Chebenk5
2022-09-13
Answered

Are the actuarial exams hard? I heard that they're hard. is this proper? Are they like the qualifying checks in grad college? as an example, is the opportunity exam and the monetary math examination comparable to qualifying assessments (e.g. desires months and months of have a look at)?

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asked 2021-01-31

The centers for Disease Control reported the percentage of people 18 years of age and older who smoke (CDC website, December 14, 2014). Suppose that a study designed to collect new data on smokers and Questions Navigation Menu preliminary estimate of the proportion who smoke of .26.

a) How large a sample should be taken to estimate the proportion of smokers in the population with a margin of error of .02?(to the nearest whole number) Use 95% confidence.

b) Assume that the study uses your sample size recommendation in part (a) and finds 520 smokers. What is the point estimate of the proportion of smokers in the population (to 4 decimals)?

c) What is the 95% confidence interval for the proportion of smokers in the population?(to 4 decimals)?

a) How large a sample should be taken to estimate the proportion of smokers in the population with a margin of error of .02?(to the nearest whole number) Use 95% confidence.

b) Assume that the study uses your sample size recommendation in part (a) and finds 520 smokers. What is the point estimate of the proportion of smokers in the population (to 4 decimals)?

c) What is the 95% confidence interval for the proportion of smokers in the population?(to 4 decimals)?

asked 2021-02-23

In the middle of the design, she decided to add the following line segment. How would she name this line segment?

___ or ___

___ or ___

asked 2022-09-05

Do a negative Gaussian curvature help with the stability of a surface?

I found the statement "[...] ruled surfaces are statically efficient, especially in the case of skewed ruled surfaces, which are very stable due to a generally negative Gaussian curvature".

Why would a negative Gaussian curvature imply (or help with) the stability of a surface? I did not find a mathematical argument for the claim in the magazine, nor any mention of the same fact elsewhere. Is it really true?

I'm interested in this question because I'm studying ruled surfaces (which have non-positive Gaussian curvature) and their applications in architecture and product design in general, and if such surfaces have strength or stability advantages, I'd be very interested to know.

I found the statement "[...] ruled surfaces are statically efficient, especially in the case of skewed ruled surfaces, which are very stable due to a generally negative Gaussian curvature".

Why would a negative Gaussian curvature imply (or help with) the stability of a surface? I did not find a mathematical argument for the claim in the magazine, nor any mention of the same fact elsewhere. Is it really true?

I'm interested in this question because I'm studying ruled surfaces (which have non-positive Gaussian curvature) and their applications in architecture and product design in general, and if such surfaces have strength or stability advantages, I'd be very interested to know.

asked 2020-12-30

To state:The null and alternative hypotheses.

The sample of 223 sales executives are presented by sales scenarios. Each subject was assigned to the one of the five closing techniques. Sales executives wereasked about their level of trust in the 7-point scale.

The sample of 223 sales executives are presented by sales scenarios. Each subject was assigned to the one of the five closing techniques. Sales executives wereasked about their level of trust in the 7-point scale.

asked 2022-09-02

Numerical method for steady-state solution to viscous Burgers' equation

I am reading a paper in which a specific partial differential equation (PDE) on the space-time domain $[-1,1]\times [0,\mathrm{\infty})$ is studied. The authors are interested in the steady-state solution. They design a finite difference method (FDM) for the PDE. As usual, there are certain discretizations in time-space, ${U}_{j}^{n}$, that approximate the solution u at the mesh points, $u({x}_{j},{t}_{n})$. The authors conduct the FDM method on $[-1,1]\times [0,T]$, for T sufficiently large such that

$\left|\frac{{U}_{j}^{N}-{U}_{j}^{N-1}}{\mathrm{\Delta}t}\right|<{10}^{-12},\phantom{\rule{1em}{0ex}}\mathrm{\forall}j,$

where ${t}_{N}=T$ is the last point in the time mesh and $\mathrm{\Delta}t$ is the distance between the points in the time mesh. The approximations for the steady-state solution are given by $\{{U}_{j}^{N}{\}}_{j}$

I wonder why the authors rely on the PDE to study the steady-state solution. As far as I know, the steady-state solution comes from equating the derivatives with respect to time to 0 in the PDE. The remaining equation is thus an ordinary differential equation (ODE) in space. To approximate the steady-state solution, one just needs to design a FDM for this ODE, which is easier than dealing with the PDE for sure. Is there anything I am not understanding properly?

For completeness, I am referring to the paper Supersensitivity due to uncertain boundary conditions. The authors deal with the PDE ${u}_{t}+u{u}_{x}=\nu {u}_{xx}$, $x\in (-1,1)$, $u(-1,t)=1+\delta $, $u(1,t)=-1$, where $\nu ,\delta >0$ . They employ a FDM for this PDE for large times until the steady-state is reached. Why not considering the ODE$u{u}^{\prime}=\nu {u}^{\u2033}$, $u(1)=-1$, $u(1)=-1$, instead?

