Suppose that we have a sample data for four people and the data has four attributes. The data is given in the following table.   Name Test-1 (nominal) Test-2 (nominal) Test-3 (nominal) Jack Code A M S Joe Code B M Y Jimmy Code C N M Jill Code A F Y Jin Code B F M Use the three nominal attributes to calculate the distance between Joe and Jill.

Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE Model 2 Model 3 Model 5 Model 4 Model 1 1 <0.000001 0.955978 0.792802 18.277907 1 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 4 2 <0.000001 0.928791 0.759583 17.253086 3 <0.000001 <0.000001 0.928295 0.855568 0.761951 0.652212 17.234646 14.639962 Test Data…

a player plays in a single team in a given season. however, the player may change the teams from a season to the next. for a player we keep the name (we want to search based on the last name), the date of birth, the jersey number (which can change when the player changes the team). a coach is hired by a club in a given season. a coach coaches one or more teams per seasons, but at most 3. organize the persons associated with the league in a hierarchy. I need the ERD Model and the relationships ?

The Python code for step 7 is needed. Step 6 has been attached for data reference

A data race is defined as follows.

Least Squares Regression 1. In MATLAB Write a function to calculate the slope and intercept for a best-fit line using the least-squares linear regression method and to calculate the R2 value. Your inputs should be arrays of X- and Y-values. Your outputs should be the slope and intercept and the R2 value. Section

PEAR:  sort the data according to the employees salary (the last column). Use the Schwartzian  transform method.

Please label each part!!

Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order.You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2)) print(mod.pvalues.round(2)) print('The smallest p-value is for sqft') Please use sm.add_constant(X)! In Jupyter Notebook please! Please use the sys module!