Imagine we have an individual-level dataset on voting in California. However, for our project we want to examine voting at the county level. In Stata, which command would we use transform our individual-level dataset into a county-level dataset?
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- Assume you are giving a dataset that is based on user surveys. You notice that many surveys have missing fields. You also notice that some surveys have fields on a scale of 1 to 5 while others are on a scale of 1 to 10. The survey has mixed information using metric measurements and the imperial system. There are some different fields depending on the country, but they all relate to the location of the participant. You also notice that some values just don’t make sense. What analysis would you do on the data set to come up with a data cleaning plan? What are some normalization and cleaning tasks you could do based on the information provided in this description?We created some models for a dataset and, for each model we computed its R2 score. The results are presented in the table below: Model m1 m2 m3 m4 m5 R2 0.85 0.76 0.87 0.68 0.79 What model should we use from the ones presented in the table? Justify your answer. Answer:Take the first 1000 entries of the training portion of the Scikit-learn 20 Newsgroups dataset (in the original order) as the training set, and set aside the next 100 entries as the test set. Predict whether a post belongs to a political discussion group using a bag-of-words model (category includes 'talk.politics'). Provide the accuracy on the test set, the input shape of the network, and the predictions of the network for the last two entries in the test set as "accuracy on the test set, network input shape, network predictions for the last two entries in the test set as 'politics' or 'non-politics'". Here's what I have so far: from sklearn.datasets import fetch_20newsgroupsfrom sklearn.feature_extraction.text import CountVectorizerimport numpy as npfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.optimizers import Adam categories = ['talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc']newsgroups =…
- Consider a dataset Name Mood Scale Jim 2 Ben 3 How do we redefine the values of mood scale to be 1=Very Sad, 2=Sad, 3=Neutral, 4=Happy, 5=Very Happy on RStudio? What are the steps and code for that?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…Take the first 1000 entries of the training part of the Scikit-learn newsgroups (20 newsgroups) dataset (in the original order) as the training set, and set aside the next 100 entries as the test set. Use the first network from the 5th practice (provided below), make necessary modifications based on the data. Predict whether a post belongs to a political discussion group using a bag-of-words model (the category contains 'talk.politics'). What is the accuracy on the test set, what is the input_shape of the network, and what does the network predict for the last two entries of the test set? here is the model that is provided: model = keras.Sequential() model.add(Dense(128,input_shape=(20000,))) model.add(Dense(64)) model.add(Dense(32)) model.add(Dropout(0.5)) model.add(Dense(4, activation='softmax')) how can i solve this?
- Set up a payroll authority. Create a new supplier record for the Receiver General as the payroll authority for Income Tax, El and CPP. Leave the remaining supplier record fields blank. Settings- Payroll: Remittance and suppliers - Receiver General for Canada Remitting frequency- Monthly End of remittance period - April 15, 2022. hOW TO DO THIS IN SAGE50?Create an ANN Network for Boston Housing price regression dataset from Keras. Compare three different networks with different activation functions, processing elements as simple, normal and complex and compare your results considering the results, MEA ,MSE and Epoch values. Submit your code and results in a PDF document. Do not send COLAB.This week, you learned about using CTEs (Common Table Expressions) to retrieve data. However, with the knowledge from CISS 202, you can still solve most of the problems in this chapter that use CTEs. Why is CTE so popular, even though it is only required for very few problems? In your answer, explain what a CTE is and provide your opinions on the benefits of using it.
- In the ETL process, one important step to process when transforming the data is to work with null, n/a, and zero values in the dataset. If you have a field of quantitative data (e.g., number of years each individual in the table has held a full-time job), what would be the effect of the following? a. Transforming null and n/a values into blanks . b. Transforming null and n/a values into zeroes c. Deleting records that have null and n/a values from your dataset (Hint: Think about the impact on different aggregate functions, such as COUNT and AVERAGE.)The Department of Motor Vehicle (DMV) wants to examine red-light violations in some counties.To do so, DMV obtains from those countries red-light citations and enters relevant data into adatabase. It plans to keep track of the driver (e.g., name, address, license number), red-lightcamera (e.g., location ID, address), and violation report (e.g., date & time, photo evidence, theofficer’s ID, the officer’s remark).o Draw an ERD (without attributes) using crow’s foot notation.o Provide logical database schema.• Provide a formal written description of each relationship between Automobile and Student.You must present an examination of "Population & Housing in San Diego County." The goal is to look for correlations between the shifting population and residential unit density in the county. How would you go about conducting this inquiry? Please specify which data files are needed and where they might be found (including any relevant references). Which data model will you use to conduct this investigation? (You do not need to finish the analysis but you may examine the data files on the web).