write a short MATLAB script to implement the above algorithm and generate 10,000 samples of the random variable S. Plot a histogram of the obtained distribution and, on the same set of axes, include a plot of the normal density function (use normfit function). Compute the empirical mean and variance of the distribution (use mean and var functions). Comment on the accuracy of this approach to generate samples from the normal distribution.

Your task this week is to write a very simple spam classifier in python. it will classify messages as either spam (unwanted) or ham (wanted). the program will have a set of spam_words, words that are known to appear in spam messages. that set is included in the template file spam. pypreview the document. you will also define a spam threshold which reflects the allowed percentage of spam words in the message. you'll compute a 'spam indicator', which is the ratio of spam words to the total number of unique words in the message. if the spam indicator exceeds the spam threshold, the message is classified as spam. otherwise it is classified as ham. we'll assume that the spam threshold is a constant and has a value of 0.10. your program will prompt the user for a message and then will print the corresponding spam indicator with two decimal digits and the corresponding classification (spam or ham). the program will be case insensitive. the spam words are detected whether they are in lower case or upper case or mixed case. each word, spam or not, is counted once (even if it appears multiple times in the message.) the program will remove punctuation from the message before identifying the words and computing the spam indicator. for example '! ' must be identified as the spam word 'now'.

Before you enter an intersection on a green light make sure

Melissa needs to add a topic to an email that she will send to her teacher. choose the name of the field where she should type her topic.