When a survey asked subjects whether they would be willing to accept cuts in their standard of living to protect the environment, 354 of 1180 subjects said yes. a. Find the point estimate of the proportion of the population who would answer yes. b. Find the margin of error for a 95% confidence interval. c. Construct the 95% confidence interval for the population proportion. What do the numbers in this interval represent? d. State and check the assumptions needed for the interval in (c) to be valid.
a. Find the point estimate of the proportion of the population who would answer yes. ModifyingAbove p with caretequals (Round to five decimal places as needed.)
b. Find the margin of error for a 95% confidence interval. (Round to five decimal places as needed.)
c. Construct the 95% confidence interval for the population proportion. (Round to five decimal places as needed.)
In: Math
Please answer using R code!
install.packages("wooldridge")
require("wooldridge")
d1 <- data("attend")
View(attend)
Q) Draw as scatter plot of stndfnl against priGPA. Customize your plot, such as give it a title, x-axis and y-axis labels, and you may put nice color to make it pretty. Run a simple regression of stndfnl on priGPA and save regression model as reg1. Are these variables related in the direction you expected? Interpret your estimated slope and intercept coefficient? Now run a multiple regression of stndfnl on priGPA and dummy variable frosh and soph. Interpret the coefficient of soph.
In: Math
Now that you have your data set for your final study mostly analyzed, you thought the data analysis was over, right? Not so fast. I want you to run one more analysis, and this is going to be a tough one. Rather than a 2 X 2 ANOVA, I want you to run a 2 X 2 X 2 ANOVA. That is, I want you to include participant gender as a third independent variable. The good news here is that the analysis is almost identical to the 2 X 2 ANOVAs you already did for Paper IV. With a 2 X 2 X 2 ANOVA, though, simply pull gender over as another “fixed factor” in your design and then run the univariate ANOVA. In your discussion, tell me what (if anything) was significant in the analysis. Think about this as an A X B X C design. There are three different independent variables, all with two levels, so there should be three main effects (one for A, one for B, and one for C). There should be three 2 X 2 interactions (AB, AC, and BC). There should be one possible three way interaction (ABC). All I need you to tell me is which (if any) main effects are significant, which (if any) two way interactions are significant), and if the three way interaction is significant. I don’t need to see the statistics, means, standard deviations, or simple effects tests: just tell me which tests are significant! Then try to interpret it for me by looking at the means. Did differences seem to emerge for the DVs? Work with your group on this interpretation.
In: Math
In country A, the vast majority (90%) of companies in the chemical industry are ISO 14001 certified. The ISO 14001 is an international standard for environmental management systems. An environmental group wished to estimate the percentage of country B's chemical companies that are ISO 14001 certified. Of the 550 chemical companies sampled, 374 are certified.
a) What proportion of the sample reported being certified?
b) Create a 95% confidence interval for the proportion of country B's chemical companies with ISO 14001 certification. (Be sure to check conditions.) Compare to the country A proportion.
In: Math
The USA Today reports that the average expenditure on Valentine's Day is $100.89. Do male and female consumers differ in the amounts they spend? The average expenditure in a sample survey of 48 male consumers was $135.67, and the average expenditure in a sample survey of 38 female consumers was $68.64. Based on past surveys, the standard deviation for male consumers is assumed to be $38, and the standard deviation for female consumers is assumed to be $18.
