This presentation provides help on numbers 13, 15 and 19 on the Week 7 Homework. This contains hypothesis testing examples for 1 Sample z, 1 Sample t and 1 proportion.
This PPT deals with the problems and solutions for sampling of large variables and relate, compare the observations with the exception of the population sample ie testing the difference between means of two samples, standard error of the difference between two standard deviations.
This presentation describes choosing the right options in Minitab for distributions related to the "tail" of the distribution. I cover Binomial, Poisson and the Geometric Distributions.
Statistics for UX Professionals - Jessica CameronUser Vision
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
I decided to investigate the correlation between the number of hours spent in front of a screen for entertainment and the corresponding average letter grade. I was curious to find out the extent at which entertainment affects a student letter grade. I did this posing these questions to the respondent:
What is your average letter grade in school?
Predicting Helpfulness of User-Generated Product Reviews Through Analytical M...Ankita Kaul
Customer reviews are an important feature on Amazon’s vast array of products. Many customers rely heavily on the honest reviews of past users during purchasing decisions. Currently, the only way to regulate the quality of these reviews is for other users to voluntarily thumbs up/down a review as ‘helpful’ or ‘not helpful’. It is in the best interest of Amazon (and potential customers) to be shown the most helpful reviews first and de-prioritize (or flag) useless reviews. Thus, we wanted to try and create a model that could successfully predict whether or not customers would find user product reviews helpful. With such a model, Amazon would be able to better prioritize user reviews displayed on product pages from the moment a review is posted.
This presentation provides help on numbers 13, 15 and 19 on the Week 7 Homework. This contains hypothesis testing examples for 1 Sample z, 1 Sample t and 1 proportion.
This PPT deals with the problems and solutions for sampling of large variables and relate, compare the observations with the exception of the population sample ie testing the difference between means of two samples, standard error of the difference between two standard deviations.
This presentation describes choosing the right options in Minitab for distributions related to the "tail" of the distribution. I cover Binomial, Poisson and the Geometric Distributions.
Statistics for UX Professionals - Jessica CameronUser Vision
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
Are you looking to expand your research toolkit to include some quantitative methods, such as survey research or A/B testing? Have you been asked to collect some usability metrics, but aren’t sure how best to go about that? Or do you just want to be more aware of all of the UX research possibilities? If your answer to any of those questions is yes, then this session is for you.
You may know that without statistics, you won’t know if A is really better than B, if users are truly more satisfied with your new site than with your old one, or which changes to your site have actually impacted conversion rates. However, statistics can also help you figure out how to report satisfaction and other metrics you collect during usability tests. And they’re essential for making sense of the results of quantitative usability tests.
This session will focus on the statistical concepts that are most useful for UX researchers. It won’t make you a quant, but it will give you a good grounding in quantitative methods and reporting. (For example, you will learn what a margin of error is, how to report quantitative data collected during a usability test - and how not to - and how many people you really need to fill out a survey.)
I decided to investigate the correlation between the number of hours spent in front of a screen for entertainment and the corresponding average letter grade. I was curious to find out the extent at which entertainment affects a student letter grade. I did this posing these questions to the respondent:
What is your average letter grade in school?
Predicting Helpfulness of User-Generated Product Reviews Through Analytical M...Ankita Kaul
Customer reviews are an important feature on Amazon’s vast array of products. Many customers rely heavily on the honest reviews of past users during purchasing decisions. Currently, the only way to regulate the quality of these reviews is for other users to voluntarily thumbs up/down a review as ‘helpful’ or ‘not helpful’. It is in the best interest of Amazon (and potential customers) to be shown the most helpful reviews first and de-prioritize (or flag) useless reviews. Thus, we wanted to try and create a model that could successfully predict whether or not customers would find user product reviews helpful. With such a model, Amazon would be able to better prioritize user reviews displayed on product pages from the moment a review is posted.
Approximate Continuous Query Answering Over Streams and Dynamic Linked Data SetsSoheila Dehghanzadeh
To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static (background) data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive.
In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application.
We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query (e.g., the sliding window definition and the current window content) to maintain the local view based on user-defined Quality of Service constraints.
