columns. Then right-click on the total and select % of row total'. Report the values as percentages to one decimal place. This reports the percentage of those with an engineer degree who are experienced and the percentage of those without an engineer degree who are experienced. Now insert a side-by-side bar chart and label the bars. Copy and paste the pivot chart and the labeled graph. (4 points) Using the percentage of Engineers with Experience (code = 1) and the percentage of non-Engineers with experience (code=1), perform a two-sample proportion test to see if, at the a=0.10 level of significance, the data support that the proportion of Engineers with more experienced (code=l) is significantly higher than the proportion of non-Engineers with experience (code = 1). Please show your work and state your conclusion in a clear sentence using the context of the data. Write the null and alternative hypotheses Calculate the Z test statistic Calculate the p-value and compare the p-value with the significance level. Make a clear concluding statement. NOTE Salvador 27 41 05 3 1 1 0 1 xa 10 3 ---- Yr N Ne No ye 47 111 95 70 117 118 ES ST 11 1 34 15 61 50 101 N Ver 27 h IF SE 1 Yes 31 31 1 1 Yes 119 130 115 131 99 182 1 TE ST Yes 0 1 44 25 2 31 33 33 19 30 40 64 30 IN 100 11 1 1 111 3 0 Y Ves 34 1 33 19 1 1 13 104 133 135 w 90 130 A 9 100 23 30 5 Sos 1 1 1 1 54 33 30 11 11 1 . Y F 3 120 VE 3 2 1 0 1 41 14 56 IL COE 4 3 You V Yes 34 54 15 11 10 10 15 1 . 1 73 106 30 50 5 o Yes + 1 1 11 Sa 304 109 34 41 SO 4 13 5 9 pot 06 1 1 1 LE I ME Ye M LIL DOC 30 59 = $30.3.3 10 10 4 1 1 e 1 1 1 13 10 17 11 11 105 w V 1 49 10 1 134 35 35 16 11 11 1 1 103 10 2 Ne Y Ver No 0 3 4 1 1 31 45 16 1 4 We 104 115 133 100 129 121 I 37 3 20 1 See 13 Sales Managers Craybill Instrument Company: The data found in the Sales Managers dataset contains a handful of variables that the Human Resources (HR) department investigated in order to get a better description of some key characteristics of their hired sales managers. These variables are: Sales Ratio of yearly sales divided by target sales valu for that region WonderScore Score from the Wonderlic Personnel Test; The higher the score, the higher the applicant's perceived ability to manage. SCScore Score on the Strong-Campbell Interest Inventory Test; The higher the score, the higher the applicant's perceived interest in sales. Experience The number of years selling experience prior to becoming a sales manager CodeExp 1 - experienced 5 or more years 0-less experienced, less than 5 years. Engineer 1 - sales manager has a degree in engineering 0 - sales manager does not have a degree in engineering Research Aims: For this analysis, you will only use engineer degree, CodeExp and sales. 1. Determine if the proportion Engineers who are experienced (code = 1) is significantly higher than the proportion of non- Engineers who are experienced (code = 1). (Two-way frequency table followed by a two proportion Z test). 2. Determine if the mean sales performance is significantly higher for those with an Engineer degree compared to the mean sales of those without an Engineer degree. (side-by- side boxplot and two-sample t-test assuming unequal variances followed by a confidence interval). Two-proportion Z test: Visually summarize the relationship between Engineer deumo Two-proportion Z test: Visually summarize the relationship between Engineer degree and CodeExp by constructing a pivot table with Engineer degree (Yes/No) on the rows and CodeExp on the columns. Then right-click on the total and select % of row total'. Report the values as percentages to one decimal place. This reports the percentage of those with an engineer degree who are experienced and the percentage of those without an engineer degree who are experienced. Now insert a side-by-side bar chart and label the bars. Copy and paste the pivot chart and the labeled graph. (4 points) Using the percentage of Engineers with Experience (code = 1) and the percentage of non-Engineers with experience (code=1), perform a two-sample proportion test to see if, at the a=0.10 level of significance, the data support that the proportion of Engineers with more experienced (code=l) is significantly higher than the proportion of non-Engineers with experience (code = 1). Please show your work and state your conclusion in a clear sentence using the context of the data. Write the null and alternative hypotheses Calculate the Z test statistic Calculate the p-value and compare the p-value with the significance level. Make a clear concluding statement. NOTE Salvador 27 41 05 3 1 1 0 1 xa 10 3 ---- Yr N Ne No ye 47 111 95 70 117 118 ES ST 11 1 34 15 61 50 101 N Ver 27 h IF SE 1 Yes 31 31 1 1 Yes 119 130 115 131 99 182 1 TE ST Yes 0 1 44 25 2 31 33 33 19 30 40 64 30 IN 100 11 1 1 111 3 0 Y Ves 34 1 33 19 1 1 13 104 133 135 w 90 130 A 9 100 23 30 5 Sos 1 1 1 1 54 33 30 11 11 1 . Y F 3 120 VE 3 2 1 0 1 41 14 56 IL COE 4 3 You V Yes 34 54 15 11 10 10 15 1 . 1 73 106 30 50 5 o Yes + 1 1 11 Sa 304 109 34 41 SO 4 13 5 9 pot 06 1 1 1 LE I ME Ye M LIL DOC 30 59 = $30.3.3 10 10 4 1 1 e 1 1 1 13 10 17 11 11 105 w V 1 49 10 1 134 35 35 16 11 11 1 1 103 10 2 Ne Y Ver No 0 3 4 1 1 31 45 16 1 4 We 104 115 133 100 129 121 I 37 3 20 1 See 13