I am reading a paper in which a specific partial differential equation (PDE) on the space-time domain $[-1,1]\times [0,\mathrm{\infty})$ is studied. The authors are interested in the steady-state solution. They design a finite difference method (FDM) for the PDE. As usual, there are certain discretizations in time-space, ${U}_{j}^{n}$, that approximate the solution u at the mesh points, $u({x}_{j},{t}_{n})$. The authors conduct the FDM method on $[-1,1]\times [0,T]$, for T sufficiently large such that

$\left|\frac{{U}_{j}^{N}-{U}_{j}^{N-1}}{\mathrm{\Delta}t}\right|<{10}^{-12},\phantom{\rule{1em}{0ex}}\mathrm{\forall}j,$

where ${t}_{N}=T$ is the last point in the time mesh and $\mathrm{\Delta}t$ is the distance between the points in the time mesh. The approximations for the steady-state solution are given by $\{{U}_{j}^{N}{\}}_{j}$

I wonder why the authors rely on the PDE to study the steady-state solution. As far as I know, the steady-state solution comes from equating the derivatives with respect to time to 0 in the PDE. The remaining equation is thus an ordinary differential equation (ODE) in space. To approximate the steady-state solution, one just needs to design a FDM for this ODE, which is easier than dealing with the PDE for sure. Is there anything I am not understanding properly?

For completeness, I am referring to the paper Supersensitivity due to uncertain boundary conditions. The authors deal with the PDE ${u}_{t}+u{u}_{x}=\nu {u}_{xx}$, $x\in (-1,1)$, $u(-1,t)=1+\delta $, $u(1,t)=-1$, where $\nu ,\delta >0$ . They employ a FDM for this PDE for large times until the steady-state is reached. Why not considering the ODE$u{u}^{\prime}=\nu {u}^{\u2033}$, $u(1)=-1$, $u(1)=-1$, instead?

asked 2020-11-08

A researcher is conducting a study to examine the effects of cognitive behavior therapy for the treatment of social anxiety in a sample of 16 participants. He measures the social anxiety scores of participants before the tratment and then again after treatment and the resulting data is as follows:

a) What type of design is this study (single-sample, independent measures, repeated measures)

b)State the null and alternate hypotheses

c) Using an

asked 2022-09-12

I'am finishing my undergraduate degree in pc technological know-how and in spite of having needed to take a few math training my math capacity is still quite terrible. I struggled plenty with it which I consider changed into due to missing a few portions of know-how that I had to realize and not seeing the large photograph. Now i'am reading neural networks and seems they require pretty some math(eg: the backpropagation set of rules uses the chain rule). some human beings say you do not need the maths but I don't think I will be capable of to understand neural networks absolutely with out it. or even if I could get away with i would in all likelihood nevertheless want the mathematics later on.

i have approximately a month that i'm able to dedicate completely to this quest and i am determined to examine linear algebra and multivariate calculus on this time frame. i can start with linear algebra (doesn't depend on calculus, proper?) they have got solutions so I ought to be able to easily tune my progress. I also observed this path from Berkeley Math a hundred and ten. Linear Algebra. It would not have video lectures but it has greater assignments with answers so i'm able to exercise even greater. As for textbooks MIT uses "advent to Linear Algebra, Fourth version, Gilbert Strang" and Berkeley uses "Linear Algebra through S.H. Friedberg,A.L. Insel and L.E. Spence,Fourth edition". people here appear to mention true matters approximately them.

I'am beginning this journey the following day. in the suggest time i'd want to get some advice. Do you've got any recommendation with the intention to make this method smoother? Are there every other assets I should recognize approximately?

i have approximately a month that i'm able to dedicate completely to this quest and i am determined to examine linear algebra and multivariate calculus on this time frame. i can start with linear algebra (doesn't depend on calculus, proper?) they have got solutions so I ought to be able to easily tune my progress. I also observed this path from Berkeley Math a hundred and ten. Linear Algebra. It would not have video lectures but it has greater assignments with answers so i'm able to exercise even greater. As for textbooks MIT uses "advent to Linear Algebra, Fourth version, Gilbert Strang" and Berkeley uses "Linear Algebra through S.H. Friedberg,A.L. Insel and L.E. Spence,Fourth edition". people here appear to mention true matters approximately them.

I'am beginning this journey the following day. in the suggest time i'd want to get some advice. Do you've got any recommendation with the intention to make this method smoother? Are there every other assets I should recognize approximately?