Experimental evaluation show the effectiveness of the approach.
Help on funky proportion confidence interval questionsBrent Heard
This presentation provides an alternate way of getting confidence intervals for proportions. We have at least one problem in Week 6 where this applies. Rather than using Minitab, I have an Excel template that will help. Instructions on obtaining the file are at the end of the presentation.
3. Week 6 Homework Examples
• This one is just a confidence interval problem.
• Example
– In a survey of 6000 men, 3695 say they wore a hat
at least once a week. Construct a 95% confidence
interval for the population proportion of men who
wore a hat at least once a week.
4. Week 6 Homework Examples
• Minitab >> Stat >> Basic Statistics >> 1 Proportion,
input number of events & number of trials
Check this box
5. Week 6 Homework Examples
• Answer in session window, rounded to three
decimal places, it would be (0.603,0.628)
7. Week 6 Homework Examples
• This one is a sample size problem.
• Example
– A researcher wishes to estimate with 99% confidence,
the proportion of adults who have high speed Internet
access. The researcher’s estimate must be accurate
within 5% of the true proportion.
– a) Find the minimum sample size needed using a prior
study that found 65% of the respondents said they
have high speed Internet access.
– b) Find the minimum sample size needed assuming
that no preliminary estimate is available.
8. Week 6 Homework Examples
• Just use the equation
Commonly Used Zc Values
Confidence
z
90%
1.645
95%
1.96
99%
2.575
9. Week 6 Homework Examples
• a) Find the minimum sample size needed using a
prior study that found 65% of the respondents said
they have high speed Internet access.
n = (0.65)(1-0.65)(2.575/0.05)^2
n = (0.65)(0.35)(2.575/0.05)^2
n = 603.3869
So the sample size would be
Confidence
604 (ALWAYS ROUND SAMPLE
90%
SIZES UP)
95%
99%
z
1.645
1.96
2.575
10. Week 6 Homework Examples
• b) Find the minimum sample size needed
assuming that no preliminary estimate is
available. (USE 0.5 WHEN NO ESTIMATE IS
AVAILABLE)
n = (0.5)(1-0.5)(2.575/0.05)^2
n = (0.5)(0.5)(2.575/0.05)^2
n = 663.0625
Confidence
So the sample size would be
664 (ALWAYS ROUND SAMPLE
90%
SIZES UP)
95%
99%
z
1.645
1.96
2.575
12. Week 6 Homework Examples
• This one is a confidence interval for a proportion.
• Example
– The table shows the results of a survey in which
separate samples of 500 adults each from the East,
South, Midwest and West were asked if traffic
congestion is a serious problem in their community.
Complete parts a and b.
• a) Construct a 95% confidence interval for the proportion of
adults from the Midwest who say traffic congestion is a
problem.
• b) Construct a 95% confidence interval for the proportion of
adults from the West who say traffic congestion is a
problem.
13. Week 6 Homework Examples
In my example there were 500
samples from each geographic
area. So that would mean 175
out of 500 in the East, 165 out
of 500 in the South, 125 out of
500 in the Midwest and 265
out of 500 in the West.
(Remember they were
separate samples)
I get these numbers just by
multiplying, for example for
the East, 0.35*500 = 175
14. Week 6 Homework Examples
– a) Construct a 95% confidence interval for the
proportion of adults from the Midwest who say
traffic congestion is a problem.
– Go to Minitab >> Stat >> Basic Statistics >> 1
Proportion
15. Week 6 Homework Examples
125 out of 500 is
25% for the
Midwest
Very
Important
that you
check this
box
16. Week 6 Homework Examples
• Answer
Rounded to three decimal
places, the answer would
be (0.212, 0.288)
17. Week 6 Homework Examples
– b) Construct a 95% confidence interval for the
proportion of adults from the West who say traffic
congestion is a problem.
– Go to Minitab >> Stat >> Basic Statistics >> 1
Proportion
18. Week 6 Homework Examples
265 out of 500 is
53% for the
West
Very
Important
that you
check this
box
19. Week 6 Homework Examples
• Answer
Rounded to three decimal
places, the answer would
be (0.486,0.574)