Which of the following statements is TRUE regarding the relationship between the MAD and MAPE values in a dataset? Multiple Choice O The MAPE will never increase if the MAD increases. The MAPE will always increase if the MAD increases. There is no firm relationship between the direction of increases or decreases in MAD versus MAPE values

Problems 1 and 2 are based on dataset eBooks.zip PROBLEM 1Select frequent words (whose count is equal or greater than 50,000).Display the frequent words in descending order.

using R please show the code needed for each step   1) Initial data overview a. Load the faithful dataset in R b. What are the column headers for this data set? c. How many rows of data are in the data set? 2) Summary stats for the full data set a. Compute all of the following for the duration of the eruptions and the waiting time between the eruptions i. mean ii. population variance iii. population standard deviation iv. population coefficient of variation 3)Sampling a. Create a new data frame that contains 100 samples of size 10 from the eruption duration column of the faithful data set i. You can use the sample() function to create your samples of size 10 ii. You can use the replicate() function to repeat the sampling 100 times iii. You can cast the result as a data frame using data.frame() 4) Analyze the Samples a. Create 3 new empty vectors – these will store the sample mean, sample variance, and sample standard deviation of each of your 100 samples b. For each…

The DBA denormalized certain data in the TAL Distributors database to improve speed, and one of the resultant tables looks like this: It has been determined that the Customer table is not in third normal form due to one or more of the following fields: CustomerNum, CustomerName, Street, City, State, PostalCode, Balance, CreditLimit, RepNum, or RepName. In this scenario, what is the normal form of the denormalized table?

Which activation function is appropriate to use in the final layer of a regression ANN? Group of answer choices softmax No activation sigmoind Tanh

Any idea where my code is messing up?The taxis dataset contains information on taxi journeys during March 2019 in New York City. The data includes time, number of passengers, distance, taxi color, payment method, and trip locations. Use sklearn's cross_validate() function to fit a linear regression model and a k-nearest neighbors regression model with 10-fold cross-validation. Create dataframe X with the feature distance. Create dataframe y with the feature fare. Split the data into 80% training, 10% validation and 10% testing sets, with random_state = 42. Initialize a linear regression model. Initialize a k-nearest neighbors regression model with k = 3. Define a set of 10 cross-validation folds with random_state=42. Fit the models with cross-validation to the training data, using the default performance metric. For each model, print the test score for each fold, as well as the mean and standard deviation for the model. Ex: If the file taxis_small.csv is used, the output is:…

The table shown below lists sample dentist/patient appointment data. A patient is given an appointment at a specific time and date with a dentist located at a particular surgery. On each day of patient appointments, a dentist is allocated to a specific surgery for that day.a. The table is susceptible to update anomalies. Provide examples of insertion, deletion and update anomalies.b. State why the above table is in 1NF.c. Identify all candidate key(s) and your choice of primary key.d. Identify the functional dependencies in the 1NF table above based on the chosen primary key, including any partial and transitive dependencies. (If necessary, clearly state your assumptions). In answering this, you may use the format:fd#: attribute(s) on the left-hand side)  (attribute(s) on the right-hand side (state whether full, partial or transitive dependency)example: fd1: (A1, A2)  A3, A4, A5, A6 (full dependency)e. After removing partial dependencies, show all tables, with data, that are in 2NF.…

For this question, create a new column in the dataset where total rainfall is just the sum of our three separate rainfall variables. (a) plot crop yield vs. time. Does yield appear to be stationary? Why or why not? (b) plot total rainfall vs. time. Does total rainfall appear to be stationary? Why or why not? (c) plot the first-difference of crop yield vs. time. Does this series appear to be stationary? Why or why not? (d) formally test whether crop yield, rainfall and the first-difference of crop yield are stationary using the appropriate test. Be sure to do all parts of the hypothesis tests. After these tests, what can you say the order of integration is for each of the variables?(e) estimate a model where yield is a function of rainfall and time. You do not have to worry about the time variable being stationary or not, but the other two must be stationary (you might need to difference one or both of them to make it stationary). Fully report your results.(f) test your model for…

The following dataset is a historic record of 14 houses that were sold in a small town in BC. The dataset is used to predict whether a new house in the same town will be sold in 10 days if listed with a specific price based on certain attributes. We are considering only four attributes (price, number of bedrooms, size, and distance to bus stop) just to simplify the calculations in this assignment but more attributes should be considered in real applications. House Price Number of Bedrooms Size (sqft) Distance to Bus-Stop House sold in 10 days? House 1 $300,000 1 3,500 sqft far No House 2 $300,000 1 3,500 sqft near No House 3 $250,000 1 3,500 sqft far Yes House 4 $350,000 2 3,500 sqft far Yes House 5 $350,000 3 5,000 sqft far Yes House 6 $350,000 3 5,000 sqft near No House 7 $250,000 3 5,000 sqft near Yes House 8 $300,000 2 3,500 sqft far No House 9…