Mean of computer experience for Males: 10.21818 Standard Deviation of computer e
ID: 374336 • Letter: M
Question
Mean of computer experience for Males: 10.21818
Standard Deviation of computer experience for Males: 2.973282
Mean of computer experience for Females: 9.296296
Standard Deviation of computer experience for Females: 2.839486
The 95% confidence interval for the difference in the two means is:
Since this interval contains the point zero, we can conclude that the differences in the 2 means of computer experiance of males and females is not significant i.e. the difference in the mean can be statistically approximated to zero.
The 95% confidence interval is obtained by t test using the statistical software R. The above figure shows the result. The R code to obtain this is:
d1 = read.csv("data1.csv")
d1_gender_male = subset(d1, d1$Gender=="Male")
d1_gender_female = subset(d1, d1$Gender=="Female")
mean(d1_gender_male$Years.Experience)
mean(d1_gender_female$Years.Experience)
sd(d1_gender_male$Years.Experience)
sd(d1_gender_female$Years.Experience)
t.test(d1_gender_male$Years.Experience, d1_gender_female$Years.Experience)
(b)
Mean of computer experience of people with PS's: 8.685714
Standard Deviation of computer experience of people with PS's: 2.446744
Mean of computer experience of people without PS's: 10.82979
Standard Deviation of computer experience of people without PS's: 2.973208
The 95% confidence interval for the difference in the two means is:
Since this interval does not contain the point zero, we can conclude that the differences in the 2 means of computer experiance of people with and wothout PC's is significant i.e. the difference in the mean cannot be statistically approximated to zero. Thus there is difference in the mean of the years of experiance of people with and wothout PC's and since the confidence interval shows negative value it means that the years of experiance of people without PC's is more than people with PC's.
The 95% confidence interval is obtained by t test using the statistical software R. The above figure shows the result. The R code to obtain this is:
d1 = read.csv("data1.csv")
d1_pc_yes = subset(d1, d1$Own.PC == "Yes")
d1_pc_no = subset(d1, d1$Own.PC == "No")
mean(d1_pc_yes$Years.Experience)
mean(d1_pc_no$Years.Experience)
sd(d1_pc_yes$Years.Experience)
sd(d1_pc_no$Years.Experience)
t.test(d1_pc_yes$Years.Experience, d1_pc_no$Years.Experience)
(c)
Since the PC ownership is statistically significant than the Gender, the PC ownership is likely to be a better predictor than the Gender.
Explanation / Answer
PRACTICE Dependency resolution is an important component of maintaining the integrity of a relational database. Your assignment is to convert this non-normalized table into a set Seku Descripobon of four normalized tables, and draw an entity-relationship (ER) diagram with attributes, relationships, and cardinalities i chased Use the following business rules: 1. Each purchase order has exactly one vendor. 2. Each purchase order can have multiple items. Save your diagram as an image, and upload it to Dropbox on eCourseware. (If you use Draw.io, you should not upload an XML file of your diagram.) Inventory +Purchase Order Number +Purchase Order Date +Purchase Price +Vendor Number +Vendor Name Vendor Address +Vendor State +Vendor ZIP