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
A company that makes car accessories. The company control its production process by periodically taking a sample of 99 units from the production line. Each product is inspected for defective features. Control limits are developed using three standard deviations from the mean as the limit. During the last 12 samples taken, the proportion of defective items per sample was recorded as follows:
|
0.01 |
0.03 |
0.0 |
0.04 |
0.01 |
0.01 |
|
0.00 |
0.01 |
0.02 |
0.02 |
0.03 |
0.03 |
a. Determine the mean proportion defective, the UCL, and the LCL? (Marks 1) (word count maximum:150)
b. Draw a control chart and plot each of the sample measurements on it? (Marks 1) (word count maximum:100)
c. Does it appear that the process for making tees is in statistical control? (Marks 0.5) (word count maximum:100)
In: Math
In a study of high-achieving high school graduates, the authors
of a report surveyed 834 high school graduates who were considered
"academic superstars" and 436 graduates who were considered "solid
performers." One question on the survey asked the distance from
their home to the college they attended.
Assuming it is reasonable to regard these two samples as random
samples of academic superstars and solid performers nationwide, use
the accompanying data to determine if it is reasonable to conclude
that the distribution of responses over the distance from home
categories is not the same for academic superstars and solid
performers. Use
α = 0.05.
| Distance of College from Home (in miles) | |||||
|---|---|---|---|---|---|
| Student Group | Less than 40 |
40 to 99 |
100 to 199 |
200 to 399 |
400 or More |
| Academic Superstars | 158 | 157 | 143 | 150 | 226 |
| Solid Performers | 105 | 94 | 83 | 65 | 89 |
State the null and alternative hypotheses.
H0: Student group and distance of college
from home are independent.
Ha: Student group and distance of
college from home are not independent. H0:
Student group and distance of college from home are not
independent.
Ha: Student group and distance of
college from home are independent.
H0: The proportions falling into the distance
categories are not all the same for the two student groups.
Ha: The proportions falling into the
distance categories are the same for the two student groups.
H0: The proportions falling into the distance
categories are the same for the two student groups.
Ha: The proportions falling into the
distance categories are not all the same for the two student
groups.
Calculate the test statistic. (Round your answer to two decimal
places.)
χ2 =
What is the P-value for the test? (Round your answer to
four decimal places.)
P-value =
What can you conclude?
Do not reject H0. There is not enough evidence to conclude that the proportions falling into the distance categories are not all the same for the two student groups. Reject H0. There is convincing evidence to conclude that the proportions falling into the distance categories are not all the same for the two student groups. Reject H0. There is convincing evidence to conclude that there is an association between student group and distance of college from home. Do not reject H0. There is not enough evidence to conclude that there is an association between student group and distance of college from home.
In: Math
Following are age and price data for 8 randomly selected ambulances between 1 and 6 years old. Here, x denotes age, in years, and y denotes price, in hundreds of dollars. Use the information to do parts (a) through (d).
x 6 1 6 2 6 2 4 5
y 280 420 275 360 265 350 325 305
Summation from nothing to nothing x equals 32 ∑x=32, Summation from nothing to nothing y equals 2580 ∑y=2580, Summation from nothing to nothing xy equals 9585 ∑xy=9585, Summation from nothing to nothing x squared equals 158 ∑x2=158
a. Compute SST, SSR, and SSE, using the formulas,
SST = ________ (Round to two decimal places as needed.)
b. compute the coefficient of determination, r2.
c. Determine the percentage of variation in the observed values of the response variable explained by the regression, and intrepret you answer.
d.State how useful the regression equation appears to be making predictions
In: Math
The average commute time in Oregon is 24 minutes, with a standard deviation of 4 minutes. For the 3 drivers in my household, what is the probability that our average commute time is over 27 minutes per day?
In: Math