Create Second Image Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model. ]: # Plot Curve Fit from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21, 21,1000)| y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1) PolyReg LinearRegression() PolyReg.fit(x_fit, y_fit) mixData = pd.DataFrame({'x_fit': [x_fit], 'y_fit': [y_fit]}) z_fit = PolyReg. predict (mixData["x_fit"][0]) plt.scatter(x_fit, y_fit) plt.plot(x fit 7 fit C='vellow')

Create Second Image Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model. ]: # Plot Curve Fit from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21, 21,1000)| y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1) PolyReg LinearRegression() PolyReg.fit(x_fit, y_fit) mixData = pd.DataFrame({'x_fit': [x_fit], 'y_fit': [y_fit]}) z_fit = PolyReg. predict (mixData["x_fit"][0]) plt.scatter(x_fit, y_fit) plt.plot(x fit 7 fit C='vellow')

COMPREHENSIVE MICROSOFT OFFICE 365 EXCE

1st Edition

ISBN:9780357392676

Author:FREUND, Steven

Publisher:FREUND, Steven

Chapter8: Working With Trendlines, Pivottables, Pivotcharts, And Slicers

Section: Chapter Questions

Problem 8EYW

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#INSTRUCTIONS#Type the codes for each question#Include answers to ALL questions in the script as a comment (with a #).#Questions with a * next to it will ALSO require you to input the answer in Canvas.#Lastly, upload this R script AND input the corresponding answers to Canvas.#If you are missing codes or answers in this script, points will be deducted. install.packages("wooldridge") library(foreign)library(wooldridge) data(wage1)attach(wage1)View(wage1) # *14. Using "wage1" Run a multiple regression predicting wage, name it wageModel# Add the variables educ, exper, and tenure# What are the coefficients, what do they mean? Are they significant? What is Adjusted R2? # Findings and explanations# educ: # exper: # tenure: # This means: # The adjusted R-Squared:# Significance:
plz run in r studio n answer all the questions biostats........ Worksheet T tests, Wilcoxon’s test, Mc Nemar’s TestAnswer the following questions using R studio and submit the MSword format from via Moodle.1. Test whether the marginal probabilities are the same or differentfor the following dataset. Create the dataset by using the followingScripts:> set.seed(50)>data <- data.frame(before = sample(c("Positive", "Positive", "Positive", "Positive", "Negative"), 250, replace = TRUE), after = sample(c("Positive", "Positive", "Positive", "Positive", "Negative"), 250, replace = TRUE))>dataNote: Prepare a contingency table using the table function, beforechoosing and running your analysis.2. The following data were recorded as independent observationsfor heights (cm) of Guava Plants treated with NPK nutrients. Wewant to know, if the average height of the these plants statisticallydiffers from 19 cm?18.50, 18.55, 18.60, 18.65, 18.70, 18.75, 18.80, 18.85, 18.90,18.95, 19.00,…
You will use the wine for this task (dataset: sklearn.datasets.load_wine()), perform NN and NearestCentroid on the dataset to perform classification. Select different parameter values (i.e., parameter tuning) and discuss the influence. Finally, report result of comparisons. The dataset can be found here https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html
Use the iloc() function to extract the first 20 features of the dataframe har_train. Save this new dataframe to first_twenty. Next, using the seaborn library create a heatmap for the correlation matrix. First you have to create the correlation matrix from the pandas dataframe (save it in a dataframe called corr) and then plot it using seaborn with these customizations: Set the seaborn style to white. Generate a mask using np.triu(np.ones_like()) with the dtype as boolean to only show the lower triangle of the correlation matrix. Save it in a variable called mask. Set up the figure with matplotlib with figsize=(11,9). Use fig, ax = ... Generate a custom diverging colormap for the heatmap with the arguments (220, 10, as_cmap=True). Save it in a variable called cmap. Draw the heatmap with the mask and correct aspect ratio, using the arguments corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5}). Finally, use fig.tight_layout() just before…
Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with linear kernel breast cancer problem : I have already answered 1 to 3. Please provide solution from 4,5,6,7. Programming language python
