Using r studio and the lahman baseball package, use a run differential model to determine the run value of a win in the mlb for the 1970s.
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Using r studio and the lahman baseball package, use a run differential model to determine the run value of a win in the mlb for the 1970s.
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- Theoretical Overview Suppose we have a set of data consisting of ordered pairs and we suspect the x and y coordinates are related. It is natural to try to find the best line that fits the data points. If we can find this line, then we can use it to make all sorts of other predictions. In this project, we're going to use several functions to find this line using a technique called least squares regression. The result will be what we call the least squares regression line (or LSRL for short). In order to do this, you'll need to program a statistical computation called the correlation coefficient, denoted by r in statistical symbols: NOTE: Equation is written assuming you start at the value 1. Lists start at index 0. Once you have the correlation coefficient, you use it along with the sample means and sample standard deviations of the x and y-coordinates to compute the slope and y-intercept of your regression line via these formulas: Tasks: In this project, you must read…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 3535MatLab Load the data flu.mat (you can do this by typing load flu in your script). This data is the flu trends seen in the United States 2005-2006, divided by region. We will use regressions to look at the data during flu season in the Pacific region. Create your x data: have x equal to 1:30. These represent 30 weeks between Oct. 2005 and May 2006. Create your y data: have y equal to flu.Pac(1:30)’. This is the flu trend for each week. Make sure you have an apostrophe after the last parenthesis. Fit the data below with a straight line and with a 2nd order polynomial. Use least-squares regression. Calculate the coefficient of determination (r^2) and the correlation coefficient (r) for each regression. Plot the two regression curves against the data. Which regression is better? Is there a polynomial you think would work better? Describe the data – what does it mean to you?
- You run a logistic regression model in R using the glm() function. The dependent variable is the factor variable Y and independent variables are X1 and X2 (in other words, the formula is Y~X1+X2). In the model output, the coefficient of the constant term is a0, the coefficient of X1 is a1, and the coefficient of X2 is a2. Assuming a cutoff = 0.5, which of the following defines the equation of a decision boundary? a0 + a1X1 + a2X2 = 0.5 exp(-(a0 + a1X1 + a2X2)) = 0 a0 + a1X1 + a2X2 = 0 O exp(-(a0 + a1X1 + a2X2)) = 0.5An INFO 5880 student is interested in predicting the city miles per gallon (MPG) rating of vehicles. He eventually came up with a regression model for predicting MPG based on horsepower based on a sample of 110 compact cars. Minitab regression output for this model is shown below. Use this output to answer the questions that follow. City MPG = 30.74 – 0.04162 Horsepower One of the vehicles in the sample has 255 horsepower and is rated at 17 MPG. For this vehicle, the residual is _____________Write a python programming code for logistic regression and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.
- Write a r programming code for logistic regression and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.The output below regresses Life Expectancy in a country on Log(Average Income). The equation for the regression is given by: Life Expectancy; = ßo + B₁ Log(Average Income); + ɛi What is the predicted life expectancy for a country with average income equal to $40,000? Note: Log(Averagelncome) is the natural logarithm of average income, not log base 10. reg life expectancy log_income Source SS df MS Number of obs = F(1, 184) 186 = 386.30 Model Residual 5714.78808 2722.03399 184 1 5714.78808 14.793663 Prob > F = 0.0000 R-squared = 0.6774 Total 8436.82208 185 45.6044436 Adj R-squared = Root MSE 0.6756 = 3.8463 life expec~y Coefficient Std. err. t P>|t| [95% conf. interval] log_income _ cons 4.726684 .2404885 28.68847 2.244139 19.65 0.000 12.78 0.000 4.252214 24.26092 5.201153 33.11602In Python, write a function that does least squares regression. The function should take input of an X and Y data set. The output should be a dict in Python with the 1) best fit values for the intercept 2) the best-fit value for the slope, 3) the sum-squared error, 4) the residuals, and 5) the p-value for the two-sided hypthesis test of the slope being zero. Each component of the dict should be labeled. Test the function with simulated data.
- ASSIGNMENT The Injury Severity Score (ISS) is a medical score to assess trauma severity. Data on ISS and hospital stay (in days) have been collected from a number of patients who were admitted to hospital after accidents. The ISS data are [64 35 50 46 59 41 27 39 66], and the length of stay data are [8 2 55 4 3 1 4 6]. Use MATLAB to plot the relationship between ISS and hospital stay.AWrite an order to generate binomial data with the number of observations (n) being 50, the number of trials (size) being 5 and the probability of success being 0.5, then storing the results in the binomial data matrix, with the number of columns being 5.sb.countplot(data = cancer, x = "Diagnosis") --NORMAL-- count 350 300 250 200 150 100 50 0 M Diagnosis B Based on the above plot, comment on location, spread, and skew. If the plot does not contain information on more or more of these features, explain why. Give a brief explanation of what we have learned from this plot.