homework2

Question 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.

The paper "The Effects of Adolescent Volunteer Activities on the Perception of Local Society and Community Spirit Mediated by Self-Conception"† describes a survey of a large representative sample of middle school children in South Korea. One question in the survey asked how much time per year the children spent in volunteer activities. The sample mean was 14.76 hours and the sample standard deviation was 16.54 hours. USE SALT Based on the reported sample mean and sample standard deviation, explain why it is not reasonable to think that the distribution of volunteer times for the population of South Korean middle school students is approximately normal. (Round your answer to nearest percent.) If the distribution of volunteer times is approximately normal, for the sample standard deviation of s = 16.54 hours and the sample mean of x = 14.76 hours, approximately 19 negative. Therefore, it is not reasonable to think that the distribution of volunteer times is approximately normal. % of…

Build A Machine Learning Regression Model with Python and TensorFlow for : • One Variable • Multiple inputs : A DNN regression • One variable • Full model

here is this statistical series in the image : all the work is in matlab. 1. Insert this series in matlab 2. Build the histogram of this series 3. Calculate the mean of this series 4. Calculate the variance of this series 5. Calculate the standard deviation of this series 6. Find the median Me of this series

Explain the flaws in this model training strategy. What's your solution? We want to create a hip X-Ray deformity prediction model. 100 individuals have 640 frontal X-rays. Three orthopedic physicians label the photos as positive or negative for hip deformity. The picture dataset was randomly divided among 80% training (training and validation) and 20% testing.

The advantages of transitioning from stepwise regression to all-subsets regression are described in detail below.

What are the major differences in use-cases for linear regression as compared with non-linear types?

The benefits of all-subsets regression over stepwise regression are discussed in detail below.

given the observed data (obsX,obsY), learning rate (alpha), error change threshold, and delta from the huber loss model,write a  function returns theta0 and theta1 that minimizes the error. Use pseudo huber loss function

The data normalization process is one of the pre-processing stages needed to improve model performance. Even so, some machine learning models use calculations that pay attention to the unit of predictor variables used so that the normalization process is something that must be done. From the models under which model requires data normalization process? a) Multiple Linear Regression] b) Random Forest c) Binomial Logistic Regression d) k-NN

Explain the difference between the sharp regression discontinuity design and the fuzzy regression discontinuity design. Give an example of each and illustrate them graphically.

2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)

Below is a detailed discussion of the advantages of all-subsets regression versus stepwise regression.

a. Feature selection can be done through both filter and wrapper method. Which of these methods is more accurate and which of these is more efficient? Explain the tradeoff and justify your answer with an example. b. Why in some situations logistic regression model is preferred over linear regression?

Is Logistic regression based on a linear combination of parameters and a link function called sigmoid?

Can you help me write the code for this MATLAB assignment?

Write code to define a K-NN regression model with K=5 and the neighbors are weighted by the inverse of their distance. [ ] # Write your code here

All-subsets regression is superior than stepwise regression in terms of data analysis, therefore explain why this is so.

I need to plot the data from a simple linear regression with its values x and y with the fitted y values from fitting a logistic regression model. How do I plot the x values with two different y's, y values on the left side with fitted y values on the right? I want to generate the fitted/predicted y values using logistic regression and then create a plot using all those three variables? Here is an example of my SAS code that doesn't work. data coupons;input redeemed discount;cards;100 5147 9176 11211 13244 15277 17310 19343 21;run; ods graphics on;proc logistic data=coupons;model redeemed(event='1') = discount;effectplot;run; It give me this warnings and I do not understand…. NOTE: Option EVENT= is ignored since LINK=CLOGIT. NOTE: PROC LOGISTIC is fitting the cumulative logit model. The probabilities modeled are summed over the responses having the lower Ordered Values in the Response Profile table. Use the response variable option DESCENDING if you want to reverse the assignment of…

Please find an article highlighting the use of one of the predictive models on a real-world dataset: Linear Regression, Logistic Regression, Decision Trees, Neural Networks, Support Vector Machines, Ensemble Methods or Multiclass Classification Techniques. Using your own words, review the article and highlight the points that you thought were most important or interesting.