In: Math
Budget Sales
14.08 27.96
16.17 22.92
12.01 21.52
18.74 25.62
19.57 29.04
16.89 22.47
16.88 25.92
19.39 25.91
24.76 32.70
22.03 28.97
20.89 34.21
24.05 30.46
15.90 24.45
19.20 27.98
20.58 29.62
22.70 35.73
17.84 23.77
18.51 25.83
21.10 27.78
23.19 27.97
20.42 25.49
22.31 25.74
18.77 28.49
16.09 25.80
22.93 29.12
19.83 27.80
21.83 34.94
15.80 27.02
22.16 26.37
19.00 28.31
21.63 32.08
23.42 31.32
23.34 32.97
27.82 28.40
23.88 34.17
17.90 27.31
18.28 26.46
19.59 26.24
14.92 23.40
22.07 29.31
20.02 24.87
19.19 30.70
19.91 30.53
19.29 30.65
17.83 24.05
21.51 26.75
23.59 31.26
24.13 30.42
21.81 27.75
19.04 24.85
27.71 34.14
27.20 32.17
20.43 30.64
19.02 28.51
15.32 28.74
20.29 27.05
17.90 28.63
15.27 21.10
19.15 27.36
21.03 32.19
19.30 29.53
21.65 25.68
14.80 28.04
19.12 33.21
12.67 22.44
23.06 31.38
17.06 29.05
18.89 30.19
21.47 31.57
14.95 25.84
24.36 29.65
25.68 36.30
14.82 21.97
12.46 22.65
16.37 21.15
21.01 30.69
18.61 25.82
21.59 31.95
21.04 23.59
21.15 30.05
13.25 26.47
12.92 23.51
17.76 25.20
16.24 29.74
17.39 28.49
17.55 25.41
17.94 25.78
22.74 32.39
16.80 26.44
26.77 31.28
13.83 20.11
17.30 25.23
17.94 24.15
19.51 29.63
24.95 35.08
25.99 31.96
27.69 35.37
21.91 30.46
23.28 31.73
14.24 21.61
8.05 19.14
25.20 28.55
16.20 29.84
20.98 25.29
23.55 30.96
21.12 28.87
20.49 25.87
20.36 32.00
18.77 31.22
18.12 24.53
24.00 28.34
23.41 29.13
21.68 28.44
18.44 28.07
26.65 30.23
19.48 26.73
22.61 25.83
17.29 22.75
18.38 30.61
17.36 23.83
Using SPSS
a. [ 10 pts ] Create a scatter plot of budget vs sales.
b. [ 10 pts ] Calculate the correlation coefficient of budget and sales.
c. [ 15 pts ] Based on the results and the plot, is this data correlated? How do you know? Note that you do not need to interpret the p-value.
In: Math
Two teaching methods and their effects on science test scores are being reviewed. A random sample of 19 students, taught in traditional lab sessions, had a mean test score of 77 with a standard deviation of 3.6 . A random sample of 12 students, taught using interactive simulation software, had a mean test score of 86.7 with a standard deviation of 6.5 . Do these results support the claim that the mean science test score is lower for students taught in traditional lab sessions than it is for students taught using interactive simulation software? Let μ1 be the mean test score for the students taught in traditional lab sessions and μ2 be the mean test score for students taught using interactive simulation software. Use a significance level of α=0.05 for the test. Assume that the population variances are equal and that the two populations are normally distributed.
In: Math
In: Math
Dr. Krauze wants to see how cell phone use impacts reaction
time. To test this, Dr. Krauze conducted a study where participants
are randomly assigned to one of two conditions while driving: a
cell phone or no cell phone. Participants were then instructed to
complete a driving simulator course where reaction times (in
milliseconds) were recorded by how quickly they hit the breaks in
response to a dog running in the middle of the road during the
course. Below are the data. What can Dr. Krauze conclude with an α
= 0.01?
cell phone |
no cell phone |
|---|---|
| 235 250 239 243 232 |
232 238 227 228 227 |
If appropriate, compute the CI. If not appropriate, input "na"
for both spaces below.
[ , ]
e) Compute the corresponding effect size(s) and
indicate magnitude(s).
If not appropriate, input and/or select "na" below.
d = ; ---Select--- na trivial effect
small effect medium effect large effect
r2 = ; ---Select--- na
trivial effect small effect medium effect large effect
f) Make an interpretation based on the
results.
Cell phone use results in significantly slower reaction time than no cell phone use.Cell phone use results in significantly faster reaction time than no cell phone use. There is no significant reaction time difference between cell phone use or no cell phone use.
In: Math
Question: A survey was conducted to attempt to determine how many hours the typical worker works during one year in the US. A survey of 33 workers found the mean number of hours worked in a year to be 1784 hours with a standard deviation of 65 hours.
a. Predict the actual mean number of hours worked by a worker in the US. State your answer using appropiate statistical terminology.
b. Explain what this answer means to someone who has never taken statistics, that is avoid statistical jargon and use common language. Use complete sentences.
