)KELVI SANG has an advantageous TV. The TV uses two batteries and both of them are dead.

Influence opens another pack of four incredible batteries and dumps them on the table.

By then he opens the TV and dumps the two dead batteries on the table with the extraordinary ones.

The batteries get worked up and Bob no longer acknowledges which can't avoid being which. (Has this consistently happened to you?)

In the occasion that Bob removes two batteries from the table aimlessly and spots them into the TV, what is the probability that he picked two incredible batteries and that the TV will work?

The typical joblessness rate in California in August 2020 was 11.4%.

2)Assume that 550 employable people in California were picked discretionarily in August 2020.

What is the for the most part expected worth of the amount of people in the model who were jobless?

Round your reaction to one decimal point

3)A deck of 52 playing a round of cards involves four suits, each with thirteen cards.

In a particular game, a hand involves eleven cards picked discretionarily without replacement.

What is the probability that a hand will have unequivocally 7 cards in a comparable suit?

Round your reaction to three decimal spots.

Merciful help! Thankful to you

Accept that in a particular country, the probability of having red hair is 0.20, and the probability of having blue eyes is 0.30. The probability of having the two characteristics is 0.06. The probabilities are showed up in the Venn diagram, where ?An is the event that a self-assertively picked inhabitant of this country has red hair, and ?B is the event that a heedlessly picked tenant has blue eyes.

A not B Both B not A

0.14 0.06 0.24

A=0.20 B= 0.30

Determine the unexpected probability of an individual having blue eyes given that the individual being referred to has red hair, and use the value to choose if having blue eyes and red hair are self-ruling.

P(B/A=)

@30@

Having red hair is? of having blue eyes considering the way that?

a) Market research has shown that 60% of individuals who are familiar with a particular thing truly buy the thing. A discretionary illustration of 15 individuals were familiar with the thing.

I. Describe the variable of interest for the present circumstance.

ii. What probability dissemination do you think best portrays the condition? Why?

iii. Process the probability that accurately 9 will buy the thing.

iv. If 80 individuals are familiar with the thing, choose the amount of person who are needed to buy the thing.

b) It is understood that a typical of 5 trains go through Grand Central Terminal predictably. Find the probability that

I. Absolutely 4 trains will pass rapidly

ii. under 2 trains will pass in an hour

The going with table summarizes the amount of understudies at Kiddies Head start Preschool, characterized by the understudies' sex and whether they have a spot with the infant kid or child region

Child

Child

Complete

Male

12

28

Female

34

26

Supreme

100

a) An understudy is picked aimlessly. Find the probability that

the understudy is male and a young kid

the understudy is female or an infant youngster

iii. the understudy is an infant kid, given that they are female.

b) Are the events 'Infant kid' and 'Female' Mutually Exclusive? Legitimize your Reaction

c) Are the events 'Infant kid' and 'Female' Independent? Legitimize your response

CS 2010 Program 5: Fishers Program description: In this assignment, you will write a complete C++ program, called Fishers program. This program helps you to practice the use of parallel arrays, for loops, and File I/O in C++ program. Create an empty project and add a C++ file using your last name, first initial and pgm5 as the project folder and C++ file name (e.g., ShortM_pgm5 and ShortM_pgm5.cpp). Your instructor and his friends just came from a fishing trip and he is asking you to write a program to determine how many fish they caught and the fisher that caught the most fish. He has a file (i.e., "pgm5.txt", available on Canvas) which includes the name of fishers and the corresponding number of fish caught by each fisher. The input file contains no more than 25 records. (Hint: use this information to declare a global constant (MAX_FISHERS) and declare your arrays (fisher and fish) by using MAX_FISHERS as the size declarator.) 1. In main function, read the data from the input file (pgm5.txt) and store the names of fishers and the number of fish they caught in two arrays. Use a sentinel loop with eof( ). Inside the loop, Read the values into the arrays inside the loop and also keep track of the number of fishers (numfishers) which will be used later. You will declare the file variable (ifstream infile; ), open, use and close the data file. Note: some of the numbers are negative, you need to test every value and change those negative values into their absolute value, then read into the fish array. * * The next activities must use for loops. 2. Display the name and number of fish caught by each fisher on the screen. 3. Find the fisher who caught the most fish - find the subscript of the fisher who caught the most fish, and display the fisher ordering number and name that caught the most fish and the number of fish caught. 4. Find the fisher who caught the least fish - find the subscript of the fisher who caught the least fish, and display the fisher ordering number and name that caught the least fish and the number of fish caught. 5. Sum the number of fish that all fishers caught and display the message of total fish they caught.You are requested to implement the following using only Numpy and a reader function for datasets (you can use the Fisher Iris Dataset included in sci-kitlearn): 1- Implement the gradient decent for logistic regression and test it using 2 classes from the Iris dataset. Compare your results with the built-in logistic regression in sci-kitlearn. 2- Implement the logistic regression algorithm for multi-class classification using One-vs-One and One-vs- All. Use the Fisher Iris Dataset for Assessment.1. The Irwin-Hall distribution In this problem, you are guided to perform simulations of the Irwin-Hall distribution. The Irwin-Hall distribution is also known as the uniform sum distribution. By its name, the Irwin-Hall distribution is the distribution generated by sum of identical uniform distributions. In addition, the Irwin-Hall distribution is a good example to demonstrate the central limit theorem, which will be covered in Chapter 8. Suppose that Uj, U2, Ug ... Un are independent random variables, each with a continuous uniform distribution on the interval [0, 1] (i.e., the standard uniform distribution). Then, Xn = )in U; follows the Irwin-Hall distribution of order n, where n is a positive integer. The mean and variance of X, are = and # respectively. Obviously, for n=1, X, follows an uniform distribution. For n=2, Xn follows a triangular distribution. When n is large, X, can be used to approximate a normal distribution with mean and variance of = and #- 1.(a) To perform a numerical simulation, please write a Matlab function that generates samples from a Irwin-Hall distribution. Please start with the standard uniform distribution and generate 10*6 random sample points (i.e., Output is a vector with 10^6 elements.). Then, repeat the process for n times and sum up those n vectors. The summation results in a vector of 10*6 elements, which is a sample of a Irwin-Hall distribution of order n with sample size of 10 6. Your function should have an input parameter n and output of a vector with 10*6 elements. [Hint: The rand function is convenient to generate samples for the standard uniform distribution.] 1.(b) Now, please use the Matlab function in 1(a) to simulate X, when n=1, 2, and 20. Please plot relative frequency histogram of each output vector, which representing samples of X,, when n=1, 2, 20 (in three separate plots). Then, for each plot, plot a normal distribution with mean and standard deviation of the corresponding X- Overlay relative frequency histogram from a sample of X with a normal distribution. Please comment on the errors when using a Irwin- Hall distribution to approximate a normal distribution with different values of n. [Hint: Pease use histogram(Sample_of_Xn, 100, 'Normalization',pdf) so that histograms are normalized to values of probability density function for ease of comparison with a normal distribution.]