You are working as a data scientists and you have received data on house prices in the Boston region. The data set contains the following variables: • crim: per capita crime rate by town • zn: proportion of residential land zoned for lots over 25,000 sq.ft. • indus: proportion of non-retail business acres per town • chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • nox: nitric oxides concentration • rm: average number of rooms per dwelling •age: proportion of owner-occupied units built prior to 1940 • dis: weighted distances to five Boston employment centers • rad: index of accessibility to radial highways • tax: full-value property-tax rate per $10,000 • ptratio: pupil-teacher ratio by town • b: 1000(Bk – 0.63)2 where Bk is the proportion of blacks by town • Istat: % lower status of the population • medv: Median value of owner-occupied homes in $1000s Given this information: 1. Download the dataset boston.csv and open it as a PANDAS dataframe. 2. Using 'medv' as the response variable and per capita crime rate by town, proportion of owner-occupied units built prior to 1940, and nitric oxides concentration as predictors, fit a linear model (OLS), and a k-nearest neigherbour model (using the 5 nearest neighbour). Which one has better prediction properties using k-fold cross validation (k=5)? Explain why. 3. Fit a model to predict the house prices using crim, zn, indus, chas,nox,rm, age, dis, rad, tax,ptratio, b, and Istat, using OLS, Ridge, and Lasso. Show the coefficients. Use lambda equal .1 to both Ridge and Lasso. What variable(s) can be eliminated from the analysis based on the Lasso results?

Use a data set of property value from 2007 - 2019.  The data contains sale price for houses and units with 1, 2, 3, 4, and 5 bedroom. The data is: date of sale; price in dollars; property type (unit or house); number of bedrooms Using R, calculate and show the mean price of the house properties and unit properties.  ```{r} ```

The InstantRide Driver Relationship team wants to learn how many travels each driver has done in the month of October. You need to send them the DRIVER_ID, and two calculated columns: DAY and RIDES. The DAY column is calculated using the DAY() function with the TRAVEL_START_TIME as the argument. The RIDES column is calculated by using the COUNT() function to determine the number of rides given for each day. Filter the results with the MONTH function.

F A E a. b. C. B d. C 8 00 7- 6 5 What do the letters in the box plot above represent? 3- 2 1- D A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers A = Median, B = Third Quartile, C = Mean. D = Inter Quartile Range. E =

A plan view sketch of a benchmark leveling run is shown below. Along each line representing a sight is the value of the rod reading for that sight. The numbering of TPs shows the direction of the level run. Place the data in the form of field notes. Assuming that the average length of each BS and FS is 125 ft, determine the order of leveling (accuracy) of the survey BM 14 48.292 7.485 不 10.260 T 2.641 1.485 TP 1 TP 3 3.982 π 2.247 8.643 不 TP 2 8.636 9.829 T 7.642 BM