Zscore - Study guides, Class notes & Summaries

5 number summary and box plots - maximum Q3 median Q1 minimum outliers not based on zscore but IQR range (more than 1.5 IQR below Q1)*** A data set consists of all businesses in Tempe that accept Discover Card branded credit cards. The variables in the data set are the total amount of purchases using Discover Card credit cards and how many purchases were made using the card and all the data are for the year 2008. T/F: The data in this data set constitute a cross section, i.e., they are ...

Kine 303 Exam Questions with Correct Answers For variables X and Y, you calculate Pearson r to be .6. How much variance in Y is accounted for by X? - Answer-36% A calculated Pearson r is NOT statistically significant when - Answer-it is less than the critical r value If someone's score on an independent variable is zero, what will be that person's predicted score on Y (YP)? - Answer-the Y-intercept If there is a high negative relationship (e.g., r = -.95) between weekly exercise dur...

What does dpmo stand for and what is the formula? Ans: defects per million opportunities (number of defects discovered/opportunities for error) x 1000000 What does DPU stand for and what is the formula? Ans: Defects per Unit number of defects discovered/number of units produced Dpmo provides a more comprehensive measure of what? Ans: Potential failures How does Six Sigma help the organization? Ans: By improving effectiveness and efficiency What is the difference between Six Sigma and t...

Which of the following alternative hypotheses makes a hypothesis test two-tailed? Select one. Question options: Parameter is not equal to a value Parameter is greater than a value Parameter is less than a value - Parameter is not equal to a value Which of the following methods from Python's submodule is used to calculate a confidence interval based on the Normal Distribution? Select one. Note: st is from the import command import as st Question options: l dence_interval dence_inter...

Kine 303 Exam Questions with Correct A For variables X and Y, you calculate Pearson r to be .6. How much variance in Y is accounted for by X? - Answer-36% A calculated Pearson r is NOT statistically significant when - Answer-it is less than the critical r value If someone's score on an independent variable is zero, what will be that person's predicted score on Y (YP)? - Answer-the Y-intercept If there is a high negative relationship (e.g., r = -.95) between weekly exercise duration...

COB 191 Exam 1 (2023/2024) Already Graded A data values assigned to measurements primary data adv: collected and orgainzed by user dis: expensive and time consuming secondary data adv: readily available dis: no control over organization and accuracy nominal data -arbitrary labels -no ranking -name/label ordinal data -ranking exists -no measurable meaning to numerical differences -order/choices interval data -meaningful differences -no true value of zero -gives orders of values AND ability t...

6-3 Homework Help MAT 133 | Assignment Solutions 2023. Joan’s finishing time for the Bolder Boulder 10K race was 1.75 standard deviations faster than the women’s average for her age group. There were 405 women who ran in her age group. Assuming a normal distribution, how many women ran faster than Joan? We need to figure out what percent (technically, proportion) of women had a shorter run time. Then we multiply that by 405 to figure out the number of women that were faster. If Joan...

MATH MAT133 6-3 Homework Help Assignment Solutions. Homework Problem 3 Let's look at this textbook problem: 7.21 Joan’s finishing time for the Bolder Boulder 10K race was 1.75 standard deviations faster than the women’s average for her age group. There were 405 women who ran in her age group. Assuming a normal distribution, how many women ran faster than Joan? We need to figure out what percent (technically, proportion) of women had a shorter run time. Then we multiply that by 405 t...

In [1]: import pandas as pd import numpy as np from sklearn import preprocessing from _selection import train_test_split from _bayes import GaussianNB from cs import accuracy_score import seaborn as sns import t as plt from import zscore import warnings rwarnings( "ignore") from r_model import LinearRegression from er import KMeans from cs import mean_squared_error from ers_influence import variance_inflation_fac tor import math from r_model import LogisticRegression from sk...

import pandas as pd import numpy as np from sklearn import preprocessing from _selection import train_test_split from _bayes import GaussianNB from cs import accuracy_score import seaborn as sns import t as plt from import zscore import warnings rwarnings( "ignore") from r_model import LinearRegression from er import KMeans from cs import mean_squared_error from ers_influence import variance_inflation_fac tor import math from r_model import LogisticRegression from sklearn im...