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Maslowich
3 years ago
7

If the supply of computer engineers increases at the same time that the demand for these workers decreases, what would be the MO

ST LIKELY effect on wages for these workers?
A) Wages would stay the same as job opportunities increase.
B) Wages would increase as the number of workers increases.
C) Wages would decline as the competition for jobs increases.
D) Wages would increase as competition for these workers increases
Computers and Technology
1 answer:
stiv31 [10]3 years ago
5 0

Answer:

Option C is correct.

Explanation:

When the supply of system engineers rises while demand for such workers falls at the similar period, Wages will drop while competition for employment rises.

According to the law of demand, when the supply of one commodity is increased then the demand for other commodity falls. So, the following option  C is correct according to the following scenario.

Other options are incorrect because they are not relevant according to the following scenario.

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Which component of the computer keeps the operating system when the computer is running​
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The data below represents the yearly earnings (in $1000s of dollars) that high school and college (BS) graduates earn at a small
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Answer:

library(moments)

library(readxl)

Exam1Q1 <- read_excel("Exam1Q1.xlsx")

View(Exam1Q1)

## Warning in system2("/usr/bin/otool", c("-L", shQuote(DSO)), stdout = TRUE):

## running command ''/usr/bin/otool' -L '/Library/Frameworks/R.framework/

## Resources/modules/R_de.so'' had status 1

plot(density(Exam1Q1$Highschool))

gostino.test(Exam1Q1$Highschool)

##  

##  D'Agostino skewness test

##  

## data:  Exam1Q1$Highschool

## skew = -0.24041, z = -0.69269, p-value = 0.4885

## alternative hypothesis: data have a skewness

agostino.test(Exam1Q1$BS)

##  

##  D'Agostino skewness test

##  

## data:  Exam1Q1$BS

## skew = 0.53467, z = 1.49330, p-value = 0.1354

## alternative hypothesis: data have a skewness

anscombe.test(Exam1Q1$Highschool)

##  

##  Anscombe-Glynn kurtosis test

##  

## data:  Exam1Q1$Highschool

## kurt = 2.56950, z = -0.29677, p-value = 0.7666

## alternative hypothesis: kurtosis is not equal to 3

anscombe.test(Exam1Q1$BS)

##  

##  Anscombe-Glynn kurtosis test

##  

## data:  Exam1Q1$BS

## kurt = 3.6449, z = 1.2599, p-value = 0.2077

## alternative hypothesis: kurtosis is not equal to 3

t.test(Exam1Q1$Highschool,Exam1Q1$BS, var.equal = T)

##  

##  Two Sample t-test

##  

## data:  Exam1Q1$Highschool and Exam1Q1$BS

## t = 0.16929, df = 76, p-value = 0.866

## alternative hypothesis: true difference in means is not equal to 0

## 95 percent confidence interval:

##  -1.131669  1.341926

## sample estimates:

## mean of x mean of y  

##  39.51282  39.40769

Explanation:

Summary:

The data set include two dependent variables which states High School annual earnings and BS annual earnings in the same small firm. Firstly, I used density plot function to view the distribution of two sets of variables. Secondly, I tested the skewness and kurtosis.Skewness of High School is -0.24041 which is left tail. Skewness of BS is 0.53476 which is right tail.Kurtosis of High School is 2.5695 which lower than 3 is platykurtic. Kurtosis of BS is 3.6449 which larger than 3 is leptokurtic. Thirdly, the sample population is 39 for each and has unknow standard deviation, so that I’m going to use t-test to perform the comparing analysis. The result shows p-value which equal to 0.866 is larger than significant value(alpha = 0.05). To the concludsion, there is no difference between yearly earnings of High School and of BS at a small firm.

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Answer:

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