Q1. For the giving dataset (X) below, calculate the following X= (455.2, 676.7, 784.7, 349.1, 437.5, 678.3, 760.2, 788.4, 278.3 and 455.2} 1) The Range ( 2) The Mode 1 3) The variance (
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- Check if these are true or false: 6. Equal width binning is suboptimal in case of data outlier’s presence. 7. Pearson correlation coefficient can be any value between -1 and 1 only. 8. Zscore normalization fits input values between 0 and 1 using the min and max values. 9. In histogram analysis using equal frequency, we divide data into buckets that have the same depth. 10. Equal width binning partitions data into buckets that have the same number of samples.Python! x = np.linspace(0,1,21)y = 2*xnp.random.seed(seed = 1)noise = np.random.normal(0, 0.1, x.size)z = y + noise 1.1Create a plot of these two data sets (?y and ?z) by making two sub-plots with separated markers. Remember to add proper axes labels and titles.\ 1.2 In this question, you should use scipy.optimize.curve_fit function for these two data sets. Please fit linear functions to both of pure data and noisy data, and plot your predicted functions with the original data sets in sub-plots. Remember to add legends.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…
- This is a Machine Learning question : Given a dataset, (1,+), (7, - ), (2, +), (6, -), (5, +), (9, -), (11, +) You are supposed to find a threshold function that minimizes the error in the given dataset. Threshold functions look like this: f( x | a,b ) = sign(a-x).b where a is a real number and b is in {1,-1}. How many possible values should you consider to solve this problem? What is the value of a and b in the case that minimizes the error? What is the minimum error? Show how you found it.This is a Machine Learning question : Given a dataset, (1,+), (7, - ), (2, +), (6, -), (5, +), (9, -), (11, +) You are supposed to find a threshold function that minimizes the error in the given dataset. Threshold functions look like this: f( x | a,b ) = sign(a-x).b where a is a real number and b is in {1,-1}. How many possible values should you consider to solve this problem? What is the value of a and b in the case that minimizes the error? What is the minimum error? Show how you found it. Note: Please first sort the dataset, and use the mid-point between two adjacent points as the threshold value to identify the misclassified points. Explain in detail how you have identified the misclassified points using the threshold value and the values of b.Which of the following correctly represents the coefficient of determination in terms of the variance that is an output from the analysis of variance table?
- Consider the following Confusion Matrix: Actual (down) \ Predicted (across) Yes No Totals Yes 20 5 No 10 15 Totals Based on the given time to event data, what is the value of the cumulative distribution function at t=6 if there are 10 right-censored observations?Q: Suppose a dataset has 8500 email collection. Among 8500 emails, 4000 emails are not-spam and remaining are spam emails. The word “dating” is used as a feature, whose frequency/count in spam emails are 310 and 106 in not-spam emails. You have to compute two probabilities using bayes theorem, only knowing it contains the word “dating”. First: Probability of an email being spam? Second: Probability of an email being not spam? Course/Subject: Introduction to Data Science.Given a data frame house.price with two columns: size for the number of square feets, and price for the price in thousand dollars. Write a R script to predict the prices for houses of 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 square feet, using a simple linear regression.
- In Big Data, suppose we wanted to retrieve records of shifts worked on a weekend in 2020 or at all during 2021. Which of the following WHERE conditions would be correct? 1 WHERE s.dayofweek IN ('SA', 'SU') AND (DATE_FORMAT(es.wdate,'%Y') = '2020' OR DATE_FORMAT(es.wdate,'%Y') = '2021' ) 2 WHERE es.wdate IN ('2020', '2021') AND s.dayofweek IN ('SA', 'SU') 3 WHERE (s.dayofweek IN ('SA', 'SU') AND DATE_FORMAT(es.wdate,'%Y') = '2020') OR es.wdate LIKE '2021%' 4 WHERE s.dayofweek = SA OR SU AND DATE_FORMAT(es.wdate,'%Y') = '2020' OR DATE_FORMAT(es.wdate,'%Y') = '2021'when a data set is unimodal, and the IQR is equal to the median that both equal to 50. However, median is equal to 100. Please explain what is the distribution?How can seasonality be analyzed per row? on a massive dataset with many observations? My code below does not work for large datasets (just on smaller ones). # Function to detect seasonal patterns using ACF for a single row detect_seasonal_acf <- function(row) { acf_result <- acf(row, lag.max = 12) return(acf_result$acf) } # Apply the function row-wise seasonal_acf_results <- apply(data[, -1], 1, detect_seasonal_acf) # Check for significant peaks (e.g., peaks above a certain threshold) significant_peaks <- sapply(seasonal_acf_results, function(acf_values) { max(acf_values) > 2/sqrt(length(acf_values)) }) # The 'significant_peaks' vector will indicate if there's a seasonal pattern for each keyword subset_data$Seasonal_Pattern_ACF <- significant_peaks