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MAT 1140: Introduction to statistics

Some of the discussions have focused on finding good and bad graphics. The discussion will focus on the story that goes better behind the graphics. It will serve as a segue into Chapter 10.
Initial Post
Step 1. Look through the Spurious Correlation website to find correlations that you really like or that you find humorous or surprising. Note that there are additional correlation , but you will need to click on the “Next Page” link towards the bottom of the first page to navigate to them.
Step 2. Find two correaltion , one positive and one negative. Add the both charts to your discussion post and indicate which is your postitive correlation and which one is your negative correaltion. Then attempt to explain the correlation. Remember that thes are called spurious correlation and may not have a serious explanantion, so go for the absurd. The more elaborate the strange the story the better.

disscussion

Hi, I need help with a discussion and I have to reply to two classmates. Discussion: What is the difference between a discrete probability distribution and a continuous probability distribution? Give your own example of each.
What is the expected value, and what does it measure? How is it computed for a discrete probability distribution? Classmate #1: the difference between discrete random variables and continuous random variables is the number of values that can be assumed, discrete variables can only assume a finite or limited series of values while continuous variables can assume one of an infinite possible set of values. examples of discreet variales are countable variables; you can count the money in your bank account, or you can count the number of change in your pocket. continuous variables are variables that would take forever to count like the total number of values between 0 and 3 or age. Expected value is the mean and it measures the central tendency of the distribution. the expected value is the sum of x multtiplied by the probability. Classmate #2: Discrete probability distribution is the random varible that can take finite number of discrete values, if a random variable is discrete random variable then its probability Distribution is known as Discrete probability distribution. The following distributions are some distributions that comes under discrete probability distributions, binomial distribution, poisson distribution, hypergeoetriv distribution, negative binomial distribution. An example of this when you flip a coin two times we hvae 4 possible outcomes, X the discrete random variable represents the number of heads then X can take only 0, 1 or 2.
Continous probability distribution is the random variable can take infinate number of values in an interval is known as continous random variable. If a random variable is continous random variable then its probability distribution is known as Continous probability distribution. The following distributions are some distributions that comes under continous probability distributions uniform distribution, exponential distribution, normal distribution, and standard normal distribution. The time taken by the professor to grade a paper is uniformly over 5 or 10 minutes. Let X be the random variable representing the time taken by professor to grade a paper, which can take values between 5 and 10 minutes.
Expected value: The expected value of a random variable is closely related to the weighted average and intuitively is the arithemetic mean a large number independent relaizations of that variable. It is also known as ‘Mean’ or ‘Expectation’, ‘average’ or first moment. The expected value of a random varible gives measure of centre of disribution of the random varible.The Discrete probability disribution can be calulated by using:
E[X] = =

Six Capasitor

Statistics Assignment Help In a box there are six capacitors, four of which are worth 0.1 ?? and the remainder
is worth 1 ??. From the box, three capacitors are taken at random. If ? is a random variable
stated MANY VALUE 1 ?? CAPACITORS” IN THREE CAPACITORS TAKEN “then
specify:
a. Random Variable Result Area ?
b. Probability Mass Function (PMF) Random Variable ?
c. Random Variable Distribution Function ?
d. E (X) and VAR (X)

Statistics Question

Statistics live assignment on January 4th 9 am – 12 pm EST.
There will be 10 questions with multiple parts to each question. There is a total of 2 hours to complete the live assignment.
I have attached a sample question below and the topics that will be on the live assignment

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