Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Normal distribution by Marjorie Rice 52021 views
- STATISTICS: Normal Distribution by jundumaug1 35632 views
- Standard Score And The Normal Curve by JOHNY NATAD 38511 views
- Chapter9 the normal curve distribution by Nenevie Villando 9905 views
- STATISTICS AND PROBABILITY (TEACHIN... by PRINTDESK by Dan 215866 views

it is a ppt with just properties of normal curves. n few of other things about normal distribution.

No Downloads

Total views

4,376

On SlideShare

0

From Embeds

0

Number of Embeds

2

Shares

0

Downloads

162

Comments

6

Likes

3

No notes for slide

- 1. SUBHRAT SHARMA CUHP13MBA85
- 2. • Each binomial distribution is defined by n, the number of trials and p, the probability of success in any one trial. • Each Poisson distribution is defined by its mean • In the same way, each Normal distribution is identified by two defining characteristics or parameters: its mean and standard deviation. • The Normal distribution has three distinguishing features: • It is unimodal, in other words there is a single peak. • It is symmetrical, one side is the mirror image of the other. • It is asymptotic, that is, it tails off very gradually on each side but the line representing the distribution never quite meets the horizontal axis
- 3. • It is symmetric around the point x = μ, which is at the same time the mode, the median and the mean of the distribution. • It is unimodal: its first derivative is positive for x < μ, negative for x > μ, and zero only at x = μ. • It has two inflection points (where the second derivative of f is zero and changes sign), located one standard deviation away from the mean, namely at x = μ − σ and x = μ + σ. • It is log-concave • It is infinitely differentiable, indeed super smooth of order 2
- 4. Probability density function Cumulative distribution function
- 5. • When number of trials increase , probability distribution tends to normal distribution .hence , majority of problems and studies can be analysed through normal distribution • Used in statistical quality control for setting quality standards and to define control l

No public clipboards found for this slide

Login to see the comments