In Problems 87 - 90 , several experiments are simulated using the random number feature on a graphing calculator. For example, the roll of a fair die can be simulated by selecting a random integer from, and 50 rolls of a fair die by selecting 50 random integers from 1 to 6 (see Fig. A for Problem 87 and your user's manual). Use a graphing calculator to simulate 200 tosses of a nickel and dime, representing the outcomes HH, HT, TH, TT by 1, 2, 3, and 4 , respectively. (A) Find the empirical probabilities of the four outcomes. (B) What is the probability of each outcome under the equally likely assumption?
In Problems 87 - 90 , several experiments are simulated using the random number feature on a graphing calculator. For example, the roll of a fair die can be simulated by selecting a random integer from, and 50 rolls of a fair die by selecting 50 random integers from 1 to 6 (see Fig. A for Problem 87 and your user's manual). Use a graphing calculator to simulate 200 tosses of a nickel and dime, representing the outcomes HH, HT, TH, TT by 1, 2, 3, and 4 , respectively. (A) Find the empirical probabilities of the four outcomes. (B) What is the probability of each outcome under the equally likely assumption?
Solution Summary: The author explains how to determine empirical probability using graphing utility for 200 tosses of a nickel and dime representing the outcomes.
In Problems
87
-
90
, several experiments are simulated using the random number feature on a graphing calculator. For example, the roll of a fair die can be simulated by selecting a random integer from, and
50
rolls of a fair die by selecting
50
random integers from
1
to
6
(see Fig. A for Problem
87
and your user's manual).
Use a graphing calculator to simulate
200
tosses of a nickel and dime, representing the outcomes
HH,
HT,
TH,
TT
by
1,
2,
3,
and
4
, respectively.
(A) Find the empirical probabilities of the four outcomes.
(B) What is the probability of each outcome under the equally likely assumption?
3. (i) Below is the R code for performing a X2 test on a 2×3 matrix of categorical
variables called TestMatrix:
chisq.test(Test Matrix)
(a) Assuming we have a significant result for this procedure, provide the R
code (including any required packages) for an appropriate post hoc test.
(b) If we were to apply this technique to a 2 × 2 case, how would we adapt
the code in order to perform the correct test?
(ii) What procedure can we use if we want to test for association when we
have ordinal variables? What code do we use in R to do this? What package
does this command belong to?
(iii) The following code contains the initial steps for a scenario where we are
looking to investigate the relationship between age and whether someone owns
a car by using frequencies. There are two issues with the code - please state
these.
Row3<-c(75,15)
Row4<-c(50,-10)
MortgageMatrix<-matrix(c(Row1, Row4), byrow=T, nrow=2,
MortgageMatrix
dimnames=list(c("Yes", "No"), c("40 or older","<40")))…
Describe the situation in which Fisher’s exact test would be used?(ii) When do we use Yates’ continuity correction (with respect to contingencytables)?[2 Marks] 2. Investigate, checking the relevant assumptions, whether there is an associationbetween age group and home ownership based on the sample dataset for atown below:Home Owner: Yes NoUnder 40 39 12140 and over 181 59Calculate and evaluate the effect size.
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, subject and related others by exploring similar questions and additional content below.
Discrete Distributions: Binomial, Poisson and Hypergeometric | Statistics for Data Science; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=lHhyy4JMigg;License: Standard Youtube License