01 Aug 2023

Posted on:

17 Dec 2022

0

# I couldnt understand the quiz qs can anyone explain this please?

I couldnt understand the quiz qs can anyone explain this please?

Super learner
Posted on:

29 Dec 2022

0

Okay let me explain this, first Distribution is not a graph somehow to represent distribution we use Graphs soo first two option are not valid
now lets understand talk about last one which says "Distribution is a function that shows probability of a variable" which at first is true too just too cross check lets see 3rd one which says "distribution is function that shows possible value of variable and probability of variable" which we saw in last lecture distribution represented by both dice value(variable value) and probability of that dice occurance.

So 3rd on is more accurate and explanatory on itself for distribution.

Posted on:

01 Aug 2023

1

The incorrect statement is: "All variables can be represented by the Normal distribution."

Explanation: While many variables in nature and human-made systems do approximately follow a Normal (Gaussian) distribution, it's not correct to say that all variables can be represented this way. Some data distributions are far from Normal. For example, income distribution in most countries, the distribution of species in ecological niches, or the distribution of the sizes of cities follow power-law distributions (sometimes called Pareto or Zipf distributions), not Normal distributions. Additionally, some variables have skewed distributions (e.g. the price of houses), while others might have a bimodal distribution (e.g. height distribution of adults when not distinguishing between males and females).

Why the others are correct:

"Distributions of sample means with large enough sample sizes could be approximated to Normal." This is the central limit theorem, which states that if you have a large enough sample size, the distribution of the sample means will be approximately normally distributed, regardless of the shape of the population distribution.

"Computable statistics are elegant."

Many statistical techniques are simplified or made possible when data follow a Normal distribution. The elegance comes from the mathematical properties of the Normal distribution that make these computations straightforward and analytically tractable.

"Decisions based on Normal distribution insights have a good track record."

Many aspects of modern life, including quality control in manufacturing, financial risk management, and other fields, are guided by insights gleaned from normally distributed data. While it's not a guarantee of success (and indeed can lead to issues if the data aren't actually normally distributed), there's a long history of effective decision-making based on this distribution.