With given mean and standard deviation we can simulate values for that normal distribution using the excel formula NORMINV(Rand(),mean,sd). This is applicable for X, Y & Z and thus the W value can also be simulated ( W = (X+Y)/Z) The simulation of 50 values are prepared and the histogram with width 3 class interval is shown in the excel sheet. Random Values around the mean and Standard deviation Y Y W Mean 1.. Y.. 0. I Deviation Variance Frequency of Simulated Values Bins Frequency

With given mean and standard deviation we can simulate values for that normal distribution using the excel formula NORMINV(Rand(),mean,sd). This is applicable for X, Y & Z and thus the W value can also be simulated ( W = (X+Y)/Z) The simulation of 50 values are prepared and the histogram with width 3 class interval is shown in the excel sheet. Random Values around the mean and Standard deviation Y Y W Mean 1.. Y.. 0. I Deviation Variance Frequency of Simulated Values Bins Frequency

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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question 6
Let us consider the data of marks scored in unit tests held in school. For each unit test, the marks scored by all students of the class is recorded. Maximum marks are 25 in each subject. The subjects are Maths, Science. Social Studies (S.St.), Hindi, and English. For simplicity, we assume there are 4 students in the class and the table below shows their marks in Unit Test 1, Unit Test 2 and Unit Test 3. Table 3.1 shows this data. Name/ Unit Subjects Test Raman 1 Raman 2 Raman 3 Zuhaire 1 Zuhaire 2 Zuhaire 3 Aashravy 1 Aashravy 2 Aashravy 3 Mishti 1 Mishti 2 Mishti 3 Table 3.1 Case Study Result Maths Science S.St. Hindi 22 21 14 20 23 22 23 24 12 15 587 18 17 21 20 19 17 15 18 19 22 25 22 21 18 18 17 15 22 21 19 20 24 19 25 25 20 20 22 24 24 25 23 15 17 21 22 24 25 Eng 21 24 23 19 15 13 22 21 23 22 23 20 Let us store the data in a DataFrame, as shown in Program 3.1: Program 3-1 Store the Result data in a DataFrame called marksUT.
Write a function in Python to perform a two sample t-test. The t-test should be unpaired and you may assume that the samples have equal variance. The function should have: specify a choice of alternative hypotheses by giving arguments “two-sided”, “less”, or “greater”. If no such alternative is given, then the default should be “two-sided” specify a parameter mu indicating the true difference in means under the null hypothesis. If unspecified, the default should be mu=0. result should be a dict with the labeled values of the t-statistic and the p-value function should be my_ttest(x,y,mu,alternative), where x and y are the two samples being compared. use the function scipy.stats.t.cdf to calculate p-values generate random samples for testing using npr.normal
"MPT star" is a program for calculating the annual municipal property taxes, based on the neighborhood, the type of property (house, store, apartment, etc.), the size of the property, the discounts to which the owner is entitled (pensioners, low income large family, single-parent family, etc.) Suggest a framework for stratified sampling test cases from the citizens' file. List your assumptions about the population's distribution.
Sum of Squared Errors: Remember from your statistics courses that if two random variables X and Y are related by a relation YaX+b and you had a set of observations {(1, 1), (2, 2)..... (z.)). then for every estimated values of a and b, the Sum of Squared Errors (SSE) was defined as in the following formula. SSE(-ar-b)². 1-1 The less SSE, the better a and b are estimated. Consider the following fixed list. In this list each sub-list of length two is standing for one pair (z.). Write a function that recieves two numbers a and b and returns the associated SSE. L- [[1, 2], [1.1, 2], [2, 7.1), (2.5, 7.21, (3, 11]]
In R, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to near…
Using the Gaussian Probability Density function write a function that will calculate the probability that variable x will be in the range µ − ασ < x < µ + ασ, where α is some multiple of the variance.
Size of sample space A six-sided dice is rolled, a five-sided dice is rolled and a three-sided dice is rolled. Considering this as a probability experiment, what is the size of the sample space? An Event In the context of the probability experiment just described (a 6-sided, a 5-sided, and a 3-sided dice thrown): Consider the event in which two face- up numbers on the three dice add to 10. What is the size of this set? Save Reset
An INFO 5880 student is interested in predicting the city miles per gallon (MPG) rating of vehicles. He eventually came up with a regression model for predicting MPG based on horsepower based on a sample of 110 compact cars. Minitab regression output for this model is shown below. Use this output to answer the questions that follow. City MPG = 30.74 – 0.04162 Horsepower One of the vehicles in the sample has 255 horsepower and is rated at 17 MPG. For this vehicle, the residual is _____________
Just answer LETTERS D,E,F dont forget to answer this part too!! ?x = ? =
You build a model predicting blood pressure as a function of three variables: weight (numeric) age (numeric) income (categorical: low, medium, high) You first specify your model as: blood pressure ~ age * income + weight How many parameters (k) does your model have? (Remember, we do not count the grand mean in k) You change the above model specification to be: blood pressure ~ age * income + weight * income How many parameters does your model have now? You change your model to include the three-way interaction (which, remember, includes all two-way interactions and main effects, too!) Your model now looks like this: blood pressure ~ age * income * weight How many parameters does your model have now?
So I have the following roshambo strategy function to return a random play, and tests for when one of the length of plays is less than 3 and otherwise. I have tried running it many times on a single scenario to estimate the probability for each return but the results are somehow confusing. Question: Write a series for the roshambo function below to test that it works as expected. (this could be testthat::expect_equal or testthat::expect_true or testthat::expect_error tests). Thank you. roshambo <- function(plays){ # return random play if (length(plays) < 3){ return(sample(c("R", "P", "S"), 1)) } # else find most common most_common <- names(which.max(table(plays))) if (most_common == "S"){ return("R") } else if (most_common == "R"){ return("P") } else { return("S") }}