Write this photo in Matlab use: c. In the main script files, plot the CDF of SNR for all users and discuss the results. The following is an example of CDF graph. Make sure to plot the SNR in dB. CDF is a cumulative distribution function that present the probability the data has <= certain level. IN the figure below for example, the probability the SNR<=500 is 0.5. The Y axis here represent the probability, while the X axis is the SNR data. 07 02 01 1000 1500 2000 2500 3000 3500 4000 SNR 500 4500
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- In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535You’ve just finished fitting a logistic regression model for email spam detection, and it is getting abnormally bad performance on both your training and validation sets (AUC of 0.55 on Train and 0.53 on Validation dataset). You know that your implementation has no bugs, so what could be causing the problem? a. You are underfitting b. You are overfitting.Consider the elliptic curve group based on the equation where a = 1405, b = 2011, and p = 2531. We will use these values as the parameters for a session of Elliptic Curve Diffie-Hellman Key Exchange. We will use P = (0,98) as a subgroup generator. You may want to use mathematical software to help with the computations, such as the Sage Cell Server (SCS). On the SCS you can construct this group as: G=EllipticCurve(GF(2531),[1405,2011]) Here is a working example. (Note that the output on SCS is in the form of homogeneous coordinates. If you do not care about the details simply ignore the 3rd coordinate of output.) 62 Alice selects the private key 41 and Bob selects the private key 20. What is A, the public key of Alice? y² = x³ + ax+b mod p What is B, the public key of Bob? 2472 After exchanging public keys, Alice and Bob both derive the same secret elliptic curve point TAB. The shared secret will be the x-coordinate of TAB. What is it?
- For each product, find the median sales quantity (assume an odd number of sales forsimplicity of presentation). (NOTE – “median” is defined as “denoting or relating to avalue or quantity lying at the midpoint of a frequency distribution of observed values orquantities, such that there is an equal probability of falling above or below it.” E.g.,Median value of the list {13, 23, 12, 16, 15, 9, 29} is 15. Report #3:PRODUCT MEDIAN_QUANT======= ============Bread 422Milk 1976. . . .1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression O K-Means Clustering K-Nearest NeighborsImplement a D-i-D in this problem. Load the dataset on STATA: use http://www.stata.com/data/jwooldridge/eacsap/jtrain1 This has data on firms and the amount of job training they get. Only use the data from 1987 and 1988. Carefully study the data before you proceed. Construct the D-i-D estimator in different ways: (a) Run the regressionhrsempit = β0 + β1 grant it + β21( year = 1988) + β3Ei + uit where Ei is a dummy variable for being a treatment (i.e. someone who would receive the grant in 1988). (b) Run the fixed effect regression with firm fixed effects θi: hrsempit = θi + β1 grant it + β21( year = 1988) + uit (c) Construct the 4 means of controls and treatments, before and after, and es- timate the difference in difference with means. (d) Do you get exactly the same answer, why or why not? (e) Now include other controls to estimate the D-i-D regression. Justify what- ever you include and interpret.
- implement a D-i-D in this problem. Load the dataset on STATA: use http://www.stata.com/data/jwooldridge/eacsap/jtrain1 This has data on firms and the amount of job training they get. Only use the data from 1987 and 1988. Carefully study the data before you proceed. Construct the D-i-D estimator in different ways: (a) Run the regressionhrsempit = β0 + β1 grant it + β21( year = 1988) + β3Ei + uit where Ei is a dummy variable for being a treatment (i.e. someone who would receive the grant in 1988). (b) Run the fixed effect regression with firm fixed effects θi: hrsempit = θi + β1 grant it + β21( year = 1988) + uit (c) Construct the 4 means of controls and treatments, before and after, and es- timate the difference in difference with means. (d) Do you get exactly the same answer, why or why not? (e) Now include other controls to estimate the D-i-D regression. Justify what- ever you include and interpret. Provide line by line code for STATA and the solutionQuestion 1: In class we went over the derivation for linear regression on the white board. Starting with = the step where you have E (Y – XW)¹ (Y – XW) write the derivation to get to the step W = (XTX)-¹XTY. Explain the math used in each step.Each person in a group of n persons (n >= 4) has independent information known only to themselves. Whenever a person calls another person in the group, they exchange all the information they know at the time of calling. Design a sequence of calls (specifying the persons involved in each call) that they have to make in order to ensure that everyone knows all the information. How many calls are there in your scheme?
- To what end are inferential statistics put to use? .Write a computer code to do linear regression analysis of a given dataset to find the relation between two variables which gives the least sum of squares error. Using Excel or Matlab. Include a copy of the script, a sample input, and a sample output from your codes. Sample output must include modelfit parameters and the sum of the squared errors for the fit. Please also run the code for thefollowing data set to find and report the relation between y and x:x y25 3040 80120 15075 80150 200300 350270 240400 320450 470575 583How do you do this part in MATLAB? Modify 1. Replace the elements of r_vec with the indices 4, 6, 8, 10 and 15 with 3, 6, 9, 12 and 15, respectively. 2. Replace the elements of mat with the indices (4, 5), (4, 17), (20, 5) and (20,17) with 5, 8, 2 and 9, respectively. 3. Replace the all the elements of the eleventh column of mat with ones. 4. Replace the characters 11 to 19 of chr with the words white cat. 5. Replace the first entry of str with the value of sclr.