Examples of Theory X in the following topics:
-
- McGregor introduced Theories X and Y, which summarize and compare the classical management and behavioral management perspectives.
- McGregor's main theory is comprised of Theory X and Theory Y.
- Theory X, based more on classical management theory, assumes that workers need a high amount of supervision because people are inherently lazy.
- In Theory X, managers tend to micro-manage and closely supervise employees.
- Explain Douglas McGregor's Theory X and Theory Y approach, merging classical and behavioral organizational theories
-
- Theory X and Theory Y describe two contrasting models of workforce motivation applied by managers in human resource management, organizational behavior, organizational communication, and organizational development.
- Among the many theories of motivation is Douglas McGregor's concept of Theory X and Theory Y.
- To draw something of a parallel here, Maslow's hierarchy has some loose alignment with McGregor's theories, wherein the lower levels of the hierarchy are more along 'X' lines while the higher levels have more of a 'Y' feel to them.
- Under Theory X, management uses control to direct behavior.
- Differentiate between the motivators in Theory X and the motivators in Theory Y
-
- Recall that the standard error of a single mean, $\bar{x}_1$, can be approximated by
- We can rewrite Equation (5.13) in a different way: $SE^2_{\bar{x}_1-\bar{x}_2}={SE^2_{x_1}+SE^2_{x_2}}$ Explain where this formula comes from using the ideas of probability theory.
- If X and Y are two random variables with variances $\sigma^2_{x_1}$ and $\sigma^2_y$, then the variance of X−Y is $\sigma^2_x+\sigma^2_y$.
- Likewise, the variance corresponding to $\bar{x}_1-\bar{x}_2$ is $\sigma^2_{x_1}+\sigma^2_{x_2}$.
- Because $\sigma^2_{x_1}$ and $\sigma^2_{x_2}$are just another way of writing $SE^2_{x_1}$and $SE^2_{x_2}$, the variance associated with $\bar{x_1}-\bar{x_2}$ may be written as $SE^2_{x_1}+SE^2_{x_2}$.
-
- $\displaystyle{f(x) = \sum_{n=0}^\infty a_n \left( x-c \right)^n = a_0 + a_1 (x-c)^1 + a_2 (x-c)^2 + \cdots}$
- $f(x) = 6 + 4 (x-1) + 1(x-1)^2 + 0(x-1)^3 + 0(x-1)^4 + \cdots \,$
- $\displaystyle{f(x) = \sum_{n=0}^\infty a_n x^n = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \cdots}$
- In number theory, the concept of $p$-adic numbers is also closely related to that of a power series.
- All power series $f(x)$ in powers of $(x-c)$ will converge at $x=c$.
-
- $\sinh$ and $\cosh$ are basic hyperbolic functions; $\sinh$ is defined as the following: $\sinh (x) = \frac{e^x - e^{-x}}{2}$.
- The latter is important in many areas of physics, including electromagnetic theory, heat transfer, fluid dynamics, and special relativity.
- $\tanh (x) = \dfrac{\sinh(x)}{\cosh (x)} = \dfrac{1 - e^{-2x}}{1 + e^{-2x}}$
- $\coth (x) = \dfrac{\cosh (x)}{\sinh (x)} = \dfrac{1 + e^{-2x}}{1 - e^{-2x}}$
-
- $f(x) = x + 5$ and $g(x) = 2x - 3$
- $f(x) + g(x) = h(x) = x + 5 + 2x - 3$
- In this case, the -3 of g(x) becomes +3.
- $f(x) - g(x) = h(x) = x + 5 - (2x - 3) = x + 5 - 2x + 3$
- Dividing equations uses similar theory as multiplying, since division is the equivalent of multiplying by the inverse.
-
- In probability theory, a probability density function (pdf), or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.
- For a continuous random variable $X$, the probability of $X$ to be in a range $[a,b]$ is given as:
- $\displaystyle{P [a \leq X \leq b] = \int_a^b f(x) \, \mathrm{d}x}$
- The expected value of $X$ (if it exists) can be calculated as:
- As shown below, the probability to have $x$ in the range $[\mu - \sigma, \mu + \sigma]$ can be calculated from the integral
-
- Pressure is explained by kinetic theory as arising from the force exerted by molecules or atoms impacting on the walls of a container.
- Pressure is explained by kinetic theory as arising from the force exerted by molecules or atoms impacting on the walls of a container, as illustrated in the figure below.
- $\begin{aligned} \Delta p &= p_{i,x}-p_{f,x} = p_{i,x}-(-p_{i,x})\\ &= 2p_{i,x} = 2mv_x \end{aligned}$
- where vx is the x-component of the initial velocity of the particle.
- This gives $\overline{v_x^2} = \overline{v^2}/3$.
-
- The probability distribution of a discrete random variable $X$ lists the values and their probabilities, such that $x_i$ has a probability of $p_i$.
- In probability theory, the expected value (or expectation, mathematical expectation, EV, mean, or first moment) of a random variable is the weighted average of all possible values that this random variable can take on.
- Suppose random variable $X$ can take value $x_1$ with probability $p_1$, value $x_2$ with probability $p_2$, and so on, up to value $x_i$ with probability $p_i$.
- Then the expectation value of a random variable $X$ is defined as: $E[X] = x_1p_1 + x_2p_2 + \dots + x_ip_i$, which can also be written as: $E[X] = \sum x_ip_i$.
- The expectation of $X$ is: $E[X] = \frac{1x_1}{6} + \frac{2x_2}{6} + \frac{3x_3}{6} + \frac{4x_4}{6} + \frac{5x_5}{6} + \frac{6x_6}{6} = 3.5$.
-
- At that time, he already knew that X and Y have to do with gender.
- He was able to conclude that the gene for eye color was on the X chromosome.
- This trait was thus determined to be X-linked and was the first X-linked trait to be identified.
- Males are said to be hemizygous, in that they have only one allele for any X-linked characteristic.
- In Drosophila, the gene for eye color is located on the X chromosome.