3 0.03 0.12\n4 0.42 0.46\n5 0.41 0.28\na. what is the expected value of the job satisfaction score for…

3 0.03 0.12\n4 0.42 0.46\n5 0.41 0.28\na. what is the expected value of the job satisfaction score for senior executives (to 2 decimals)?\n4.05 \n b. what is the expected value of the job satisfaction score for middle managers (to 2 decimals)?\n3.84 \n c. compute the variance of job satisfaction scores for executives and middle managers (to 2 decimals).\nexecutives 0.67 \nmiddle managers 0.89 \n d. compute the standard deviation of job satisfaction scores for both probability distributions (to 2 decimals).\nexecutives 4.05 \nmiddle managers 3.84 \n e. what comparison can you make about the job satisfaction of senior executives and middle managers?\nmiddle managers have higher satisfaction with less variation

3 0.03 0.12\n4 0.42 0.46\n5 0.41 0.28\na. what is the expected value of the job satisfaction score for senior executives (to 2 decimals)?\n4.05 \n b. what is the expected value of the job satisfaction score for middle managers (to 2 decimals)?\n3.84 \n c. compute the variance of job satisfaction scores for executives and middle managers (to 2 decimals).\nexecutives 0.67 \nmiddle managers 0.89 \n d. compute the standard deviation of job satisfaction scores for both probability distributions (to 2 decimals).\nexecutives 4.05 \nmiddle managers 3.84 \n e. what comparison can you make about the job satisfaction of senior executives and middle managers?\nmiddle managers have higher satisfaction with less variation

Answer

Explanation:

Step1: Recall variance formula

The variance formula for a discrete - random variable $X$ is $\sigma^{2}=\sum(x - \mu)^{2}P(x)$, where $x$ are the possible values of the random variable, $\mu$ is the expected value, and $P(x)$ is the probability of $x$.

Step2: Calculate variance for executives

Let the job - satisfaction scores for executives be $x_1 = 3,x_2 = 4,x_3 = 5$ with probabilities $P(x_1)=0.03,P(x_2)=0.42,P(x_3)=0.41$ and expected value $\mu_{executives}=4.05$. [ \begin{align*} \sigma_{executives}^{2}&=(3 - 4.05)^{2}\times0.03+(4 - 4.05)^{2}\times0.42+(5 - 4.05)^{2}\times0.41\ &=(- 1.05)^{2}\times0.03+(-0.05)^{2}\times0.42+(0.95)^{2}\times0.41\ &=1.1025\times0.03 + 0.0025\times0.42+0.9025\times0.41\ &=0.033075+0.00105 + 0.370025\ &=0.40415\approx0.40 \end{align*} ]

Step3: Calculate variance for middle managers

Let the job - satisfaction scores for middle managers be $x_1 = 3,x_2 = 4,x_3 = 5$ with probabilities $P(x_1)=0.12,P(x_2)=0.46,P(x_3)=0.28$ and expected value $\mu_{middle - managers}=3.84$. [ \begin{align*} \sigma_{middle - managers}^{2}&=(3 - 3.84)^{2}\times0.12+(4 - 3.84)^{2}\times0.46+(5 - 3.84)^{2}\times0.28\ &=(-0.84)^{2}\times0.12+(0.16)^{2}\times0.46+(1.16)^{2}\times0.28\ &=0.7056\times0.12+0.0256\times0.46 + 1.3456\times0.28\ &=0.084672+0.011776+0.376768\ &=0.473216\approx0.47 \end{align*} ]

Step4: Recall standard - deviation formula

The standard deviation $\sigma=\sqrt{\sigma^{2}}$.

Step5: Calculate standard deviation for executives

$\sigma_{executives}=\sqrt{0.40415}\approx0.64$

Step6: Calculate standard deviation for middle managers

$\sigma_{middle - managers}=\sqrt{0.473216}\approx0.69$

Step7: Compare job satisfaction

The expected value of job - satisfaction for senior executives ($\mu_{executives}=4.05$) is higher than that for middle managers ($\mu_{middle - managers}=3.84$). The variance of job - satisfaction for senior executives ($\sigma_{executives}^{2}\approx0.40$) is lower than that for middle managers ($\sigma_{middle - managers}^{2}\approx0.47$). So, senior executives have higher average job satisfaction with less variation.

Answer:

c. Executives: $0.40$, Middle managers: $0.47$ d. Executives: $0.64$, Middle managers: $0.69$ e. Senior executives have higher average job satisfaction with less variation.