plz help me fix the following trigger problem is in picture CREATE OR REPLACE TRIGGER MEM_BALANCE AFTER UPDATE ON LATE_FEE FOR EACH ROW BEGIN UPDATE DETAILRENTAL SET LATE_FEE = CASE WHEN LATE_FEE = (DETAIL_DUEDATE – DETAIL_RETURNDATE) * DAILY_LATE_FEE WHEN LATE_FEE = (DETAIL_DUEDATE – DETAIL_RETURNDATE) * DAILY_LATE_FEE IS NULL THEN LATE_FEE := 0 WHEN UPDATED_LATE_FEE:= ORIGINAL_LATE_FEE – LATE_FEE WHEN UPDATED_LATE_FEE:= ORIGINAL_LATE_FEE – LATE_FEE IS NOT NULL THEN MEM_BALANCE:= TOTAL_BALNCE – UPDATED_LATE_FEE ELSE NULL END; the database im trying to put the trigger in CREATE TABLE MEMBERSHIP( MEM_NUM CHAR(3) CONSTRAINT MEMBER_MEM_NUM_PK PRIMARY KEY,MEM_FNAME VARCHAR(30) NOT NULL,MEM_LNAME VARCHAR(30) NOT NULL,MEM_STREET VARCHAR(15),MEM_CITY VARCHAR(10),MEM_STATE CHAR(2),MEM_ZIP CHAR(5),MEM_BALANCE NUMBER (2)); ALTER TABLE MEMBERSHIPMODIFY MEM_STREET VARCHAR(25);CREATE TABLE RENTAL( RENT_NUM CHAR(4) CONSTRAINT RENTAL_RENT_NUM_PK PRIMARY KEY,RENT_DATE DATE,MEM_NUM CHAR(3),CONSTRAINT…
nycflights13::flights Q4<- flights %>%filter(carrier == "JFK") %>%summarise(average_dist = mean(distance)%>%summarise(max_dist = max(average_dist))%>%group_by(Q4, month, day)%>%head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) *not pictured, I did upload, tidy verse, dplyr, and the nycflights13 libraries. Error: Incomplete expression: Q4<- flights %>% filter(carrier == "JFK") %>% summarise(average_dist = mean(distance)%>% summarise(max_dist = max(average_dist))%>% group_by(Q4, month, day)%>% head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) RStudio Question: I can't figure out what is wrong with my code. Could someone take a look at it? See below for what I am trying to do, my code and my error output. I am trying to find what 5 days of the year had the highest mean distance from JFK airport, using the nycflights13 library. I want to format it in month, day, andvmean distance.
Let the document be “good bad student good bad instructor” and the query be “good student”. Fill out the empty columns in the following table and then compute the cosine similarity between the document vector and the query vector. In the table, df denotes document frequency, idf denotes inverse document frequency (i.e., idft = log10N/dft), tf denotes term frequency, log tf denotes the tf weight based on log-frequency weighting as shown in slides (i.e., 1+log10tft,d for tft,d> 0 and 0 otherwise), d is the document vector, d’ is the length-normalized d, q is the query vector, and q’ is the length-normalized q. Assume N (number of documents in the corpus) = 10,000,000.
In BLAST search, the Expected value is the number of nucleotide M in the query sequence multiply by the number of nucleotides N in total in the nucleotides sequence database and consecutively multiplied by the p value for the random match distribution. The Expected value means the number of random matches you may encounter in a database search as you do. A query sequence is 96 nucleotide long is used to search a nucleotide database that contained 5 X 10 ^7 nucleotides (that is: 5 time 10 to the power of 7), and the p value to find an exact match is 2 X 10 ^ (-9). What is the expected value? (precision to 0.00001)
Consider the below given iris data set. This dataset contains 3 classes of 15 instances each and each class refers to a type of iris plant. The dataset has two features: sepal length, sepal width. The third column is for species, which holds the value for these types of plants. A new plant is identified. You have to classify the Species class of new identified plant with the help of KNN algorithm. Note: Before applying KNN modify the given data by adding last two digits of your registration number. Such as if the last two digits of your registration number is 23 then first row in the given table will be 28.3and 26.3.
Automated Title: In eq_world_map_3.py, we specified the title manually when defining my_layout, which means we have to remember to update the title every time the source file changes. Instead, you can use the title for the data set in the metadata part of the JSON file. Pull this value, assign it to a variable, and use this for the title of the map when you’re defining my_layout.
To run any stats you must first import statistics True False 2. For relatively low-level stats, it generally doesn't matter if you run them in statsmodels, the statistics library, or the other ways we saw True False 3. By default, scikit-learn and statsmodels create equivalent logistic regression models True False 4. which of these might you want to add to the default statmodels regression model? Constant Multiple comparisons Scatterplot 5.Which of the following is how we would create a logistic regression model? sm.OLS sm.Logit() sm.LogReg() sm.Logistic() 6.Which of the following are common ways of installing packages? conda pip install.py install.packages()…