*note: please give a step by step explanation for explaining what you did and why
In: Math
Imagine an automobile company looking for additives that might increase gas mileage. As a pilot study, they send 30 cars fueled with a new additive on a road trip from Boston to Los Angeles. Without the additive, those cars are known to average 25.0mpg with a standard deviation of 2.4 mpg. Suppose it turns out that the thirty cars averaged 26.3 mpg with the additive. What should the company conclude? Is the additive effective? Let α=0.01.
a)Use three methods: the p-value, the critical value approach and the confidence interval method.
b) Describe what a type I error would be. Describe what a type II error would be.
In: Math
I NEED ANSWER OF A, B, C, D
You are probably familiar with (and may have used)
back belts, which are widely used by workers to protect their lower
backs from injuries caused by lifting. A study was conducted to
determine the usefulness of this protective gear. Here is a partial
description of the study, published in the Journal of the
American Medical Association and reported by the
Associated Press (December 5, 2000):
New research suggests that back belts, which are
widely used in industry to prevent lifting injuries, do not work.
The findings by the National Institute for Occupational Safety and
Health stem from a study of 160 Wal-Mart stores in 30 states.
Researchers [based their findings on] workers’ compensation data
from 1996 to 1998.
Although you do not know the
study’s particulars, think about how you would go about
investigating the effect of back belt usage on back injuries.
Assume that you have data on each of the 160 retail stores in your
study. For each store, you know whether back belt usage was low,
moderate, or high. You classify 50 stores as having low belt usage
by employees, 50 stores as having moderate usage, and 60 stores as
having high usage. You also know the number of back-injury workers’
compensation claims from each store. This information permits you
to calculate the mean number of claims for low-usage,
moderate-usage, and high-usage stores.
A. The following hypothesis suggests
that back belt usage helps prevent injury: In a comparison of
stores, stores with low back belt usage by employees will have more
worker injuries than will stores with high back belt usage. What is
the independent variable? What is the dependent variable? Does this
hypothesis suggest a positive or negative relationship between the
independent and dependent variables? Explain.
B. Fabricate a mean comparison table
showing a linear pattern that is consistent with the hypothesis.
Sketch a line chart from the data you have fabricated. (Because you
do not have sufficient information to fabricate a plausible mean
for all the cases, you do not need to include a “Total” row in your
mean comparison table.)
C. Use your imagination. Suppose the
data showed little difference in the worker injury claims for
low-usage and moderate-usage stores, but a large effect in the
hypothesized direction for high-usage stores. What would this
relationship look like? Sketch a line chart for this
relationship.
There us no data, you have to hypothesis
it.
In: Math
11) You are testing the claim that the proportion of men who own
cats is significantly different than the proportion of women who
own cats.
You sample 180 men, and 30% own cats.
You sample 100 women, and 70% own cats.
Find the test statistic, rounded to two decimal places.
12) You are testing the claim that the mean GPA of night
students is different than the mean GPA of day students.
You sample 60 night students, and the sample mean GPA is 2.01 with
a standard deviation of 0.53
You sample 30 day students, and the sample mean GPA is 1.75 with a
standard deviation of 0.74
Calculate the test statistic, rounded to 2 decimal places
20) Give a 98% confidence interval, for μ1-μ2 given the following information.
n1=35, ¯x1=2.69, s1=0.47
n2=25, ¯x¯2=2.42, s2=0.99
___ < μ1-μ2 < ___ Use Technology Rounded to 2 decimal places.
In: Math
An environmentalist wants to find out the fraction of oil tankers that have spills each month.
Step 2 of 2:
Suppose a sample of 356 tankers is drawn. Of these ships, 246 did not have spills. Using the data, construct the 90% confidence interval for the population proportion of oil tankers that have spills each month. Round your answers to three decimal places.
In: Math
The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question.
Such measures may be used in statistical hypothesis testing, for example, to test for normality of residuals, to test whether two samples are drawn from identical distributions, or rather outcome frequencies follow a specified distribution (Pearson's chi-squared test).
In the analysis of variance one of the components in
to which the variance is partitioned may be a lack of fit sum of
squares. In other words, it tells you if your sample data
represents the data you would expect to find in the actual
population.
Please in a minimum of 200 words:
What good is this information to us? Why would we need to know something like this?
In: Math