Describe five real-world applications in which regression can be used. For each of these applications, describe the y-value and the corresponding feature vector X. Also discuss whether linear regression can be used in each case.
Regression is basicly when you have to predict a value ( mostly continuous) based on some old data set which has that value along with the parameters which are common in that data set and what you have to predict that target value.
1) Stock Market Prediction - based on some 10-12 parameter like what's the current value , what's the day before and a few move as a vector [X1,X2,X3... ] We have to predict the value of he stock at the end of today ( Y).
2)Realstate price prediction - based on the number of rooms (X1), bathroom (2), sq. Foot area(3) and a few more parameter which you personally see before buying a property you can have these as [X1,X2,X3...] And then predict the selling price of a new property which you haven't seen (Y).
3) What's the score of the team at the end of the game, we have per over run rate which they are making, number of wickets down and the previous performance of a cricket team all these are X1,X2,X3 we can choose any of these and predict the score at the end of the game (Y).
4) Your own Height, based on your parents and siblings height at a given age you can predict your age (Y) based on your age(X).
5) Your Salary(Y) after your graduation based on parameters like grades in each semester or overall grades(X), can be a prediction job for regression.
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Describe five real-world applications in which regression can be used. For each of these applications, describe...
Using resources such as the Internet and Section 7.7, identify two real-world applications of HGLs and EGLs that are interesting to you. For each application, write a paragraph in which you describe how and why HGLs and EGLs are used
4. You will now think of some real-life applications for statistical learning. (a) Describe three real-life applications in which classification might be useful. Describe the response, as well as the predictors. Is the goal of each application inference or prediction? Explain your answer. (b) Describe three real-life applications in which regression might be useful. Describe the response, as well as the predictors. Is the goal of each application inference or prediction? Explain your answer. (c) Describe three real-life applications in...
Q. 22 The deviation which is explained by the regression can be expressed as: a. the difference between each actual Y value and the mean of Y b. the difference between each residual value and the corresponding Y value: (e - Y) c. the difference between each predicted Y value and the mean of Y d. the difference between each actual Y value and the predicted y value e. the difference between each x value and each y value: (x...
There are important applications in which due to known scientific constraints, the Problem 5 of 5 regression line must also go through origin (i.e. the intercept must be zero). In other words, the model should read Y Bai,i 1,2,.,n This model is often called the regression through the origin model. Assuming that e's are independent with distribution N(0, o2) (a) Show that the least squares estimator of the slope is ΣL Υ B = Σ (b) Show that B in...
23. Describe the two categories of long-term memory and their subtypes (there are five). Discuss the brain regions involved in each, and provide a real-world example of three of them from your own life. (As a reminder, you can find a discussion of these types of memory in the Video Learning and Memory: Basic distinctions 2 at about 25 minutes). 24. Describe what happens when you damage the brain, and discuss why the nervous system has a difficult time repairing...
. Necessary assumptions for regression two scatter plots and the corresponding regression lines in the following dilagrams. Identily which graph is more homoscedastic. Graph I Graph II Y SCORES Y SCORES 10 10 2 0 2&1 X SCORES 10 X SCORES You wish to use a least squares regression line to predict a new response value Y for a given explanatory value X. Which of the assumptions are necessary in order to expect some accuracy in the prediction? Check all...
There are four conditions that should be at least approximately true for linear regression. A plot of residuals versus the x values can be used to check which of the following conditions: A straight line is the best model to describe the association between x and y, AND the variance of the values of y at each value of x should be the same. Observations in the sample are independent of each other AND there should be no extreme outliers...
• Select a financial institution or market and discuss the causes of asymmetric information. Describe real-world examples of adverse selection and moral hazard problems for your institution/intermediary or market. Evaluate the impacts of adverse selection and moral hazard problems on your financial institution/intermediary or market. Discuss a principal-agent problem in your financial institution/intermediary or market. A principal-agent problem is a moral hazard problem between managers and shareholders. Analyze whether your financial institution/intermediary or market can reduce the adverse selection and/or...
Question 6A regression line can be used to determine the strength of a relationship. determine if there is a cause and effect relationship. predict Y for any X value. establish if a relationship is linear. Question 7 If the correlation coefficient R between two variables is ,it is expected that the slope of the regression line will be positive; positive positive; large negative; small positive; negative Question 8 If the slope of the simple regression line is .12, then the Pearson correlation coefficient r is expected to be positive negative small large
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7. Think of two variables in your field for which linear regression could be used to explain their possible association. a) Describe each component of the simple linear regression equation using your example. Y = Be + B1X + € X: Bo: B1: E: b) State the null and alternative hypotheses using words (not just symbols) of your example. He: Hi: